modeling land use change in chittenden county, vt using urbansim austin r. troy, phd...
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Modeling Land Use Change in Chittenden County, VT Using UrbanSim
Austin R. Troy, [email protected]
Brian Voigt, Research [email protected]
Project: “Dynamic land use and transportation modeling”
• Purpose: to simulate future land use, transportation and environmental impact in Chittenden County under baseline and alternative scenarios
• US DOT FHWA funded; 2006-2008
• Collaborators: Resource Systems Group (RSG, Inc), CCRPC, CCMPO, UVM
• Tools: UrbanSim, TransCAD
Image source: Above and Beyond by Alex MacLean, Julie Campoli and Beth Humstone
Research Questions• What will land use
patterns in Chittenden County look like in 25 years?
• What effect(s) will future development patterns have on the environment?
• How might policy and investment strategies influence these outcomes?
Image source: Microsoft Virtual Earth
Modeling with UrbanSim• University of Washington
– www.urbansim.org
• Model parameters based on trend analysis• Integrates market behavior, land policies,
infrastructure choices• Simulates evolution of households, jobs
and real estate development– agent-based for household and employment
location decisions– grid-based for real estate development
decisions from Waddell, et al, 2003
The Four D’s of UrbanSim
• Dynamic
• Disequilibrium
• Different time scales
• Disaggregated
UrbanSim Decision Makers
Grid_ID: 60211
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
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Household and Employer Activity
• Occupancy / Vacancy• Transition• Mobility• Location
– options– decisions
• Development is based on supply of and demand for additional units / area
0 20 40 60 80
Construction
Transpo & Util
Financial
Ed and Hlth Serv
Trade
Government
Manufacturing
Services
# of Jobs (1000s)
1970 1990 2004
data store
modeloutput
output visualization
submodels
modified from Waddell et al., 2001
export model
control totals
TDM outputs
macro-economic
model
travel demand model
user specified events
scenario assumptions
model coordinator
UrbanSim Model Architecture
UrbanSim Model Architecture• Suite of sub-models
– land price– accessibility– transition– mobility– location choice– Development
• User specifications– model interval: one-year
time step– sub-model order and
frequency– schedule of TDM runs
from Waddell, et al, 2003
Exogenous Inputs: Control Totals
• Externally derived inputs
• Model does not predict demographic / ecnomic changes
• Spatially allocates changes to population / employment
• Many estimates; ultimate source to be determined
Households by Grid Cell: 2000
Model Output
• Output database: defines grid cell state
• Graphics– maps– charts– tables
Households by Grid Cell: 1990
Services2866
Retail Trade1537
Finance432
Construction422
Wholesale Trade355
Top 5 Employment Sectors: 1990GRID_ID SECTOR_ID EMPLOYEES
77061 8 13
76187 11 256
75916 9 13
75938 13 18
75434 9 3
69171 8 1
68900 9 22
…
68902 8 3
Indicators• Predefined indicators
– transport: VMT– land use: vacancy, non-residential sq ft– land value– households: income– population: density
• Environmental– watershed function– habitat fragmentation
Image source: Microsoft Virtual Earth
Using a Simulation Model for Comparative Scenario Analysis
• What is a scenario?–Alteration of model inputs/
assumptions from baseline
• Types of changes that can be assessed–Zoning–Transportation investments–Non-transportation capital
investments–State and regional policy–Economic and demographic
changes
base year
policy event 1
employment event
New major employer
New highway
infrastructure
development event
Rezone growth
center(s)
Fuel taxpolicy event 2
Potential Zoning ScenariosModeling the effects of • upzoning, • downzoning, • reconfiguring zone
boundaries, • new zoning categories, • density regulations or use
changes for specific districts
Should have specific zoning changes in mind first
Potential Transportation Investment Senarios
Modeling the effects of hypothetical transportation investments like:
• new roads / highways• new interchanges, exits• road widening• bus line expansion• carpooling programs
Potnential Non-Transportation Capital Investment Scenarios
• All capital investments not included under the transportation scenarios like– Utilities: water, sewer,
power, telecomm– Schools– Public facilities (libraries,
post offices, courthouses)
– Parks/Open Space– Joint public/private
developments– Major public institutions
Image source: Microsoft Virtual Earth
Potential State and Regional Policy Scenarios
Hypothetical state and county level policies, or changes to existing policies, that are expected to affect land use or transportation like:
• Tax policies– property tax, current use, gas
tax, speculation tax, etc.• State land use policies
– growth centers, Act 250, urban service boundary, changes to current use development penalties, etc.
• Transportation policies – tolls, congestion pricing, gas
tax, etc.• Environmental conservation policies
– farmland, wetlands and shoreline protection, etc
• Air quality attainment standards
Economic and Demographic Change Scenarios
Economic and demographic changes to the county to be prepared for:
Economic Examples: • loss or gain of a major employer,
increases or decreases in business taxes, telecommuting, energy price spikes or shortages, new federal fuel economy or tailpipe emissions requirements, changes in prices of raw materials, changes to the economy due to global warming
Demographic Examples:• regional baby boom, influx of
residents from other states due to global warming, changes in household characteristics Image source: Microsoft Virtual Earth
Methods for implementing scenarios with difficulty level
1. Changes to control totals
2. Changes to base year dbase tables
3. Change to spatial inputs (GIS editing)
4. Adding/changing variables to UrbanSim
5. Adding/changing variables to TransCAD
6. Combination of above
7. Programming new behaviors
? = increased level of uncertainty due to lack of prior trends or data to analyze or lack of knowledge of behavioral responses
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Examples
• Zoning: density or use changes• Transportation: digitizing new
interchanges/exits• Policy: Growth Centers Legislation
(if boundaries available)
• Employment: loss or gain of a major employer
• Non-transportation: joint public/private developments
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? = increased level of uncertainty due to lack of prior trends or data to analyze or lack of knowledge of behavioral responses
Project Status
• More Information: www.uvm.edu/envnr/countymodel
or Austin Troy: [email protected]
• Thanks to: US DOT (current funder), RSG, US EPA (previous funder), CCRPC, CCMPO and Research Assistants (Brian Miles, Alexandra Reiss, Galen Wilkerson).