UPlan: A Versatile Urban Growth Model for Transportation Planning

TRB Paper 03-2542

Robert A. Johnston Dept. of Environmental Science and Policy

University of California One Shields Ave.Davis, CA 95616

rajohnston@ucdavis.edu(530)582-0700

and

David R. ShabazianSacramento Area Council of Governments

3000 S. St.Sacramento, CA 95816drshabazian@sacog.org

(916) 457-2264

October 22, 2002

Resubmitted to the Transportation Research Board forPresentation at the Annual Meeting, January 2003

Keywords: GIS, urban modeling, urban planning, impact assessment, induced development, growth-inducing impacts

Word count: 7,919 plus one table

UPlan: A Versatile Urban Growth Modelfor Transportation Planning

ABSTRACT: We review urban models useful in transportation planning, focusing especially on

ones that are based on geographic information systems (GIS) software. We then describe UPlan, a simple model written by us in the ArcView GIS. Several different applications of UPlan are outlined, involving transportation planning and the analysis of the growth-inducing effects of new facilitites, to demonstrate its use. Such models are coming into use for NEPA assessments and for joint land use and transportation planning.

I. INTRODUCTIONIn this paper, we describe a Geographic Information System (GIS)-based urban

growth model that runs in the Windows version of ArcView on a personal computer. The model was designed by us to rely on a minimum amount of data, but allocates urban growth in several land use types for small (parcel-sized) grid cells. It is a scenario-testing model that can be applied to any county or metropolitan region and that is transparent to the user, making it easy to change the assumptions for land use allocation. The model is rule-based, that is it is not strictly calibrated on historical data and uses no choice or other statistical models. Projecting the detailed footprint of development with several land use types allows us to then apply various urban impact models, that forecast soil erosion, local service costs, and other impacts. We describe several applications of UPlan. Any state/regional/local transportation agency, citizens group, or county planning department will be able to utilize this model, using generally available datasets.

II. BACKGROUNDThe 1991 Intermodal Surface Transportation Efficiency Act (ISTEA) marked a

major turning point in how transportation modeling is conducted. Prior to the Act, transportation planning occurred in somewhat of a vacuum. Travel demand models were run with the same land use inputs for all scenarios, so changes in land uses due to network improvements were not accounted for. The basic concern of most modeling efforts was to simply test how network improvements affected congestion and air quality, overlooking how those improvements influence urban development. Since the enactment of ISTEA, however, transportation agencies have begun to adopt methods that simulate changes in land use, as well as changes in travel. These integrated models, or, more commony, linked transportation and land use models, enable planners to develop a better understanding of how these urban systems interact.

Urban models are an improvement over travel demand models of the past, both theoretically and operationally. However, the urban models considered to be the most comprehensive and behaviorally based (land market bidding models) are also very tedious to calibrate and operate. They take 2-3 years to develop the data and calibrate the model and cost $1-2 million. For planning agencies and citizen groups interested only in the land use results of these models, they will likely find them prohibitively complicated and expensive. It is in this context that the geographic information system (GIS)-based land use models are especially useful.

III. TYPES OF URBAN MODELSAs there are many types of urban models, we should start by describing the

various other types, to place GIS models in perspective. The FHWA has recently put up a Web site, called the Toolbox, that outlines a variety of analysis methods useful in transportation planning and in evaluating transportation plans and projects (http://www.fhwa.dot.gov/planning/toolbox/land_develop_forecasting.htm). Their typology of methods for forecasting land development patterns is: 1. Proximity-Based Forecasting. These are regression models that project development based on the proximity of past development to transport facilities and other urban infrastructure.2. Delphi/Expert Panel. Several case studies of these methods are given. The Delphi method has also been documented in a TRB report (Land Use Impacts of Transportation 1999).3. Accessibility-Based Forecasting. Accessibility, derived from a travel model, is used to forecast development. 4. Simple Land Use Models. These are zone-based models based on a small set of equations defining relationships with accessibility and past development rates. HLFM II+ is a FHWA-supported model for use by small MPOs. 5. Complex Land Use Models. These can be a land use model that interfaces with an existing travel model, or an integrated urban model with land development and travel models together. These models generally use land prices, and sometimes floorspace lease values, to represent demand for space. They also use accessibility and other factors to represent site attributes. DRAM/EMPAL has been widely used in the U.S. and does not use land value or floorspace lease value data and so is the easiest to implement. TRANUS and MEPLAN have been applied to many regions all over the world and do rely on land market data. A review of complex land use models can be found at Wegener (1994).

Another way to categorize land use models is to examine those in use in regional transportation planning agencies. The following table is derived from Miller, Kriger, and Hunt (1998) and updated to 2001. It shows the combinations of land use models and travel models in use or in development in the U.S. It is important to note that most MPOs use the judgement method of land use forecasting and then use this single forecast for all transportation investment scenarios. This is an inaccurate method, in that improvements in radial accessibility will generally increase the spread of land development. Significant additions to road capacity, especially on the edges of congested urban regions, will increase land development in those areas, according to the official study in the U.S. (Expanding Metropolitan Highways 1995). If these land use impacts in the outer areas are not assessed, the NEPA documents will be inaccurate in that the studies will likely bias the projections of travel and emissions downward for highway improvement plans and projects. The secondary effects of land development on habitats, water quality, farmlands, and other systems will also be underprojected.

Note that UPlan is in the Connected Land Use Models category, in its application in Sacramento with a travel model, which we describe below. Equilibrium Allocation land use models are a type of model in the Complex Land Development Models category in the FHWA typology and are used in several regions in the U.S.

We believe that all MPOs should adopt land development models of some sort. The advantage of taking an overview of these models is so MPOs can see that they can

Travel Model/Land Use Model Integration Matrix

Travel ModelsLand Use Models

Trip-Based Models Tour ActivityStandard Enhanced Complex Aggregate Simulation Aggregate Simulation

Stan

d A

lone

Factored        

JudgementFresno

San Joaquin  

Boise

New Hampshire

San Francisco County

Policy+Trends Allocation   SACMET Edmonton    

Con

nect

ed

Rule-Based Allocation    SACMET +

UPLAN      

Equilibrium Allocation (e.g.

DRAM)

 San Diego

Puget Sound

San Francisco Bay Area

Atlanta

Santiago

Portland (now)

   

Market-BasedAllocation       Portland(soon)

Inte

grat

ed

Aggregate Economic

(Input/Output)

Sacramento (MEPLAN

test)London

Oregon Statewide

Disaggregate Economic

Microsimulation       

start with a simple Rule-Based Model, such as UPlan, and then advance to a more complex model type as they gain expertise and gather more data. This table also shows that agencies can move to the right, improving their travel models, or they can move downward, improving their land use modeling, first. As the errors from not forecasting land development changes can be substantial (Rodier 2001), it seems that MPOs should advance their land use modeling, at least to the Rule-Based level or the Equilibrium Allocation level, before improving their travel models to account for trip tours or household activity allocation.

Once an agency decides which general type of model they wish to develop, they can make use of another recent review, in which the USEPA suggests criteria that MPOs can use to select a specific land use model, such as policy relevance, cost, data requirements, and accuracy (Projecting Land-Use Change 2000).

IV. REVIEW OF GIS-BASED URBAN MODELSThe main applications of GIS have been as “spatial data models” used by

planning and natural resource agencies to organize and display spatial data. Recently, however, professionals in these fields have begun to recognized the potential for GIS to also be used as a “spatial process model,” including urban growth models (Heikkila 1998).

Many GIS urban models use suitability criteria, in one form or another, for the selection of developable sites. Suitability ratings are generally determined by proximity to infrastructure and services. The higher a suitability rating, the more attractive a site is for development. The models also have the capability to exclude development from restricted areas such as public lands, steep slopes, protected habitats, etc. This rating of the landscape is applied to disaggregated units of land resulting in site-specific allocations. With the overlay capabilities of GIS, various allocation patterns can be compared to natural resources data to assess potential environmental impacts, as well as economic losses resulting from natural disasters such as flooding or wildfires.

The models discussed below are included as examples of the various forms of GIS-based urban growth models currently available.A. California Urban Futures Model (CUF)

CUF was one of the first GIS-based urban growth models used to simulate regional and subregional growth and relies on vector (polygon-based) data for its analysis. A housing market is modeled where demand is a function of population growth (calculated exogenously) and land supply is determined by spatial representations of factors such as general plan land use categories, current land uses, slope, wetlands, agriculture lands, land development costs, service costs, and jurisdictional boundaries. These factors are combined to supply the model with the geometry, location, and land attributes of Developable Land Units (DLU). Each DLU polygon is also evaluated by its potential profitability, which is determined by subtracting all the development costs from the sale price of a new home (all costs and prices are exogenous). The model allocates population to DLUs in four steps:

1. All DLUs are ranked by their potential profitability;2. DLUs that are unsuitable for development, based on their attributes, are

dropped;3. Remaining DLUs are sorted from high to low profit potential; and then

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4. Future population is allocated to DLUs according to density rules set by the user. DLUs with the highest profitability are “filled” first and then subsequently lower profitability cells are “filled” until all of the future population has been allocated (i.e., the market is cleared).

It is assumed that developers are price-takers with respect to the price of new homes and raw land. It is further assumed that developers want to build on sites that have the highest profit potential. Once all allocation has occurred, the model then annexes the new urban lands to incorporated cities or forms new cities from new development that is noncontiguous to existing incorporated area (Landis 1994; Landis 1995).

The model does have its shortcomings, such as only simulating residential land consumption. CUF also relies on exogenously specified land prices; therefore, development spills over jurisdictional boundaries rather than possibly becoming denser in reaction to high land prices. Nonetheless, CUF has made contributions to the field of urban modeling. It operates on individual polygons of land rather than zones, making it fairly disaggregated. It also incorporates the influence of private land developers on land use changes. Further, it is based in GIS, making it a tool available to many planning agencies (Landis 1994). Because it requires data for raw land prices, construction costs, site improvement costs, service costs, development fees, and other development costs, it is somewhat data-hungry and costly to apply. The model cannot represent infill, because of the lack of a spatially detailed data layer for existing urban land uses for the base year. B. Cellular Automaton Model

This urban growth model is based on the cellular automaton construct used to simulate organic growth and was developed from the same framework Clarke used for a wildfire model. The model relies on GIS raster (cell-based) data of urban extent, slope, transportation networks, and protected lands. Cells representing the urban extent for a particular year are used as the “seeds” of future development. The model then randomly looks for cells to urbanize. Each cell selected is evaluated in terms of the spatial properties of surrounding cells, which are determined by:

1. A Diffusion Factor – how development will be dispersed and its movement outward through the road network.

2. A Neighbor's Coefficient – the level of connectivity to existing urban cells needed to continue outward expansion or develop infill sites.

3. A Breeding Coefficient – the potential for a cell to become a seed that will attract new growth.

4. A Spread Coefficient – a control of how much contiguous outward expansion occurs.

5. A Road Gravity Factor – used to attract development along roads.6. A Road Weight – a set of weights used to represent distance from a road,

where cells closer to a road have a higher weight.7. A Slope Resistance Factor – used to discourage development of steep slopes.The model has been designed to be self-modifying, using another set of

development rules:1. When the rate of growth exceeds a critical value, the diffusion, breeding and

spread factors are increased.2. When the rate of growth falls below a critical value, the diffusion and spread

factors are reduced.

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3. The road gravity factor is increased as roads are added to the network.4. As the developable land decreases, the slope resistance factor decreases.5. As development moves up onto steeper slopes, the spread coefficient

increases to encourage development in flatter areas.If the randomly chosen cell is a suitable site, the model then decides whether or not to convert that cell to “urban” subject to a set of probabilities (Clarke, et al. 1996a and 1996b).

In order to calibrate Clarke’s model, it is necessary to have several base years of data so that the factors/coefficients can be adjusted to replicate the growth patterns seen through time. For example, in the application of the model to the San Francisco Bay area, Clarke started with a raster image of the urban extent in 1900 as the seed and then grew the urban area. Rasterized maps of urban extent for 1940, 1954, 1962, 1974, and 1990 were used for model calibration. The development factors were adjusted until a reasonable fit to the historical data was found. Calibration appears to be quite time consuming and tedious, as two phases are needed. First, a “visual” phase is conducted to determine an appropriate range of values for each parameter. Then, numerous runs are executed until reasonable goodness-of-fit statistics are achieved (Clarke, et al. 1996a). Future urban development is then projected as a continuation of the past growth patterns used during the calibration.

Clarke’s model lacks the ability to distinguish activity types as it operates on simple “urban” and “non-urban” designations. The model also has rather poor resolution as allocations are assigned to relatively large 300 m cells. It runs in the UNIX environment and requires a tremendous amount of spatial data. The model also has neither coherent economic theory nor a behavioral component to help understand its results. However, the model is probabilistic, it runs quickly, and can be applied to any region with the necessary data. C. California Urban Futures Model-2 (CUF-2)

In CUF-2, significant changes were made, resulting in a cell-based model that uses regression analysis to determine land conversion probabilities. The idea is similar to Clarke’s model in that Landis uses data for past land use patterns to predict how future changes will occur. The endogenous population growth model now projects employment as well as households in ten-year intervals. DLUs take the form of one-hectare (100 m) cells, rather than polygons, and are constructed using similar land use and geographic factors as before. CUF-2 differs from Clarke’s model in that historical data are used to derive probabilities of various land use changes in a cell, estimated on nearby site and community characteristics such as population and employment growth, proximity to job centers, proximity to transportation facilities, etc. These probabilities are assigned to each cell and then the allocation routine uses these probabilities to either develop or redevelop the cell according to its potential profitability. The land uses can "bid" against each other in the choice model formulation, but there is no economic basis for the "bidding."

Like Clarke’s model, CUF-2 has the advantage of being probabilistically based. However, the land use change probabilities are estimated on the differences observed between 1985 and 1995 land use data sets. Therefore, the probabilities are estimated on only two years of data and changes in future development patterns are assumed to be

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similar to those changes that occurred between 1985 and 1995. The authors state that the applicability of the results to other time periods is "unclear" (p. 824).

The model has inconsistent signs for many coefficients among the counties, making the land use change probabilities difficult to interpret. This problem may reflect the model's lack of a theoretical basis in urban economics. The goodness-of-fit measures for many counties are poor and highly varying across counties by specific land use change type. Many are quite poor for changes to specific land use types (pp. 803-821). The authors overall assessment is that these models are "generally less capable of predicting which specific sites will shift land use" (p. 824, emphasis theirs).

This project presents useful data and analysis on land use change, but the different coefficient signs across counties and the low power of the models for each type of specific land use change tell us that such a model type may not be very useful for projecting future land uses. In addition, the model did not use local land use plans as an attractor or constraint and so would probably not be accepted and used by local officials. D. Other Models

Some GIS models have used accessibility data from a travel model as part of the GIS development attraction function. The most extensive set of these that we are aware of is the work by Marshall and Lawe, then at Resource Systems Group, Inc. in Vermont. They have linked a Lowry-type land allocation model to typical regional travel models in the Burlington, Vermont region, the seacoast region of Maine/New Hampshire/ Massachusetts, and the Tampa Bay region. Generalized accessibility is used, which is based on composite impedance for all modes. The other typical data layers include slope, soils, sewers, existing land uses, and protected lands. Some of the models are menu-driven (Marshall and Lawe 1996). No standard software is available to perform this integration of travel models with the GIS land allocation model.

The new version of the Index model, called Smart Growth Index and supported by the USEPA is a similar model. It is now going through testing in some regions in the U.S. It will interface with the Tranplan, Minutp, and Viper travel model systems software. Initial test versions are available through the Urban and Economic Development Division at EPA, but future versions will be proprietary and leased from the developer, Criterion Planners of Portland, Oregon (url: www.crit.com). Cell size ranges from 2 to 40 ha and any set of land uses can be projected. This model requires input data for the base year including residences by type and employment by type, by cell or polygon. The model allocates land uses with decision rules based on the user-set criteria and weights. This model integrates travel modeling with GIS land use allocation. It is not clear how this model differs from the work done by consulting firms in the past, on an ad hoc basis, such as in the models developed by Marshall and Lawe. Smart Growth Index appears to be difficult to set up and run in conjunction with an agency's regional travel model.

The available model closest to UPlan seems to be the WhatIf? model (Klosterman 1999). It is a rule-based model, running in ArcView on polygons. The user defines the rules for attraction for, and constraints to, development. WhatIf? can operate on land use types as defined by the user. Polygons are 0.25 ha or larger, generally. Redevelopment is represented, but only crudely. The model produces several reports and has easy-to-use dialog boxes. The model is proprietary and the consultant's services must be purchased, to help apply the model.

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V. THE UPLAN MODELA. Model Design Objectives:

Rule-based land use models are a good method for MPOs and counties to start with, in that most agencies now have a GIS staff. In addition, this class of model uses datasets that are generally available. We believe that a GIS-based urban model must project several land use types, in grid cells that roughly match the development parcel sizes. Grid (raster) data is preferred over vector data also because model runtime is minimized. At least three residential densities must be represented, in addition to industrial and two densities of commercial land uses. This helps to identify fiscal, runoff, water quality, and habitat impacts accurately, in later analyses. The model need not be strictly calibrated on historical data because its intended use is for long-range scenario testing. However, it relies on fine-grained grid data that represent existing urban land uses, local general land use plans, and all other relevant natural and built features that define the model. It must be deterministic and rule-based, so as to be transparent to users and to replicate scenarios. The allocation rules must simulate land markets, broadly. Most importantly, the model must be inexpensive and be applicable to counties, metropolitan regions, watersheds, and bioregions. If all required digital spatial data are availble, UPlan can be applied in a few weeks by an ArcView user.B. The Role of Models:We view models as tools for consensus building. Models may be used for: 1. Analysis of past and present spatial patterns of phenomena (with maps and descriptive statistics), 2. Projection of the most likely future patterns of these conditions, and 3. Prescription of desired future conditions and requisite policies (and testing of these policy sets).

The background studies are more important than generally thought. Analysis and projection can help to identify common ground among user groups, as they come to understand and accept current problems and future likely problems. Regarding prescription, we believe the role of modeling to be one of clarifying value choices, especially the values that are traded off when one selects any particular policy set. In order for models to help us to understand these tradeoffs, the models must be complex enough to represent a great variety of social, economic, and environmental phenomena, but simple enough for citizens to run scenarios.

So, models allow us to learn together about the urban region and to test prescriptive concepts. Models can greatly facilitate bargaining, by bringing all interest groups into the planning process and allowing the quick testing of the ideas of all participants. Models that run in a few minutes or less on PCs, can be used in meetings, aiding a detailed discourse.

Models can be useful in all these roles, because they are systematic assemblages of our assumptions about how the world works. They provide a consistent framework for our discussions and analyses. Scenario testing models do not provide answers, they just illustrate various points of view and help rank order scenarios. Since they employ graphical outputs, models can greatly help get interest groups to meet together and bargain, because of the evocative methods of analysis and portrayal used.

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C. Model StructureUPlan uses any year as its base year and allocates the increment of additional land

consumed in future years. This increment of urban growth is in discrete land use categories: Industrial, High-Density Commercial, Low-Density Commercial, High-Density Residential, Medium-Density Residential, and Low-Density Residential. The user can designate any land use types desired, however. In some applications, we match the employment categories to those of the MPO, such as Manufacturing, Retail, and Other. Sometimes, we define a Residential Very-Low category, if there are a lot of very-large-lot residences.

County or regional land consumption is calculated endogenously, based on user-specified assumptions. UPlan is built with a module that allows the user to input demographic and land use density factors that are converted to hectares (ha) of land consumed for each land use. The conversion starts with population projections for counties or the entire region. The user is then prompted to specify the demographic and land use characteristics he or she would like to test. To determine ha needed for future housing, the user specifies persons per household, percent of households in each density class, and average parcel size for each density class. A similar conversion is used to determine ha of land consumed for industry and commerce and uses workers per household, percent of workers in each employment class, and average land area per worker. These calculations produce a table of land demanded, for each land use type, from which the model operates its land allocation routine. In general, the input values for baseline cases are derived from recent experience in each jurisdiction. Overall gross land consumed per person can be derived from historical data, such as the USDA Natural Resources Inventory data used in Fulton (2001).

In standard applications, it is assumed that development occurs in areas that are attractive due to their proximity to existing urban areas and transportation facilities, such as freeway ramps. It is also assumed that the closer a vacant property is to an attraction, the more likely it will be developed in the future. Following this assumption, each development attraction (described below) is surrounded by user-specified buffers. The user can designate the number and size of the buffers and assign an attractiveness weight to each buffer. Buffer specifications are applied to each of the attraction grids and then the grids are overlaid and added together to make a composite Attraction Grid.

There are areas where development cannot occur, which are called exclusions. Exclusions include features such as lakes and rivers, public open space, existing built-out urban cells, and other such areas where development cannot occur. There are also other features that the user can choose to exclude, such as sensitive habitats, 100-year floodplains, and farmlands. Once the user decides which features are to be excluded, the model adds together the various exclusion grids to generate a “Mask.” Once the Attraction Grid and the Mask Grid are generated, the model overlays the two grids and attraction cells that fall under the mask are converted to “no data” cells, thereby removing them from possible development allocations. This process creates the Suitability Grid, which becomes the template for the allocation of projected land consumed in the future.

The Suitability Grid is overlaid with a grid of the 20-year General Plan land use map for the region enabling the model to further isolate areas which are suitable for each of the various land use categories that are allocated. The model is then ready to allocate projected ha of land consumed in the future.

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The model allocates future development starting with the highest-valued cells. As the higher-valued cells are consumed, the model looks for incrementally lower-valued cells until all ha of projected land consumption are allocated. The model does this for each of the discrete land use categories and the user can decide the order in which land uses are allocated. By default, the model starts with industry, then proceeds to high-density commercial, high-density residential, low-density commercial, medium-density residential, and finally low-density residential. This is roughly the order in which land uses can outbid each other.

This attraction method does not apply to low-density residential, which is randomly allocated throughout available rural areas to represent the prevalent noncontiguous pattern of exurban rural development. Because the allocation is random, low-density residential does not use the Attraction Grid to find the best locations; however, the Mask Grid does apply. While all other land uses are allocated in 50 meter grid cells (≈ 0.25 ha), 200 meter grid cells are used for low-density residential to represent average parcel size (≈ 4 ha). The user can change the grid cell sizes, if desired.

One can generally calibrate the location of residential development by using the Census block population data for 1990 and 2000, both in the 2000 blocks. One can purchase these change data inexpensively from GeoLytics, Inc., and they are especially useful for determining the location and percentage of growth for Low-Density Residential. This land use consumes over half of all land, in many jurisdictions. D. Data Requirements

Most of the data listed below would typically be available in any region where UPlan is applied.

Attraction Grids: 1. Freeway Ramps, 2. Highways, 3. Major Arterials, 4. Minor Arterials, 5. Cities, and 6. Passenger Rail Stations. For industrial allocation only, we also use 7. Airport and 8. Port.

Exclusion Grids: 1. Land Use Plans; 2. Rivers (A user-specified distance buffers the rivers before they are added to the Mask Grid. This precludes development from occurring too close to waterways.); 3. Lakes (buffered); 4. Vernal Pools (seasonal wetlands; buffered); 5. Floodplains; 6. Slope (Steep slopes are used to mask out areas that are too steep to develop, and moderately steep slopes are used as a discouragement factor for areas that remain. The discouragement factor works by dividing the sum of the Attraction Grid weights by values >1, taken from a lookup table.); 7. Public Lands; 8. Existing Urban (This grid is ofter constructed using satellite data. This layer can be corrected and updated with parcel data, where such data exist.); 9. Permanent Open Space; 10. Farmlands (The “exclusive agriculture” designation in a local land use plan.)

VI. EXAMPLE PROJECTSWe review some of our early projects, to show the versatility of UPlan.

A. UPlan by Itself: Espanola, New MexicoUPlan was used as part of a community outreach effort initiated by the University

of California in the region surrounding the Los Alamos National Laboratory in New Mexico. The study was focused on the City of Espanola (population 10,000), which is roughly 30 km north of Santa Fe and 20 km east of Los Alamos. Espanola has begun to see growth that has spilled over from Los Alamos as that community and the Lab continue to expand. The Sustainable Communities Consortium at the University of

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California at Davis was hired to help the region plan for future growth. The Consortium employed UPlan to assist in forecasting where future growth might occur and what impacts might result from this urbanization. The study area is about 45 km by 45 km and was done in 25 m grid cells.

Data for the region were provided by the Earth Data Access Center (EDAC) at the University of New Mexico at Albuquerque. EDAC possessed all of the topographical and other base map data needed, but did not have the required land use data, which were assembled by the Consortium. Staff used aerial photographs and paper maps provided by the City of Espanola to create coverages of general plan and zoning designations and to describe existing urban development at the parcel level. Since the staff had only recently been introduced to the GIS software, the process of digitizing and preparing these coverages for use in the model required nearly 3 person-months.

While these data were being prepared, other staff members prepared the EDAC data for use in the model. The EDAC data needed some processing such as reprojecting, clipping down to the study area, joining and dissolving adjacent coverages, and reselecting and reclassing attributes so as to be compatible with the model. This processing took a little over a person-month to complete, again with inexperienced personnel. The processing of all data was assisted by the authors, who guided Consortium staff on structuring the data to work with the model. Roughly one week of our time was needed to guide data preparation.

Once the model was running, the Consortium staff was able to operate it with little assistance from the authors. The staff became familiar with the operation of the model and its output within 2-3 days. They were then able to test a wide variety of planning options for the Espanola region. The group produced several scenarios for two future years each, testing policies for different densities, degree of infill development, changes in county land use plans, and allowing development in various adjacent pueblos.

Overall, the implementation of UPlan on the Espanola region took four months. However, much of this time was spent familiarizing the Consortium staff with the GIS software. The staff estimates that given their level of knowledge today, they could implement the model in 1.5 person-months, if no digitizing was needed. Clearly, the ease of implementing the model is dependent on the availability of data and one’s familiarity with ArcView (and ArcInfo, to create data layers).

Other examples of using UPlan by itself include a grad student modeling the effects of a new freeway in the Salt Lake City region. This project was completed in about two weeks of effort, as most data layers were available. In another study, we have projected the effects of the proposed Foothill Freeway on urbanization in the San Joaquin Valley of California. We also evaluated the effects of proposed high-speed rail lines on development patterns. Further, we have projected the effects of urban growth on agricultural land availability in the San Francisco Bay area and the Central Valley. A UC Berkeley research group is examining the effects of urban growth and vineyard development on oak woodland habitats in Sonoma County, California, using UPlan and a vineyard expansion model. B. UPlan with A Travel Model: Sacramento, California

The most recent test application of UPlan is at the Sacramento Area Council of Governments (SACOG), the MPO for the Sacramento region, where it has been linked to SACOG’s Minutp-based travel demand model, SACMET. UPlan is being used to test

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changes in development location due to changes in accessibility. In each analysis zone, accessibility is determined by a series of calculations in SACMET. First, a representative income class is chosen. Their trip profiles are used to determine the number of trips that leave a zone (origins) and the number that come into a zone (destinations). The logsum of choice coefficient for each mode is then calculated. Each logsum is multiplied by the number of trips for that mode and then all logsum-trip products for all origin-destination pairs are summed. The result is a measure of accessibility for both origin and destination by zone. These accessibilities are fed into UPlan as a measure of attractiveness, where higher accessibility makes a zone more attractive for development.

In this application of UPlan, accessibility is the only measure of attractiveness because SACMET captures the conditions of the transportation network that are normally represented by the various roadway and transit attractiveness grids. For the allocation of residential development, UPlan uses the origin accessibility to determine if the zone is an area that people can travel from easily. To test whether a zone is attractive for non-residential land uses, UPlan uses destination accessibility to measure how easy it is to get to a business in that zone. When UPlan begins to allocate development, it looks to the most accessible zones first and then moves on to less accessible zones until is runs out of development to allocate in that period. As UPlan builds out the most attractive zones, SACMET calculates more trips in those zones and hence, a rise in congestion. This reduces the level of accessibility in those zones and causes UPlan to look to other, now more accessible zones, as suitable development locations. The models are run three times in sequence to get to the horizon year, 2025. C. UPlan After An Urban Economic Model

We have also used UPlan as a means of disaggregating the land use projections made by an integrated urban model, Tranus. In this application, we ran Tranus on our Sacramento region datasets and performed projections to the year 2020 for four transportation scenarios (Johnston and de la Barra 2000). We then ran UPlan on each of the 53 zones in the Tranus model, individually, to disaggregate the land uses spatially.

In an academic comparison, we also ran UPlan by itself on the Sacramento region and compared these land use maps with those from the exercise where we ran Tranus and disaggregated its zonal projections with UPlan. The maps compared favorably, that is, UPlan produced an urbanized layer that overlapped the Tranus-UPlan layer to a fairly high degree. Using the Lee-Sallee index (Is = (AB)/(AB)), we found over 60% agreement between Tranus-UPlan and the stand-alone version of UPlan. D. UPlan Linked to Urban Impact Models

We have developed rudimentary impact models, to demonstrate their use in the Sacramento region. All of these models were quite easy to implement in ArcView. 1. The Costs of Flooding model uses per-ha real estate assessed values for the six UPlan land use types and applies these to the ha of new development within the 100-year floodplain in each scenario being considered. The values are then factored down, since only buildings are damaged, not land. These values are factored down again, according to the percentage loss projected in the floodpool. Currently, we assume an average floodpool depth, but intend to refine this with actual depth data. 2. Costs from Wildfires are projected with a model developed by the California Department of Forestry and Fire Protection that runs in ArcInfo. A layer for Probability

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of Fire Occurance was created. Development patterns are evaluated by density and location. Loss of structures is translated into loss of property value.3. Local Service Costs are projected, using national data (Frank 1989) for the cost of each service type, by distance from current services. These data are applied to the ha of new development of each UPlan land use type, by distance from existing urban land uses. It would be more accurate to map all actual water and sewer main lines, but we do not have those data layers and they are not digital in most localities. We attempted to get local data on service costs for roads, sewer, water, police, and ambulance, but many agencies told us that costs were the same, regardless of distance from existing facilities. They seemed to be confusing (their presumably flat) service charges with (by necessity, variable) actual service costs, and so we used the national data.

4. The Erosion Impact model was based on factors used in the Universal Soil Loss Equation (USLE). These factors are soil erodibility, slope steepness, and precipitation. We obtained soil erodibility factor values from the STATSGO GIS database. Slope steepness was derived from a 30m DEM, then transformed into a slope steepness factor using a single, continuous function for slope steepness influence on soil loss (Nearing 1997). Annual average precipitation data came from the PRISM GIS database (Daly 1997) in a grid of approximately 1km cells; these data were then resampled to 50m grid cells using a cubic transformation. All data were converted into 50m grid cells. We then used ArcView Model Builder to combine these three factors using a multiplicative arithmetic overlay and used the result as a measure of erosion impact. 5. Habitat Damage. We created a score for wildlife habitat quality using the Ecosystem Management Decision Support program (EMDS; Reynolds 1999). The analysis looks at two main issues, wildlife habitat presence and habitat quality, to determine the score for wildlife habitat quality.

For the purpose of this analysis, we defined wildlife habitat presence as a combination of aquatic habitat integrity (distance to stream and aquatic diversity areas), wildlife occurrences (WHR richness and natural diversity database), and terrestrial habitat integrity (hardwoods, vernal pools, grasslands, wetlands, significant natural areas, and expert opinion). These data were converted into 50m grid cells and input into an EMDS knowledge base in this fashion.

EMDS links the GIS program ArcView 3.2 (ESRI 2000) with the knowledge base Netweaver (Saunders 1989; Saunders 1990). Netweaver is an object-based hierarchical network, with nodes based on fuzzy logical relationships. EMDS provides Netweaver with the necessary GIS base data for combining together using fuzzy logic rules to determine degree of truth for an assertion (Reynolds et al. 1996; Reynolds 1999). These assertions have “sub-assertions” (dependency networks) that are nested within the overall network. Eventually, these sub-assertions connect to base-data that address a single assertion. Data Needs Identified from Projects Completed

From our experiences to date, we conclude that satellite data are not accurate enough to be used to represent Existing Urban in the base year. Countywide parcel datasets are needed for both spatial accuracy and categorical accuracy. Satellite data can help to identify where buildings and pavement are on large rural parcels, however. California does not make parcel data available and so researchers and planners are subject to county-specific data policies. Most counties in California have parcel data, but

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many counties will not give it to researchers for free. Multispectral airphotos at 1m and 2m resolution are adequate to identify Existing Urban land use types, if interpreted for this purpose, as raster data. These data are available generally only for urbanized areas, not rural ones, unless countywide habitat planning is underway. Digital local land use plans are generally available now, although interpreting a large number of city and county plans into one coherent set of land use types can be time-consuming, especially if the documentation is poor.

VII. CONCLUSIONSWe believe UPlan is a reasonable starter model for MPOs, COGs, counties, and

state transportation agencies to use in modeling land use changes. It will project the footprint of development, which permits the analysis of several kinds of impacts. It will also allow the agency to iterate the land use model with their travel model, in 5- or 10-year steps. A simple rule-based model will not, however, give land market data, such as floorspace rents. Nor can such models represent redevelopment, except manually. It can be implemented in a month or two, if most or all data layers are available in GIS. If the agency wishes to progress on to a more sophisticated model type, the UPlan data and model will both be useful later for suballocation of growth projections, display of model outputs, and for other purposes. Current improvements being made are a better user interface, so all rule changes can be made without recoding, and more flexibility in user rules. Future improvements include coding in VBA for ArcGIS8.

ACKNOWLEDGEMENTSWe would like to thank USDOT, Caltrans, and USDA for funding the development of UPlan. Also, we thank the staff at SACOG for their assistance with data in our initial application.

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