HELIOS: Household Employment and Land Impact Outcomes Simulator

FLORIDA STATEWIDE IMPLEMENTATIONDevelopment & Application

Stephen Lawe RSG

Michael Doherty URS

MAY 2013

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

2005 2006 2007 2008 2009

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.

Implemented for Florida’s Turnpike at the Statewide Level.

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.Allocate remaining:A. Determine land

availability including converting some agriculture land

B. Apply a probability model to distribute remaining growth to vacant lands and underutilized developed areas

BEBR Forecast (Control Total)

DRIs in County

Allocate remaining

GIS Processvacant residential, non-

residential, and converted agricultural land

1

Probability Model Appliedwith iterative scaling

3

2

A B

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

1,132

849

0

200

400

600

800

1000

1200

1985 1990 1995 2000 2005

Vacant

Build Year

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: Pi = Probability of Development

Pi = Pr(Yi = 1 | Xi) =ἀ + β1x1 + β2x2 +…+ βkxk e

ἀ + β1x1 + β2x2 +…+ βkxk 1 + e

Y(g) = β1x1 + β2x2 +…+ βkxk

Where (g) is a log link function

2. Linear Model Estimates: Y = Intensity of Development

Resulting outcomes scaled to control totals

Model Structure – Parameters of Model

Variable Source Effect on Development

Accessibility Travel Model Positive

Distance from Coast GIS Negative

Distance from Arterials & Interchanges GIS/Travel Model Negative

Density of Current Use Parcel Non-Linear, generally positive

Undeveloped Area Parcel Non-Linear, generally positive

Mix of Land-Uses Parcel Homogeneity is positive

Urban Growth Boundary GIS layer Less development outside UGB

Res. & Non-Res. Development History Parcel (dependent variable)

Model Evaluation in Lee-Collier

Planning Region

Model Structure – Calibration Results (Residential Growth 1980 - 2005)

Pearson’s Correlation

TAZ= .58

ZIP = .81

Observed Growth Modeled Growth

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

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

Agricultural Land

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 2012New Growth assumptionsRevised Occupancy assumptionsRevised Agricultural land conversion assumption

Policy ShiftsShifting DRI Designation

Model Interface

Windows Executable

Text file inputs and outputs

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

Questions