Modeling Land Use Change in Chittenden County, VT Using UrbanSim Austin R. Troy, PhD [email protected] Brian Voigt, Research Assistant [email protected].

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).