Modeling Land Use Change in Chittenden County, VT Austin Troy, PhD, Brian Voigt, graduate research assistant, University of Vermont Rubenstein School of Environment and Natural Resources

Shelburne road, circa 1937 And in 2003

New North End and Colchester, 1937 New North End and Colchester in 2003

Dorset St. Spear St. So. Burlington, 1937

So. Burlington, 2003 Dorset St. Spear St.

Some areas of major new development between 87 and 02

Close up: Shelburne development since 1950 Click for animation

Chittenden County Land Use 1982 – 1997

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 Population Density:

Percent Occupied Household Demographics Median Household IncomeAverage Age of Head of HouseholdAverage Household Size

Project: “Dynamic land use and transportation modeling” Purpose: to simulate future land use and environmental impact in Chittenden County under baseline and alternative scenarios Tools: UrbanSim and TransCAD + original modules for simulating environmental impact US DOT FHWA funded; Research started in 2003 under EPA grant Conducted at UVM Spatial Analysis Lab Collaborators: Resource Systems Group (RSG, Inc), CCRPC, CCMPO, UVM (Breck Bowden, Jon Erickson, Dave Capen, others)

Research Questions What will land use patterns in Chittenden County look like in years? How will these change under different scenarios? What effect(s) will future urban development patterns have on: –Water quality –Habitat fragmentation –Environmental aesthetics –Auto-dependency

Scenario Modeling with UrbanSim Simulate impacts of user-defined scenarios –Highway infrastructure –Utility infrastructure –Zoning –Land use policies (e.g., growth centers) –Exogeneous shocks (e.g. energy prices) Intended to facilitate discourse not predict policy adoption

Management Implications Help assess impacts of policy, planning and infrastructure investment alternatives Help find alternatives that accommodate future growth while minimizing social and environmental impacts Allows for stakeholder input

Modeling with UrbanSim University of Washington, Center for Urban Simulation and Policy Analysis: Paul Waddell – Model parameters based on empircal data analysis: cross sectional and longitudinal Integrates analysis of market behavior with land policies and infrastructure choices –informed by research in economics, sociology Does not predict total population / employment changes –spatially allocates growth based on externally derived estimates Simulates evolution of households, jobs and real estate –one-year time step –individual-based for household and employment location –grid-based real estate market from Waddell, et al, 2003

Dynamic Disequilbrium Approach Dynamic: feedback loops between components –Multiple processes interacting: households, jobs, real estate development and location choices –Different processes work at different time scales short: travel behavior medium: household / business location long: real estate / infrastructure development Disequilibrium: –avoids oversimplification of general equilibrium conditions (perfectly competitive market, products are homogenous, resources are mobile, present and future costs are known, etc.) –Does not re-equilibrate sectors at each step

UrbanSim Model Architecture Software is written in Python scripting language –model currently operates from a command line interface Open source framework –customize model components for location specific requirements / limitations –create new model components to address research interests Suite of sub-models that interact with a data repository (MySQL database) –land price - –accessibility – normal good w/positive economic value, derived from external travel demand model –economic transition – distribution of jobs through employment sectors –demographic transition – distribution of households by type over time –employment / household mobility – P(job / household moves from one location to another) –employment / household location – P(new or relocated job / household, located at a particular site) Each sub-model is recalculated at a user-specified interval –annual time step is commonly employed from Waddell, et al, 2003

data store model output 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

Household Synthesis Create synthetic baseline population (Beckman, et al, 1996) –iterative proportional fitting (IPF) algorithm that creates a household distribution that matches block group marginal distributions Data inputs –US Census marginal distribution tables (STF-3A) at the block group level –# households, total population, income, automobiles, presence of children, age of head of household, workers –Public-Use Microdata Sample (PUMS) 5% sample detailed description of household characteristics from Public-Use Micro Area (PUMA) Synthetic households assigned to available housing stock

Household Synthesis Block Group: Grid_ID:23674 HSHLD_ID: 23 AGE_OF_HEAD: 42 INCOME: $65,000 Workers: 1 KIDS: 3 CARS: 4

Travel Demand Model Often coupled with land use models –strong interdependence b/t phenomena –relationship widely recognized by research and government (US DOT: ISTEA 1991, TEA ) Evaluating land use and transportation scenarios –infrastructure performance –investment alternatives –air quality impacts

Travel Demand Model Process Steps Area of interest is divided into Traffic Analysis Zones (TAZs) –340+ in Chittenden Co. Four-step process –trip generation: quantify incoming & outgoing travel by zone –trip distribution: assign trips to zones –modal split: estimates trips by mode for each zone –traffic assignment: identifies trip route I = 375 O = 216 I = 17 O = 240 Zone IDWalkBusDrive …

Accessibility Model Consumers value access –work, shopping, recreation –household demographics determine preferences Distribution of opportunities weighted by composite utility of all modes of travel to set of opportunities Summarize the accessibility from each TAZ to various activities considered relevant for household or business locations Assign accessibility values for each gridcell based on TAZ results Travel utility remains constant, but the distribution of activities changes annually

Transition Models Computes changes to previous year employment / demographic conditions Models are analogous for employment and demographic transitions Use externally derived control totals that specify growth or decline from previous year totals –employment: distribution of jobs by sector –households: distribution of households by type –control totals define new distributions, or model assumes static distributions for duration of model run Probability a specific job / household is lost is proportional to the spatial distribution of the jobs by sector / household by type

Transition Model Process Steps Model process steps –calculate the number of jobs / households to be added or removed –in the case of growth new jobs / households are added to a list of unplaced jobs / households –in the case of decline random subset of jobs / households removed from set of current jobs / households selected job / household locations are marked as vacant Employment Location Choice Model Job ID Unplaced Jobs … Employment Change Total Employment Year Employment Control Totals … Employment Change Total Employment Year Employment Control Totals V V

Mobility Models Predicts the probability that a particular job / household will move from their current location Based on annual mobility rate calculated from prior year observations –employment: transitional change reflecting layoffs, relocations, closures –households: differential mobility rates for renters, owners, and households at different life stages Model structure is analogous for households and employment Probability a job / household will move is proportional to their spatial distribution

Mobility Model Process Steps Procedure generates a random number for each job / household Compares random number to the job sector / household type mobility rate A random number greater than the mobility rate indicates a decision to move –previously occupied locations added to set of vacant locations –job / household added to set of unplaced jobs / households mobility rate = V V V V V Unplaced Households Household Location Choice Model … Household ID

Location Choice Models Predicts the probability that a new job / household (from the Transition Models) or a relocated job / household (from the Mobility Models) will be located in a specific gridcell Models can be generalized for entire region or stratified by employment sector / household type Assumes the stock of available locations is fixed in the short run Set of locations is a combination of the vacant locations and gridcells available to accommodate additional development (of the specified type) Models are analogous for employment and household location choices Employment –define the maximum rate of home-based employment based on observed regional conditions –model variables include: building age, real estate characteristics, regional accessibilities Household –incorporates the classic tradeoff between transportation cost and land cost –model variables include: housing characteristics, regional accessibilities, urban design-scale

Location Choice Model Process Steps Processes each job / household in the mover queue in random order Queries gridcells for alternative locations to consider Selects a location from the list of alternatives Selected space becomes unavailable to the remaining jobs / households in the queue Placed jobs / households are removed from the list of unplaced jobs / households Newly occupied locations are removed from the list of vacancies Unplaced Households Household Location Choice Model … Household ID

Real Estate Development Model Simulates the construction of new development or the intensification of existing development Development types: gridcells are classified by the number of residential units and the amount of nonresidential square feet they contain Predicts future development patterns based on analysis of prior development events –year built data is key Development constraints are based on user-specified decision rules –identify allowable uses within specified development types –identify allowable transitions from one development type to another Model variables include: site characteristics, urban design-scale, regional accessibilities, and market conditions DEV_TYPE_IDNAMEMIN_UNITSMAX_UNITSMIN_SQFTMAX_SQFT 1R R R R R R R R M … 18C C I I I Government VacantDevelopable Undevelopable0000 Simulates the construction of new development or the intensification of existing development Development types: gridcells are classified by the number of residential units and the amount of nonresidential square feet they contain Predicts future development patterns based on analysis of prior development events –year built data is key Development constraints are based on user-specified decision rules –identify allowable uses within specified development types –identify allowable transitions from one development type to another Model variables include: site characteristics, urban design-scale, regional accessibilities, and market conditions

Real Estate Developer Model Process Steps Identify the set of allowable transition types for each gridcell Estimate the probability of transition from the existing type to each member of the set of allowable types New development type is defined as the outcome of the selection process –this includes the possibility of no change Update database to reflect new gridcell development types

Land Price Model Assumptions –price adjustments alter location preferences –households are price-takers –individual preferences are capitalized into land values –more expensive alternatives will be chosen by those with lower price elasticity of demand Hedonic analysis –house as a bundle of individual components –measure the preference for specific attributes (structural, neighborhood, environment) through real estate transactions or assessor’s data Model variables include: site characteristics, regional accessibilities, urban design-scale, and market conditions Land price is updated annually after construction and transaction activity is complete Update price defines the market for subsequent year’s transactions

UrbanSim and Travel Demand Models External to the UrbanSim system User-specified time interval for TDM iteration –typical specification is 5 years –processing pattern continues for the duration of the simulation TDM accessibilities Base year database Model specification Data prepyear 1year 2year 3year 4year 5 Run submodels Update database Run submodels Update database Run submodels Update database Run submodels Update database Run submodels Update database Recalculate accessibilities

Model Output Output database: defines gridcell state at the end of the model run –data can be cached annually for trouble shooting and further analysis Indicators –conveys info on the condition and / or trend of a system attribute –primary mechanism for communicating model results –can be computed at varying levels of aggregation TAZ, block group, city, county –examples of predefined indicators transportation: per capita gas consumption, % trips walked, % trips SOV residential development: # units added, density, occupied units, unit value nonresidential: square feet added, vacancy rate other: gridcells per development type, area of land converted households: car ownership, mean income, unplaced households –system allows user to define new indicators Data visulatization –maps –charts –tables

Visualizing Model Outputs – Land Use Change:

Vermont UrbanSim Application Geographic extent –Chittenden County, VT Good site because relatively isolated 150 meter grid cells Annual time step Model calibration: 1990 – 2002 Model run: 2000 – Software: UrbanSim, TransCAD, MySQL, LimDEP, Access

Data Development Economic –land value, employment location, type, and size, Structures –Housing and business location, characteristics, year built, lots Biophysical –topography, soils, wetlands, flood plains, etc. Infrastructure –roads, transit, travel time to CBD, distance to Interstate Planning & zoning –current and future land use, development constraints Census –household characteristics defined by: age of head of household, income, race, # of autos, children

Control Totals Model does not predict population / employment changes –spatially allocates changes to population / employment Control totoals are externally derived inputs –population and employment estimates –macroeconomic model of regional economic forecasts –land use and transportation system plans Employment: VT Department of Labor Demographics –US Census: 1990 & 2000 –Public-Use Microdata Samples (5%): 1990 & 2000 County projections??

Employment Data 1990 data –VT Secretary of State database tradenames & corporations employment location, description of business –Greater Burlington Industrial Corporation inventory of manufacturers w/in Chittenden County 2000 data –Claritas business listings geocoded location number of employees / employment sector Data conversion –extensive geocoding required for base year data development –# of records = –records placed = (88%) Data attribution –jobs classified by NAICS sector & grouped into general categories –estimate # of employees, square footage / employee, improvement value

Employment Data SECTOR_IDNAME 1Lumber and wood 2Other durable 3Food products 4Other nondurable 5Construction 6Mining 7Transportation 8Wholesale trade 9Retail trade 10Finance 11Services 12Education 13Government 14Agriculture 15Utilities

Employment by Sector:

Regional Employment 2000 Large employers –~90 businesses with > 75 employees –IBM, IDX, Metro Airlines, Lane Press, UVM Small business –~1100 small businesses with 1 employee –~4000 small businesses with <= 5 employees

Employment Data` Block Group: Grid_ID: Employment_ID: 427 Sector: 2 Employees: 135 Block Group: Grid_ID: Employment_ID: 413 Sector: 7 Employees: 2

Structure Data Housing point and parcel data used for geolocating structures Sequence of development estimated through attributing with year built data –Only available digitally for about half of Chittenden County’s towns (but most of structures) –Other towns had to be modeled with help of e911 database going back to 1998 Property values and some attributes dervied through new grand list data Land price model uses VT Dept. of Taxes sales database to regress sale price against attributes

Year built example Parcel ID: Year: 1930

Environment sub-modules Working on developing sub-modules that take output from UrbanSim to estimate environmental impacts on landscape –Modeling water quality/ watershed impairment/ nutrient output based on development intensity (Breck Bowden) –Modeling habitat fragmentation and associated wildlife impacts (David Capen) –Future project: mobile air quality

Other value added components GIS data integration software tools to facilitate the easy visualization of outputs and the manipulation of spatial inputs directly in GIS (Brian Miles) Software “wrapper” to more seamlessly integrate UrbanSim and TransCAD (RSG)

Alternative Scenarios: what if? Policy events –Change in Act 250 –Growth centers legislation –Zoning changes –Urban service boundary Investments –New highways –New exits –New utility infrastructure Exogenous shift –New major employer –Loss of major employer –Dramatic energy price increase base year establish growth center(s) policy event 1 employment opportunity employment event alter transport infrastructure investment increase density policy event 2

Scenario modeling allows us to: Simulate the effect of these changes on – land use patterns, –densities, –commute times, –energy usage, –mobile emissions, –employment and residential location, –environmental quality …And compare them against the baseline

Applications of scenario modeling Help towns estimate the effects of planning and zoning changes Help the State, RPC, and MPO estimate the impacts of proposed policies with state or regional effects Help transportation planners compare transportation project alternatives, including creating a model of induced growth based on Vermont data Help stakeholders get involved in the process of decision making

Fall Workshop Only a limited number of scenarios can be modeled due to time constraints Meeting planned for November 2006 with local, county and state planners to collaboratively define and prioritize a set number of model scenarios Please and add your name to the workshop information and availability list during the break or contact us

Lessons so far Chittenden County is a good site for this model Data development is difficult and time consuming –historical data is integral part of model but hard to find –similar data from individual towns often feature different data formats, attributes, and level of completion –data requirements for large scale model make application in rural areas challenging –Data availability limits ability to expand to other counties Reward: empirically based model Stakeholder input and collaboration is key

Project Status Near done: data development, accessibility model, household synthesis, and GIS visualization tools To do: –Compute model coefficients (early Fall 2006) –Population TransCAD with data (late Fall 2006) –Run 1990 model and calibrate against 2000 data (late Fall 2006 through Winter 2007) –Scenario planning meeting (late Fall 2006) –Run scenarios (all of 2007) –Develop methodology to utilize model output as input in ecosystem modeling efforts (late 2007) –Subsequent stakeholder meeting (late 2007) –Refine and document (2008)

Acknowledgements Current funder: US DOT Federal Highway Administration, Previous funders: US EPA, MacIntire Stennis Program, Northeastern States Research Cooperative Graduate researchers past and present: Brian Voigt, Brian Miles, John D’Agostino, Weiqi Zhou UVM Collaborators: Breck Bowden, Jon Erickson, David Capen, Alexei Voinov UVM Spatial Analysis Lab and Rubenstein School of Environment Outside Collaborators: –RSG: Stephen Lawe and John Lobb –CCRPC: Pam Brangan, Michelle Maresca, Greg Brown –CC MPO: David Roberts –University of Washington Center for Urban Simulation and Policy Analysis: Paul Waddell, David Socha, many others