Integrated Coastal Growth Projection Model

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Transcript Integrated Coastal Growth Projection Model

Slide 1

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 2

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 3

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 4

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 5

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 6

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 7

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 8

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 9

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 10

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 11

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 12

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 13

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 14

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 15

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 16

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 17

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 18

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 19

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 20

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 21

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 22

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 23

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 24

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 25

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 26

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 27

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 28

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 29

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 30

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 31

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 32

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 33

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 34

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 35

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!


Slide 36

Predicting Urban Growth on the Atlantic
Coast Using an Integrative Spatial
Modeling Approach
Jeffery S. Allen and Kang Shou Lu
Clemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.
County
Beaufort
Berkeley
Charleston
Colleton
Dorchester
Georgetown
Horry
Jasper
South Carolina

Population 1970
51,136
56,199
247,650
27,622
32,276
33,500
69,992
11,885
2,590,713

Population 1990
86,425
128,776
295,039
34,377
83,060
46,302
144,053
15,487
3,486,703

Population 2000
120,937
142,651
309,969
38,264
96,413
55,797
196,629
20,678
4,012,012

Population density map for North Carolina, South Carolina, and Georgia

# of People Per Square Mile*

> 800
400 - 800
200 - 400
100 - 200
0 - 100
* 1999 population estimates by CACI International, Inc. based on 1990
US Census

South Carolina: Comparison of Population Grow th to
Increase in Developed Land 1992-97

35%
30%
25%
20%
15%
10%
5%
0%

30.2%

5.3%

Developed Land
Population

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on
the Future, Clemson University.

Total Acres of Land Conversion by State, 1992-1997 (thousand acres)
Rank
1
2
3
4
5
6
7
8
9
10

STATE Acres converted to developed land (1,000 acres)
Texas
1219.5
Pennsylvania
1123.2
Georgia
1053.2
Florida
945.3
North Carolina
781.5
California
694.8
Tennessee
611.6
Michigan
550.8
South Carolina
539.7
Ohio
521.2

Source: (London and Hill, 2000) -- USDA, 1997 National Resource
Inventory Summary Report

Location of Study Area

Urban Growth Trends (Past)
1985-1997

Urban Area Grows by 67%
1985-2000

Population Grows by 20.6%
Sprawl Index
3:1
(ratio of urban area growth to population growth)

Purposes and Objectives
Gain a better understanding of urban growth process;
Develop a methodology for urban growth prediction; and
Provide better information for:
 Land use decision-making toward smart growth
 Impact assessment studies
 Public education of environmental awareness

The objectives of this project are:
 Developing an operational urban growth model
 Calibrating the model using 1990-2000 data
 Predicting urban extent by year 2030 for the
Beaufort-Colleton-Jasper Region

Urban Growth Models
 Lowry’s Model (1957) and Its Variants
 Cellular Automata (Deltron) Model
(San Francisco Bay Area)
--- Clarke (1996)

 California Urban Future Model (CUF I and II)
--- Landis (1994, 1995, and 1997)
 Land Transformation Model (LTM)
(Michigan’s Saginaw Bay Watershed)
---Pijanowski et al (1997)

Challenges Faced in Urban Land Use Modeling
Geology
Geomorphology
Hydrology
Climate
Soil
Vegetation

Physical
Systems

•Natural resources
•Activity settings
•Aesthetic sanities
•Natural functions

Land
•Availability
•Suitability
•Capacity
•Sustainability

1.
2.
3.
4.

Uses

Land Use
Systems

Human
Systems

Economic
Social
Cultural

•Functions
•Structures
•Activities
•Ownership
•Use status

Components or structures of the land use systems:simple vs. complex
Relationships between components, agents, factors, and processes:
deterministic vs. indeterministic.
Changes over space (and time): ordered vs. random vs. chaotic
Spatial distribution or patterns: regularity vs. irregularity (fractal)

Model vs. Reality

Murrells Inlet

Analysis Units
Mount Pleasant

---200x200 m2 grids (cells)
for calibrating models
---30x30 m2 grids (cells)
for prediction

Parcel
--smallest legal unit

Part of Mount Pleasant

Zone
--area demarcated by
the major roads
Grid or Cell
--square-shaped area

Predictor Variables
• Physical suitability
– Land cover, Slope, Soil suitability

• Service accessibility
– Transportation, Waterline, Sewer line, CBD, Industrial parks,
Demographic

• Initial conditions
– Existing urban, Vacant infill area, Agriculture land, Forest land

• Policy constraints
– Protected land, Comprehensive planning, Growth boundary,
Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural
sites, Land ownership

Data Sources
Land-use
(SCDNR)

Reach Files, Version 3
(USEPA)

Population
Density
(Census)

Subwatersheds
(SCDNR)

Elevation
(USGS)

Soil
Suitability
(NRCS)

Examples of Predictor Variables

Distance to 2000 Urban Area
Distance to 80 Industry Point
Distance to Roads
Distance to Highway System
Distance to Water Lines
Distance to Sewage system

Final Suitability

Census

Distance to
Existing Development
Soils

Final Suitab ility
M os t Suita b le
2
3
4
Lea st Suita ble

Final Study Area Selection

Results---Predicted Urban Growth
BCJ Region

Results---Predicted Urban Growth
Beaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses fo

r Selected

Species (1 990 -2030)

Area ( in acr e s)
Commo n Na m e
Green Tr e efrog
Re d Fox
Re d C o ckade d Woodpecke r
(Endangered)

1990
301323.5

7

Hab ita t L o ss
2030

Acres

%

243374.81

57948.7 6

19.23

68338.83

41023.59

27315.2 4

39.97

208 82.65

17984.84

2897.81

13.88

Wood Stork (Endan
g ered)
135728.0 3
128129.46
7598.57
5.60
Not e : Urban d ev e lopme n t through 2030 was pre d ic ted b ase d on the curren t growth r atio.

Simulated Growth

Urban Sprawl Problems
Urban growth is necessary and unavoidable.
But uncontrolled growth - urban sprawl
results in many problems such as:
 Increased cost of living
 Rising taxes and pressure on infrastructure and
urban services
 Traffic congestion and increased (travel) time
 Environmental pollution
 Loss of farm/forest land, habitats and rural
(natural) landscape
 Downtown declines and community segregation

Benefits of Urban Growth

 Increased standard of living
 Generation of wealth
 Increase in amenities
 Production of affordable housing
 Increase in tax base
 New business opportunities
 New job opportunities
 Increased “freedom” with the automobile
 It is what we desire - “Freedom of Choice”

Urban Growth Trends
The pattern follows paths of subsidy.
•Undervalued infrastructure
•Discounted resources
•Reductions for individual risk

•Unintended consequences of past policies

What do we do now?

 Growth is coming whether we want it or not
 Determine where we do not want to grow
 Increase communication among SPD’s, etc.
 Be inclusive in planning
 Provide incentives for growth in “growth areas”
 Provide “dis-incentives” for areas to protect
 Make users pay the freight for new growth
 It is always easier said than done!!!