Stephen Lawe RSG Michael Doherty URS MAY 2013

HELIOS:

Household Employment and Land Impact Outcomes Simulator

FLORIDA STATEWIDE IMPLEMENTATION

Development & Application

Presentation Outline 1. Model Introduction

 Model Goals & Process  Model History

2. Model Development

 Data Used  Estimation and its challenges  Model structure

3.

Model Evaluation

 Southwest Example

4.

Model Use

 Model Interface  Projects where HELIOS was used

Goals during development HELIOS

     

Consistent Land Use Forecast Initially used to refine the Turnpike ME process Integrate into the statewide and MPO models Support multiple geographic levels (parcels, different TAZ boundaries, etc.)

 

Sensitive to accessibility. Able to integrate into any transportation model.

Modest Runtimes:

The model runs the entire state of Florida in less than 5 minutes for a specified time period Analysis of policy measures other than changes in accessibility run in less than 2 minutes

Model History

Implemented for Florida’s Turnpike at the Statewide Level.

2005

Model applied to

Southwest Florida Investment Grade Project

. Land use forecasts were used in Florida’s Turnpike Planning out to 2045.

Model applied to

Central Florida Investment Grade Project

. Forecasts used to 2060 Implemented for Florida DOT at the Statewide Level.

2006 2007 2008 2009

Setting the Stage Peer Reviewed



2010 The Florida MPO Land Use Model Task Force reviewed a broad range of tools and has suggested that HELIOS be made available across the state.

Application History

     

14 Managed Lane Studies Wekiva Parkway Suncoast 2 Suncoast 3 [ Tampa to Jacksonville Corridor ] I295 / I95 Managed Lanes Turnpike System Forecasts [ Traffic Trends Process ]

HELIOS Process 1.

Determine control totals to be allocated 2.

Apply Developments of Regional Impact (DRI) growth to known TAZs 3.

A.

B.

Allocate remaining:

Determine land availability including converting some agriculture land Apply a probability model to distribute remaining growth to vacant lands and underutilized developed areas 1 BEBR Forecast (Control Total) 3 Allocate remaining A GIS Process

vacant residential, non residential, and converted agricultural land

2 DRIs in County B Probability Model Applied with iterative scaling

Model Development

Data Inputs for Model



Estimation

   

Parcel-level land use Urban Planning Boundaries (urban growth constraints) Geoprocessing with GIS (proximities) Base Year Socioeconomic Data



Model Implementation

 

Generalized land use Developments of Regional Impact

DRIs

Data Cleaning – InfoUSA Employment

Land Use Data – Development History (parcel level)

Land Use Data – Lumpy by Year 1200 1000 800 600 400 200 0

Vacant

1985

Build Year

1990 1995 849 1,132 2000 2005

Land Use Data – Spatial Variability (parcel variability)

Land Use Data – Urban Growth Boundaries (legal constraints)

Model Structure – Two Stage Logit/Linear

1. Logit Model Estimates: P

i

= Probability of Development P

i

= Pr(Y

i

= 1 | X

i

) =

e

ἀ + β

1

x 1

1 + e

+ β

2

x 2 ἀ + β

1

x 1 +…+ β

k

x k + β

2

x 2 +…+ β

k

x k

2. Linear Model Estimates: Y = Intensity of Development Y(g) = β

1 x 1 + β 2 x 2 +…+ β k x k Where (g) is a log link function

Resulting outcomes scaled to control totals

Model Structure – Parameters of Model

Variable

Accessibility Distance from Coast Distance from Arterials & Interchanges Density of Current Use Undeveloped Area Mix of Land-Uses Urban Growth Boundary

Res. & Non-Res. Development History Source

Travel Model GIS GIS/Travel Model Parcel Parcel Parcel GIS layer

Parcel Effect on Development

Positive Negative Negative Non-Linear, generally positive Non-Linear, generally positive Homogeneity is positive Less development outside UGB

(dependent variable)

Model Evaluation in Lee-Collier Planning Region

Model Structure – Calibration Results (Residential Growth 1980 - 2005) Observed Growth Modeled Growth Pearson’s Correlation

TAZ= .58

ZIP = .81

Model Sensitivity Test - Accessibility



Significant bridge capacity added during 1990s



Large subsequent observed increase in development in Cape Coral



Modeled removal of new bridge capacity

  25% decrease in HH growth in Cape Coral 40% decrease in Employment growth in Cape Coral

Legend % Difference HH

-0.53 - -0.40

-0.39 - -0.30

-0.29 - -0.25

-0.24 - -0.20

-0.19 - -0.15

-0.14 - -0.10

-0.09 - -0.05

-0.04 - 0.00

0.01 - 0.11

Model Use

Model Structure – Land Conversion Algorithm

Agricultural Land 

According to US Census of Agriculture, FL farmland declined from 10.4 M acres in 2002 to 9.2 M acres in 2007



We assume this continues; each modeling period, a fraction of agricultural land becomes available for development

HELIOS - Final Observations



Inconsistent Inputs to HELIOS

  It is possible to give the model an “inconsistent set of inputs”. An example would be growth control totals that exceed the available land HELIOS warns the user and then “softens” the constraint assumptions to allow full allocation.

 This provides “what if” testing but also requires user consideration 

Recession Impacts: comparison of 2006 and 2012

   New Growth assumptions Revised Occupancy assumptions Revised Agricultural land conversion assumption 

Policy Shifts

 Shifting DRI Designation

Model Interface



Windows Executable



Text file inputs and outputs



Ability to turn on/off key features (accessibility and distance calculations)

Questions