Brian Voigt, Austin Troy, Brian Miles, Alexandra Reiss University of Vermont – Spatial Analysis Lab

 What will land use patterns in Chittenden County look like in years?  What effect will future urban development patterns have on environmental quality?  How might alternative policies alter these outcomes?  How can we develop a model framework that effectively integrates the (inter)actions of households, employers, developers, transportation, and the environment? 2  Do indicators of predicted land use change differ depending on whether accessibilities are updated to reflect changing land use?

Integrated Model Framework

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YEAR 1930 Min = 0.79 per / mi 2 Max = 3712 per / mi 2 YEAR 1940 Min = 0.79 per / mi 2 Max = 4221 per / mi 2 YEAR 1950 Min = 0.59 per / mi 2 Max = 4709 per / mi 2 YEAR 1960 Min = 0.00 per / mi 2 Max = 5189 per / mi 2 YEAR 1970 Min = 1.98 per / mi 2 Max = 5111 per / mi 2 YEAR 1980 Min = 1.78 per / mi 2 Max = 4418 per / mi 2 YEAR 1990 Min = 0.40 per / mi 2 Max = 4650 per / mi 2 YEAR 2000 Min = 2.38 per / mi 2 Max = 4588 per / mi 2 6

 Model parameters based on statistical analysis of historical data  Integrates market behavior, land policies, infrastructure choices  Simulates household, employment and real estate development decisions ◦ agent-based for household and employment location decisions ◦ grid-based for real estate development decisions from Waddell, et al,

 Data-intensive  Disaggregated  Dynamic  Disequilibrium  Driven by trends and forecasts Model Coordinator Database Scenario Data Control Totals TDM Exogenous Data Output / Indicators 8

Grid_ID: Employment_ID: 427 Sector: 2 Employees: 135 Grid_ID:23674 HSHLD_ID: 23 AGE_OF_HEAD: 42 INCOME: $65,000 Workers: 1 KIDS: 3 CARS: 4 Grid_ID:23674 Households: 9 Non-residential_sq_ft: 30,000 Land_value: 425,000 Year_built: 1953 Plan_type: 4 %_water: 14 %_wetland: 4 %_road: 3 9

Land Price Real Estate Development Residential Land Share Accessibility Mobility & Transition Location Choice mover vacant units probabilities site selection

New land development events in response to insufficient supply Land Price Residential Land Share Accessibility Mobility & Transition Location Choice Real Estate Development

Coefficient NameDefinitionEstimatet_statistic AVE_INCAverage income in the gridcell1.19E BUILD_AGEAverage age of improvements COST_INC_RAT Average cost of improvement to average income ratio DEV_TYPE_M1Is zoned mixed use development IS_NEAR_ART_300Is within 300m of arterial street IS_NEAR_HIGHWAYIs within 1500m of the interstate LN_COMSF_WWD LN of commercial square feet w/in walking distance LN_HOME_ACC_POPLN home access to population by auto LN_HOUSEHOLDSLN number of households in grid cell LN_RVAL_PER_RUNIT LN average value of res land per res unit w/in walking distance %_LOW_INC_WWD_ IF_HIGH_INC % low income households w/in walking distance if high income %_LOW_INC_WWD_ IF_LOW_INC % low income households w/in walking distance if low income VAC_RES_UNITS# of vacant residential units

 TransCad 4-step model  Developed by RSG, Inc for the CCMPO  Run on 5-year interval  TDM accounts for changes in land use patterns  Calculates accessibility measures and passes results to UrbanSim model 13

14 TDM No TDM

15 TDM No TDM

16 TDM No TDM

17  No TDM – with TDM  No clear spatial pattern in the differences

18  No TDM – with TDM  No TDM clusters new residential development in the western portion of the County  With TDM clusters new residential development in the eastern portion of the County

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 Residential Units by TAZ H o : sd(with TDM / without TDM) = 1 H a : sd(with TDM / without TDM) ≠ 1 f = Pr(F > f) =  Commercial Feet 2 by TAZ H o : sd(with TDM / without TDM) = 1 H a : sd(with TDM / without TDM) ≠ 1 f = Pr(F > f) =

 No TDM vs RPC housing data H o : sd(no TDM / RPC) = 1 H a : sd(no TDM / RPC) ≠ 1 f = Pr(F > f) =  With - TDM vs RPC housing data H o : sd(with TDM / RPC) = 1 H a : sd(with TDM / RPC) ≠ 1 f = Pr(F > f) =

 Current implementation of model yields mixed results # of development projects Zoning  Continue to explore alternative model specifications  Integration with disaggregate travel model 24

 This work was funded by grants from the US DOT – FHWA and the University of Vermont Transportation Research Center  UVM UrbanSim team: Brian Miles, Alexandra Reiss  Special thanks: Chittenden County MPO & RPC, Dr Adel Sadek and Shan Huang, Resource Systems Group, Inc – Stephen Lawe, John Lobb, and John Broussard  For more information 25

Questions??? University of Vermont Spatial Analysis Lab 26

Data CategoryData Set NameData Source EconomicLand and improvement valueGrand List from individual town assessor’s office Year built for all structures in the countyIndividual town clerk’s office Employment (size, sector, location)VT Secretary of State and Claritas 1 Residential UnitsCCRPC 2 BiophysicalTopography, soils, wetlands, waterVermont Center for Geographic Information Land CoverUniversity of Vermont – Spatial Analysis Lab InfrastructureRoadsGDT 1 TransitChittenden County Transit Authority Planning & ZoningZoningIndividual town plans Conserved landUVM – Spatial Analysis Lab DemographicsHousehold characteristicsUS Census: SF1, SF3, 5% PUMS ForecastCCRPC 2 / CCMPO 3 1: proprietary data sets 2: Chittenden County Regional Planning Commission (CCRPC) 3: Chittenden Country Metropolitan Planning Organization (CCMPO) 27

Coefficient NameDefinitionEstimatet_statisticSE Constant DIST_ART Distance to nearest arterial street ELEVElevation E-05 IND_WIWLK % industrial w/in walking distance E E-08 IN_SEWERIs within sewer district IS_CONSLIs conserved land LN_HOUSEHOLDSLN grid cell # of households TT_CBDTravel time to CBD YRBLTYear built E E-06 28

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