Transcript Introduction to GIS

Introduction to Geographic
Information Systems
Miles Logsdon
[email protected]
http://sal.ocean.washington.edu/
Spatial Information
Technologies
 Geographic Information Systems – GIS
 Global Positioning System – GPS
 Remote Sensing and Image Processing - RS
Technologies to help answer:
 What is “here”? … give a position
 What is “next” to “this”? … given some description
 Where are all of the “???” … detecting or finding
 What is the spatial pattern of “???”
 When “X” occurs here, does “Y” also occur?
GIS
Geographic Information System
GIS - A system of hardware, software, data, people,
organizations and institutional arrangements for collecting,
storing, analyzing, and disseminating information about areas
of the earth. (Dueker and Kjerne 1989, pp. 7-8)
GIS - The organized activity by which people
•Measure aspects of geographic phenomena and processes;
•Represent these measurements, usually in a computer database;
•Operate upon these representations; and
•Transform these representations. (Adapted from Chrisman, 1997)
A KEY POINT: Geo-referenced Data
GIS - consists of:
Components
People, organizational setting
Procedures, rules, quality control
Tools, hardware & software
Data, information
Functions
Data gathering
Data distribution
Common “short hands”
 CAM- Computer Aided
Mapping
 AM - Automated mapping
 CAD - Computer-Aided Design
 LIS - Land Information
Systems
 AM/FM - Automated
Mapping/Facilities
Management Systems
 RS - Remote Sensing
aerial Photography
Photogrammetry
Photo interpretation
Thermal sensing
Radar imaging
Satellite Remote Sensing
Meteorological
Terrestrial
Image Processing
Geographic Data
Spatial Data
location
shape
relationship among
features
Descriptive Data
attributes, or
characteristics of the
features
After Sinton, 1978:
Components of spatial information: time, space, theme (attribute)
Sounds obvious. useful starting point to remember
Role of these Dimensions: One must be fixed, one controlled, one
measured.
Components of Spatial Data
Temporal examples:
Control:
Measure:
Time (hour) Attribute (water level) = strip chart (stream guage)
The Basic Spatial Data Structures
Control:
Measure:
Location
Attribute => Raster (Location controlled by grid)
Attribute
Location
=> Categorical coverage (Vector)
Indirect measurement
Control: Measure:
First: Attribute Location => Categorical Coverage (eg. land use category)
Second: Category Attribute => Estimate for category (eg. % Corn yeild)
Composite Measurement
Control: Measure:
First: Attribute Location => Collection Zones (eg. counties)
Second: Location Attribute => Choropleth (eg. % vote for Initiative 187)
DATA - “more than one”
DATUM - “only one item, or record”
Three Attributes of Data
Thematic (Value Variable)
Nominal, … name, label
Ordinal, … rank ordered
Interval / Ratio, … measurement on a scale
Spatial (location)
Temporal
Spatial Data: the spatial attribute is explicitly stated and linked
to the thematic attribute for each data item.
Spatial - thematic value
types
200’
Sta. 94, DOC 4.9
Stream,3
Former Land Fill
100’
FOREST
URBAN
Duvall, pop 1170
FOREST
100’
200’
WELL
AGRICULTURE
Snoqualmie
River, 1
Brush
Creek, 2
Geographies
Layers, Coverages, Themes
Land use
Soils
Streets
Hydrology
Parcels
Concept of Spatial Objects
 POINTS
 LINES
 AREA
Spatial Encoding - RASTER
POINT
0 0
0
0 1
0 0
0
0
AREA
LINE
1
0
0
0
0
1 0
0 1
5
5
3
1
1
3
1
3
2
Spatial Encoding - VECTOR
POINT
- x, y
* a single node
with NO area
LINE
(Arcs)
- x1, y1
- x2, y2
.
.
- xN, yN
* a connection of
nodes (vertices)
beginning with a
“to” and ending
with a “from”
Area
(Polygons)
* a series of arc(s)
- x1, y1
that close around
- x2, y2
a “label” point
.
.
- xN, yN (closure Point)
Vector - Topology
Descriptive
Spatial
Object
VAR1 VAR2
1
2
3
x1,y1
x2,y2
x3,y3
1
2
3
Fnode Tnode x1y1, x2y2
1
3
2
1
2
1
2
2
3
xxyy, xxyy
xxyy,xxyy
VAR1 VAR2
1
2
2
1
VAR1 VAR2
2
1
2
3
15
10
5
1
11
12
4
10, 11, 12, 15
10, …….
1
2
Raster Data Model
Set Selections
[ 1 2 3 4 5 6 7 8 9 10 ]
Reduce Select - RESEL GT 5 = [6 7 8 9 10]
Add Select
- ASEL EQ 5 = [5 6 7 8 9 10]
Unselect
- UNSEL GE 9 = [5 6 7 8 ]
Null Select
- NSEL = [1 2 3 4 9 10 ]
AND,
OR,
XOR
2
1
AND
3
= 2
OR
= 1,2,3
XOR
=1
Spatial Overlay
1
-
UNION
1
1
2
3
6
2
2
4
5
3
7
3
8
11
12
9
4
10
5
13
14
16
17
15
#
1
2
3
4
5
attribute
A
B
C
D
#
1
2
3
attribute
102
103
#
1
2
3
4
5
IN attribut
OUT attribute
102
A
A
B
102
102
Spatial Overlay
INTERSECT
1
-
1
1
2
2
2
3
3
4
5
3
6
4
5
8
#
1
2
3
4
5
7
attribute
A
B
C
D
#
1
2
3
attribute
102
103
#
1
2
3
4
5
9
IN attribut
A
B
A
B
OUT attribute
102
102
103
103
Spatial Overlay
IDENTITY
1
-
1
1
2
2
2
5
3
3
4
6
7
8
9
3
4
5
10
11
12
#
1
2
3
4
5
attribute
A
B
C
D
#
1
2
3
attribute
102
103
#
1
2
3
4
5
13
IN attribut
A
A
B
B
OUT attribute
102
103
Spatial Poximity - BUFFER
Spatial Poximity - NEAR
Assign a point to the
nearest arc
Spatial Proximity Pointdistance
DISTANCE
1
2
3
1
2
3
2,045
1,899
1,743
Spatial Proximity - Thiessen
Polygons
Map Algebra
In a raster GIS, cartographic modeling is also named Map
Algebra.
Mathematical combinations of raster layers
several types of functions:
•
Local functions
•
Focal functions
•
Zonal functions
•
Global functions
Functions can be applied to one or multiple layers
Local Function
Sometimes called layer functions Work on every single cell in a raster layer
•Cells are processed without reference to surrounding
cells
•Operations can be arithmetic, trigonometric, exponential,
logical or logarithmic functions
Local Functions: Example
•Multiply by constant value
2
2
0
3
1
0
1
4
1
1 2
X3 =
6
6
0
9
3 3
0 12
3
3 6
3 2
9 6
•Multiply by a grid
2
2
0
3
1
1
0
1
4
1 2
3 2
2
3
X
0
3
2
3
2
3
2
2 2
1 1
4
6
=
0
9
2 2
0 12
2
2 4
3 2
Focal Function
Focal functions process cell data depending on the values of
neighbouring cells
We define a ‘kernel’ to use as the neighbourhood
•for example, 2x2, 3x3, 4x4 cells
Types of focal functions might be:
•focal sum,
•focal mean,
•focal max,
•focal min,
•focal range
Focal Function: Examples
•Focal Sum (sum all values in a neighborhood)
2
2
0
3
1
0
1
4
2
1
1 2
2
3
3 2
(3x3)
=
16 13
17 19
•Focal Mean (moving average all values in a neighborhood)
2
2
0
3
1
0
1
4
4
2
2
3
1
1
3 2
(3x3)
=
1.8 1.3 1.5 1.5
2.2 2.0 1.8 1.8
2.2 2.0 2.2 2.3
2.0 2.2 2.3 2.5
Zonal Function
Process and analyze cells on the basis of ‘zones’
Zones define cells that share a common characteristic
Cells in the same zone don’t have to be contiguous
A typical zonal function requites two grids
•a zone grid which defines the size, shape and location of each zone
•a value grid which is processed
Typical zonal functions
•zonal mean,
•zonal max,
•zonal sum,
•zonal variety
Zonal Function
An Example
•Zonal maximum – Identify the maximum in each zone
2
2
2
3
1
3
3
1 1
1
1
1
5
2
6
3
7
4
8
2
1
2
3 4
2 2
5
6
7 8
=
5
5
8
5
7
8
7
8
8
7
5
8
5 5
Useful when we have different regions “classified” and wish to
treat all grid cells of each type as a single “zone” (ie. Forests,
urban, water, etc.)
Global function
In global functions •The output value of each cell is a function of the entire
grid
•Typical global functions are distance measures, flow
directions, or weighting measures.
•Useful when we want to work out how cells ‘relate’ to
each other
Golbal Function
An Example
•Distance Measures – Euclidean distance based upon cell size
1
2
1
1
=
2 1
1.4 1
1
0
1
0
0
0
1 1
1.4 1
1.4 2
Or – some function which must consider all cells before
determining the value of any cell – (“cost” associated with a
path across the surface)
Examples
outgrid = zonalsum(zonegrid, valuegrid)
outgrid = focalsum(ingrid1, rectangle, 3, 3)
outgrid = (ingrid1 div ingrid2) * ingrid3
Spatial Modeling
Spatial modeling is analytical procedures applied with a GIS. Spatial modeling uses geographic
data to attempt to describe, simulate or predict a real-world problem or system.
There are three categories of spatial modeling functions that can be applied to geographic
features within a GIS:
•geometric models, such as calculating the Euclidean distance between features,
•coincidence models, such as topological overlay;
•adjacency models (pathfinding, redistricting, and allocation)
All three model categories support operations on spatial data such as points, lines, polygons,
tins, and grids. Functions are organized in a sequence of steps to derive the desired information
for analysis.
The following references are excellent introductions to modeling in GIS:
Goodchild, Parks, and Stegaert. Environmental Modeling with GIS. Oxford University Press, 1993.
Tomlin, Dana C. Geographic Information Systems and Catograhic Modeling. Prentice Hall, 1990.