Brian Voigt, Austin Troy, Brian Miles, Alexandra Reiss
University of Vermont Spatial Analysis Lab
Slide 2
What will land use patterns in Chittenden County look like in
20-30 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?
Slide 3
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
Slide 7
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, 2003 7
Slide 8
Data-intensive Disaggregated Dynamic Disequilibrium Driven by
trends and forecasts Model Coordinator Database Scenario Data
Control Totals TDM Exogenous Data Output / Indicators 8
Land Price Real Estate Development Residential Land Share
Accessibility Mobility & Transition Location Choice mover
vacant units probabilities site selection
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New land development events in response to insufficient supply
Land Price Residential Land Share Accessibility Mobility &
Transition Location Choice Real Estate Development
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Coefficient NameDefinitionEstimatet_statistic AVE_INCAverage
income in the gridcell1.19E-0517.2403 BUILD_AGEAverage age of
improvements-0.001493-3.8204 COST_INC_RAT Average cost of
improvement to average income ratio -0.345484-9.32952 DEV_TYPE_M1Is
zoned mixed use development0.2236114.69345 IS_NEAR_ART_300Is within
300m of arterial street2.72118.52261 IS_NEAR_HIGHWAYIs within 1500m
of the interstate-0.453467-2.49592 LN_COMSF_WWD LN of commercial
square feet w/in walking distance 0.03599287.33788
LN_HOME_ACC_POPLN home access to population by auto-3.88147-4.20383
LN_HOUSEHOLDSLN number of households in grid cell-0.386432-20.0571
LN_RVAL_PER_RUNIT LN average value of res land per res unit w/in
walking distance -0.348223-11.6168 %_LOW_INC_WWD_ IF_HIGH_INC % low
income households w/in walking distance if high income
-0.0451663-19.3233 %_LOW_INC_WWD_ IF_LOW_INC % low income
households w/in walking distance if low income 0.054372319.3845
VAC_RES_UNITS# of vacant residential units-0.682592-63.5107 12
Slide 13
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
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14 TDM No TDM
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15 TDM No TDM
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16 TDM No TDM
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17 No TDM with TDM No clear spatial pattern in the
differences
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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 = 0.6420 Pr(F > f) = 0.0000
Commercial Feet 2 by TAZ H o : sd(with TDM / without TDM) = 1 H a :
sd(with TDM / without TDM) 1 f =1.0452 Pr(F > f) = 0.6564
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Slide 23
No TDM vs RPC housing data H o : sd(no TDM / RPC) = 1 H a :
sd(no TDM / RPC) 1 f = 0.9203 Pr(F > f) = 0.2247 With - TDM vs
RPC housing data H o : sd(with TDM / RPC) = 1 H a : sd(with TDM /
RPC) 1 f = 0.8136 Pr(F > f) = 0.0303 23
Slide 24
Current implementation of model yields mixed results # of
development projects Zoning Continue to explore alternative model
specifications Integration with disaggregate travel model 24
Slide 25
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 www.uvm.edu/envnr/countymodel 25
Slide 26
Questions??? [email protected] University of Vermont Spatial
Analysis Lab 26
Slide 27
Data CategoryData Set NameData Source EconomicLand and
improvement valueGrand List from individual town assessors office
Year built for all structures in the countyIndividual town clerks
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)
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Coefficient NameDefinitionEstimatet_statisticSE Constant
11.16889954158.32699580.070543297 DIST_ART Distance to nearest
arterial street 0.42414900743.894798280.00966285
ELEVElevation-0.000367311-30.91169931.18826E-05 IND_WIWLK %
industrial w/in walking distance 1.04801E-078.7936697011.19177E-08
IN_SEWERIs within sewer district0.81976199257.448101040.0142696
IS_CONSLIs conserved land-0.227327004-16.222900390.0140127
LN_HOUSEHOLDSLN grid cell # of
households0.16217799520.764999390.00781016 TT_CBDTravel time to
CBD-0.0187907-29.97150040.000626952 YRBLTYear
built5.41195E-0510.172400475.32023E-06 28