Modeling the Dynamics of Urban Development

and the Effect of Public Policies

The Human Dimension of PRISM

Marina Alberti

Alan Borning

Paul Waddell

Outline of Talk

• Scope of the Human Dimension• The UrbanSim model system• Land cover change model• Current research agenda• Objectives for this Year

The Scope of the Human Dimension• Inputs

– Starting conditions: inventories of land use, land cover, real estate, business locations, and household locations

– Macro-economic and demographic trends– Local infrastructure investments and regulations/pricing

(transportation, water, sewer)– Land use policies (growth management, comprehensive land

use plans, environmental regulations)

• Outcomes– Spatial patterns of land use and land cover change– Spatial patterns of real estate development and prices– Spatial patterns of business and household location

UrbanSim Modeling Approach

• Model choices of agents • Discrete choice models (multinomial logit)• Microsimulate individual agents• Dynamically simulate annual time steps• Model market interactions• Use very disaggregate spatial units (150 Meter

grid cells)

A 150 Meter Grid Cell in the Queen Anne Neighborhood

Classification of Development Types

Current UrbanSim Components

Accessibility1

Economic andDemographic

Transition2

LocationChoice

4

Land Price6

Control Totals

ScenarioAssumptions

Travel ModelOutputs

Real EstateDevelopment

5

Mobility3

UserSpecifiedEvents

GISMacroeconomic

Model0

Travel DemandModel System

0

SQLDatabase

UserInputs

ModelCoordinator

ExternalModels

Factors Considered in Residential Location Model:

• Household Characteristics– Income– Age– Presence of children– Number of workers– Number of Vehicles

• Housing Characteristics– Cost – Quality – Density

• Neighborhood Characteristics– Neighborhood housing density– Neighborhood commercial and industrial space– Neighborhood retail employment– Neighborhood land values

• Regional Accessibility to Employment by Transit and Auto for– 0 car households– 1 car households– 2+ car households

All independent variables are endogenous in the model system

Factors Considered in Employment Location Model:

• Employment Characteristics– Industry Sector

• Nonresidential Space Characteristics– Cost – Type of Space– Density

• Local Characteristics– Land values– Agglomeration Economies: mix of jobs by sector– Proximity to Freeways and Arterials

• Regional Accessibility to Population

All independent variables are endogenous in the model system

Factors Considered in Real Estate Development Model

• Site characteristics– Existing development characteristics

– Land use plan

– Environmental constraints

• Urban design-scale– Proximity to highway and arterials

– Proximity to existing development

– Neighborhood land use mix and property values

– Recent development in neighborhood

• Regional– Access to population and employment

– Travel time to CBD, airport

• Vacancy rates

All independent variables are endogenous in the model system

Factors Considered in Land Price Model

• Site characteristics– Development type

– Land use plan

– Environmental constraints

• Regional accessibility– Access to population and employment

• Urban design-scale– Land use mix and density

– Proximity to highway and arterials

• Vacancy rates

All independent variables are endogenous in the model system

Assessment of Current Status

• Operational urban simulation system– Open Source software at www.urbansim.org

– Generic SQL Database for read/write

– Interoperates with GIS

– Version 2.0 now completed (complete re-engineering)

• Has been applied in Eugene-Springfield, Honolulu, Salt Lake City, Houston now starting

• Puget Sound application to be supported by Puget Sound Regional Council

Land Cover Change Model

• A new model component under development• Predicts probability of 30 meter cell changing land

cover classification during a single year• Separate model specifications for differing

conditions:– Cells affected by land use change in immediate area– Urban-rural fringe areas not immediately affected by land

use change event– Urban (built up) areas not affected by land use change– Rural (agricultural, forest) areas not affected by land use

change

Land Cover Change

The probability of transition of a pixel of initial land cover i at time t having the same land cover class at time t+1 (j=0) or changing to one of the other land cover classes (j = 1…J) can be written as a multinomial logit:

Where:

Pij is the probability of land cover at a given grid cell at time t having the same cover class at time t+1 or changing to another cover class. j is a vector of estimated logit coefficients. J is the number of land cover states

Jj ,...,1

J

js

X

X

ijs

j

e

eP

)(

(

'

'

)

Table 1 Land Cover Classes

Top Level Classes 2nd Level Classification Final Classification Paved Urban Paved Urban >75%

Mixed Urban > 75% Paved Urban

Mixed Urban 25-75% Mixed Urban Mixed Urban Grass

Broad Urban

Mixed Urban <25% Mixed Urban Forest

Coniferous Forest Coniferous Forest Deciduous Forest Deciduous Forest

Vegetation

Grass Shrub Crops Grass Shrub Crops Bare Soil Bare Soil Clear Cut Clear Cut Water Water

Intensity of development event Devtype transition 1…24

Specific attributes of site Land cover Slope of cell Aspect of cell Soil quality Parcel ownership Parcel size Land value Distance to critical areas Distance to nearest road Distance to water infrastructure Distance to critical areas Distance to nearest road Distance to CBD Distance to forest source area Distance to nearest land cover transition Distance to nearest development event Restriction on minimum lot size

Independent Variables

Continued….

Spatial context of development Built-up densityRoad density % High erodible soils

% of prime farmland Mean patch size of land use/cover

Contagion of land use/coverDominant land use/cover Transition to land coverResidential units recently addedCommercial units recently addedRoad capacity recently addedChange in mean patch sizePosition on the urban gradientUrban Growth Boundary

Independent Variables

Land Cover Change in Km2Entire Image

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

mixedurban7.82%

paved6.71%

forest -8.21%

grass10.14%

bare soil18.97%

clear cut26.96%

sq

ua

re k

m 1991

1999

Current Research Projects

• National Science Foundation, Urban Research Initiative, “Reusable Modeling Components for Simulating Land Use, Transportation, and Land Cover,” Marina Alberti, Alan Borning, Scott Rutherford, Paul Waddell, $439,357, 1999-2001.

• National Science Foundation, “The Impact of Urban Patterns on Ecosystem Dynamics,” Marina Alberti, Derek Booth, Kristina Hill, and John Marzluff, $424,977, 1999-2003.

Current Research Projects

• National Science Foundation, Information Technology Research Initiative, “Interaction and Participation in Integrated Land Use, Transportation, and Environmental Modeling,” Alan Borning, Batya Friedman, Mark Gross, David Notkin, Zoran Popovic, and Paul Waddell, $3,500,000, 2001-2006.

• National Science Foundation, Digital Government Program, “Software Architectures for Microsimulation of Urban Development, Transportation, and Environmental Impact,” Alan Borning, David Notkin, and Paul Waddell, $600,000, 2001-2004.

Current Research Projects

• National Science Foundation, Biocomplexity Program, “Modeling the Interactions between Real Estate Development, Land Cover Change, and Bird Diversity,” Marina Alberti, Mark Handcock, John Marzluff, Paul Waddell, $1,128,818, 2001-2004.

• Federal Highway Administration, “Case Study on the Application of UrbanSim to the Salt Lake City Region,” Paul Waddell and Alan Borning, $150,000, 2002-2003.

• Puget Sound Regional Council, “Development of a Land Use Model,” Paul Waddell, Alan Borning, Marina Alberti, $150,000, 2002-2003.

Proposed Research Objectives for 2002-3(funded by sources other than PRISM)

• Development of the data and calibration of the existing UrbanSim model specification for the Central Puget Sound region (King, Kitsap, Pierce, and Snohomish counties) – pending funding from PSRC.

• Development of land cover classification and accuracy assessment for Landsat images every two years from 1986 to 2001.

• Calibration and testing of the initial version of the land cover change model for at least King County – pending funding from King County DNR.

• Work on an indicators and evaluation component for UrbanSim, to support a set of predefined indicators, and flexibility to allow users to modify and add indicators.

PRISM-Specific Objectives

• Developing close collaboration with the Crystal Team, to set up the protocols for coupling UrbanSim and Crystal, jointly defining the specifications for a water demand model, and implementing the model as a component within the UrbanSim architecture.

• Developing close collaboration with the DHSVM Team to set up the protocols for coupling UrbanSim and DHSVM through the land cover change model and jointly defining specifications to add the artificial drainage to the hydrological model in urbanizing landscapes.

PRISM-Specific Objectives

• Expanding the scope of the UrbanSim Indicators and Evaluation component to incorporate more environmental indicators, with input from other PRISM teams. Some or all of these additional indicators would come from other PRISM model outputs, so this would also require setting up protocols for passing the information from those models to the indicator component.

• Restructuring and teaching an Introduction to Urban Simulation course, scheduled for Spring 2003. It will serve as a workshop and introduction to urban simulation using UrbanSim, focusing on the application of the model to the Puget Sound, and development of indicators and the creation and evaluation of scenarios.