HELIOS: Household Employment and Land Impact Outcomes Simulator FLORIDA STATEWIDE IMPLEMENTATION Development & 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 Model applied to Southwest Florida Investment Grade Project. Land use forecasts were used in Florida’s Turnpike Planning out to 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 Process vacant residential, non- residential, and converted agricultural land 1 Probability Model Applied with iterative scaling 3 2 AB

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 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: P i = Probability of Development P i = Pr(Y i = 1 | X i ) = ἀ + β 1 x 1 + β 2 x 2 +…+ β k x k e 1 + e Y(g) = β 1 x 1 + β 2 x 2 +…+ β k x k 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 VariableSourceEffect on Development AccessibilityTravel ModelPositive Distance from CoastGISNegative Distance from Arterials & InterchangesGIS/Travel ModelNegative Density of Current UseParcelNon-Linear, generally positive Undeveloped AreaParcelNon-Linear, generally positive Mix of Land-UsesParcelHomogeneity is positive Urban Growth BoundaryGIS layerLess development outside UGB Res. & Non-Res. Development HistoryParcel(dependent variable)

Model Evaluation in Lee-Collier Planning Region

Model Structure – Calibration Results (Residential Growth ) 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

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 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