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Toolbox for Regional Policy Analysis Report (2000)

Case Study: Envision Utah


Salt Lake City, Utah Aerial View

This case study highlights a series of methods to analyze the land use, transportation, air quality, water use, and infrastructure cost implications of alternative regional transportation and land use scenarios. The methods can be applied individually or in combination, and vary in their ease of application.

This case study highlights a series of methods to analyze the land use, transportation, air quality, water use, and infrastructure cost implications of alternative regional transportation and land use scenarios. The Greater Wasatch Region of northern Utah is a 10-county area containing three urban areas, including the greater Salt Lake City metropolitan area. The region has recently been experiencing high growth, which has created strains on transportation infrastructure, water supply, and the natural environment. Development in the region is of increasing concern not only to the government agencies that must support it, but to the public that experiences its effects. Since 1996, growth has consistently ranked in public surveys of Utahans as the single most important issue facing the state.

Reflecting this concern, a public-private partnership known as Envision Utah was initiated in 1996 to study the effects of long-term growth in the region and to propose strategies to address growth-related issues. After three years of analysis and public discussion, the Envision Utah process resulted in a set of recommended actions to achieve an overall "Quality Growth Strategy." Development of the Quality Growth Strategy was supported by an extensive technical analysis of alternative transportation and land development scenarios. The Envision Utah analysis showed that in 2020, compared to the baseline, the Quality Growth Strategy will conserve 171 square miles of land; include a more market-driven mix of housing (by modifying some restrictive zoning regulations); result in a 7.3 percent reduction in mobile source emissions; include less traffic congestion; and require $4.5 billion less investment in transportation, water, sewer, and utility infrastructure.

The Envision Utah technical analysis was conducted through a collaborative effort among state, regional, and local agencies. The methods highlighted here include:

The case study also describes the land use scenario development process; revisions to the regional travel model to estimate non-motorized travel; a water supply and demand model to estimate the water use impacts of different development patterns; and a housing demand analysis to ensure that the Quality Growth Strategy's housing mix is consistent with the demands of the housing market.


Growth in Utah

When a random sample of Utahans were asked in an open-ended question in January 2000 what the most important issue facing Utah today is, 27 percent, more than any other topic, identified growth. Growth has held this top position in 16 of the past 17 quarterly surveys asking this same question (Utah, 2000). Figure 1 shows recent and projected population and employment growth in the Wasatch Front Region, which is the metropolitan region in which Salt Lake City is located.

Figure 1. Population and Employment Projections, Wasatch Front Region

Fig. 1 Population and Employment Projections, Wasatch Front Region

Source: Utah Governor's Office of Planning and Budget, 2000.

Beginning in 1995, the state government initiated explicit and formal efforts to address the growth concerns of the public. The Growth Summit, a jointly sponsored effort of the Governor and Legislature, was held in December of 1995. Over 60 proposals suggesting ways to manage the state's growth were submitted. The Summit resulted in a 10-year transportation improvement plan. Based on this plan, the state is now in the process of rebuilding previously unfunded core highway infrastructure in the state. The Summit also resulted in development by the state of growth-related data sources and technical analysis methods, to support the evaluation of alternative growth scenarios.

Envision Utah Process

In 1996, the state partnered with Envision Utah, a public/private community partnership dedicated to studying the effects of long-term growth in the 10-county Greater Wasatch Area of northern Utah. Envision Utah's purpose is to create and be an advocate for a publicly supported growth strategy that will preserve Utah's high quality of life, natural environment, and economic vitality. Membership includes over 100 partners from the business, academic, conservation, local and state government, and religious communities.

The Envision Utah partnership then initiated a process, known as the Envision Utah analysis, to define alternative regional scenarios and to analyze the consequences and impacts of the alternatives. The first phase of the overall Envision Utah analysis - development of the "quality growth" strategy - took place over a period of two and one-half years, from spring 1997 through fall 1999. The activities during this phase are shown in Figure 3. During this period, the Envision Utah partnership directed many activities, including an in-depth values study, baseline analysis, more than 100 public workshops, scenario development and analysis, and a million-dollar public awareness campaign. The resulting Quality Growth Strategy (QGS) includes recommended transportation investments, zoning changes, land preservation policies, and water conservation incentives.

Figure 2. Envision Utah: Phase 1 Process

Fig. 2 Envision Utah: Phase 1 Process

The second phase of the Envision Utah process, beginning in the fall of 1999, will be the implementation of the "quality growth" choice. Through a multi-year implementation plan, Envision Utah will promote the preferred strategy at appropriate levels throughout the region. In addition, all public and private entities will be encouraged to voluntarily make planning decisions consistent with the vision of the quality growth scenario. Envision Utah (1999) identifies a set of goals and strategies to achieve the Quality Growth Scenario, who will be responsible, and how these strategies will be implemented.

The Envision Utah phase 1 analysis was a three-year, multimillion-dollar effort. Funding for the analysis has come from a variety of local, state, federal, and private sources. A grant from FHWA's Transportation and Community and System Preservation Pilot Program (TCSP) has in part funded the development and application of modeling tools for this analysis.

State Technical Assistance

The Envision Utah partnership has its own staff and operates mostly with private funds and no direct state financing, but much of its technical work has been prepared by a state/local technical committee coordinated by the Governor's Office of Planning and Budget. The state's technical support to the Envision Utah partnership began in 1996 and is known as QGET (Quality Growth Efficiency Tools). The focus of QGET is to enhance technical modeling tools, data, and processes, so that decision-makers have information related to air quality, transportation, water, and land use that is comprehensive, reliable, accessible, and consistent. The QGET Technical Committee consists of technical representatives from state and local government and the private sector. The Governor's Office of Planning and Budget coordinates QGET's work.

After its formation, QGET compiled an inventory of planning models in Utah. The committee then developed or assisted with the development of models to improve analysis of air pollution, infrastructure costs, auto ownership, and land use. Working with its local government and private sector partners, QGET then analyzed the transportation, air quality, land use, water use, and infrastructure cost impacts of alternative futures. This analysis has taken the form of a projected baseline future (the future based on existing plans and trends); four alternative futures, designed to delineate a spectrum of choices; and, finally, the recommended Quality Growth Strategy. The QGET technical analyses benefited from the input of 88 cities, 10 counties, two metropolitan planning organizations, five state agencies, PSOMAS Engineering, and Fregonese Calthorpe Associates.

QGET provides technical support not only to Envision Utah, but to state agencies conducting demographic and economic forecasting, transportation systems planning, air quality analysis, water supply planning, land use data management, affordable housing programs, and community development. QGET's data and analysis products are available free of charge to jurisdictions that can use them. Their products have included:

QGET has resulted in a coordinated effort that simultaneously provides technical support to Envision Utah and other planning efforts, as well as improving the longer-term capabilities of the state to formally model and understand growth.


Overall Technical Approach

The various planning models applied in the Envision Utah analysis, and their logical interconnections, are illustrated in Figure 3.

Figure 3. Interconnection of Planning Models

Fig 3. Interconnection of Planning Models

Source: Based on Utah Governor's Office of Planning and Budget (1999).

The land use analysis served as the foundation for the remaining planning models, including transportation, air quality, water supply/demand, and infrastructure cost assessment. The land use analysis allocated regional population and employment to a grid of 50.3-meter-square cells, consistent with different development characteristics and constraints under each scenario. The household and employment data, along with a walk/bike mode split obtained from the calculation of an Urban Index for each cell, were aggregated to Traffic Analysis Zones (TAZs) for transportation and air quality analysis. Land consumption and agricultural conversion also informed both the land use and water use models.

Feedback between transportation and land use was not explicitly modeled, although the scenarios did include different transportation and land use scenarios in combination that were designed to be complementary. The Envision Utah project is currently working to develop and apply UrbanSim, a land use model that interacts with transportation models, to the Greater Wasatch area.

Two rounds of technical analysis were conducted: one for the analysis of the baseline and three alternative scenarios, and one for the comparison of the baseline and Quality Growth scenarios. The methods used were largely similar, but in some cases were refined for the Quality Growth analysis. Relevant differences in technical approaches are noted below.

Envision Utah also commissioned a housing demand study to ensure that the Quality Growth Strategy's housing mix is consistent with the demands of the housing market. The study examined current development trends, constraints on the real estate market such as zoning regulations, and how changes in consumer preferences and regional demographics will affect housing demand in 2020.

The land use analysis, GIS application, air quality model, and infrastructure cost analysis are the primary focus of this case study. The transportation modeling approach, water supply/demand analysis, and housing demand analysis are described more briefly. The scenarios are described to provide background and context.

Scenario Development

Envision Utah and QGET produced a baseline scenario (1997), three alternative scenarios (1998), and an analysis of the Quality Growth Strategy (2000). All of the scenarios utilize the same regional population and employment projections. The baseline (Scenario B) and three alternative scenarios (A, C, and D) include the following basic characteristics:

The "quality growth" scenario, resulting from the feedback obtained on the initial four scenarios and additional strategy development, is most similar to Scenario C as described above, but is still a unique combination of land use and infrastructure investment. Scenario C is depicted pictorially in Figure 4, and the regional concept map corresponding to this scenario is shown in Figure 5. One significant difference is that in the Quality Growth scenario, county-level employment did not differ appreciably from the baseline scenario. In contrast, in Scenarios A, C, and D, employment totals were constrained at the regional level but not at the county level. In the Quality Growth scenario, only the distribution within the county and the nature of development varied significantly. The assumption was that the voluntary nature of the growth strategy would not change the population and economic growth of individual counties.

Figure 4. Growth Scenario C

Fig. 4 Growth Scenario C

Figure 5. Regional Concept Map for Scenario C

Fig. 5 Regional Concept Map for Scenario C

Land Use

The land use analysis was performed by QGET technical staff based on GIS data, economic and demographic forecasts, local plans, and input from local officials and the public. The analysis considered both the design (allocation of population and employment) and the characteristics (amount of infill/redevelopment, total land consumed, population and employment density, housing types, and proximity measures) of the baseline and three alternative scenarios. Rather than simply shifting population and employment among zones, the land use analysis was conducted through an extensive process in which development by type and characteristic was assigned to 50.3-meter grid cells within the GIS environment. A number of land use-related layers were used, in conjunction with local plans and feedback from local officials and the public, to develop a realistic allocation of future development under different scenarios. Despite the detailed spatial scale of the analysis, its purpose was not to identify specific areas that would be developed, but rather to determine a realistic allocation of future development in order to measure overall regional impacts for each scenario.

The land use analysis included the following steps:

  1. Incorporation of input from regional design workshops, including the amount and density of development by type under each scenario, as well as realistic expectations for future development by geographic area;
  2. Development of the "constrained and developed" land mask, to identify undevelopable and developable land;
  3. Allocation of new development. Increments of various densities/types were added to developable land, consistent with control totals and the characteristics of each scenario; and
  4. Evaluation of the land use characteristics of each of the scenarios. Land use measures included total land consumption, level of infill/redevelopment, densities, housing types, population with access to rail transit, and loss of agricultural lands.

To create the constrained and developed land mask, lands that were considered unbuildable due to environmental or safety concerns were removed, as were existing developed lands. These were merged to form a composite mask of land not available for development. The remaining available land was then used to accommodate development based on the parameters of the four scenarios. Unavailable land could be considered available for future development if it met the criteria for potential redevelopment and infill development, which varied for each scenario. The process for developing the land use mask and allocating development in each of the scenarios is described in more detail in Utah (1999).

Once future land development by type was allocated spatially, the incremental growth was combined with the existing developed grid to calculate total developed land area. The final product became a virtual city in a grid structure, containing households, employment and other information distributed across the landscape of the Greater Wasatch area in different patterns.

The land use analysis was conducted by Fregonese Calthorpe and Associates and the State of Utah's Automated Geographic Reference Center.

Raster GIS Environment

The key to the land use analysis was the use of Spatial Analyst, a raster-based module of ArcView GIS software. It can be thought of as an extensive, geographically referenced spreadsheet with rows and columns of cells containing data corresponding to the layer. The raster environment allows multiple data layers to be quickly combined into composites, making it an efficient tool for large-scale modeling. The modeling utilizes a 50.3-meter (156-foot square) grid cell for the analysis, so that one square mile contains exactly 1,024 cells. Given the level of analysis in the first phase of Envision Utah, this provided sufficient resolution for the comparative models.

The gridding of data into a common raster environment provided the foundation of the Envision Utah scenario analysis and also facilitated other components of the analysis. Analysts with Utah's Automated Geographic Reference Center (AGRC) felt that while some of the analysis could have been performed with GIS data in a vector environment (polygons), it was more efficient in a raster environment. Logistically, converting polygon to grid data is a relatively simple process, but it requires a knowledge of how to work with the data. For example, census tract population data must be converted to densities, which are then assigned to associated raster cells and converted back to population. According to AGRC, working with GIS data to the extent undertaken in the Envision Utah analysis requires a "sophisticated and trained GIS technician."

A GIS raster environment has also been applied by the State of Oregon for statewide land use forecasting, and in the analysis of regional scenarios in a number of European cities (see SPARTACUS case study).

Air Quality

Modeling Approach

The air quality analysis was conducted by the Utah Department of Environmental Quality, Division of Air Quality (DAQ). DAQ considered the use of a regional airshed model to model ambient air quality conditions under each scenario. DAQ determined that this class of models was not a practical alternative for the Envision Utah analysis, however, because of the long lead time necessary to prepare the necessary data inputs. Instead, DAQ developed a modeling system which is highly simplified in some respects, yet captures some of the meteorological parameters involved in regional airshed models.

The resulting model, QMOD, is a simplified air quality planning model that tracks the movement of pollutants but does not model chemical reactions (for example, ozone formation). DAQ has been using a GIS to support its regional airshed modeling since 1994. The idea for QMOD resulted from the strong integration developed between the GIS and two important components of the regional airshed model. The first component is a model that simulates wind speed and direction over the area on an hourly basis. The second component is the capability to spatially disaggregate emissions from county-wide totals to an overlay of a regularly spaced (four-kilometer) grid. The essence of QMOD is to track the movement of pollutants, by grid cell and hour, during some hypothetical day in the future and then compare the air quality implications for each development scenario against the baseline scenario. The relationship between QMOD and the underlying GIS data is shown in Figure 6.

Figure 6. Integration of GIS and QMOD Regional Airshed Modeling

Fig. 6 Integration of GIS and QMOD Regional Airshed Modeling

Source: Based on Utah Governors Office of Planning and Budget (1999).

The process for applying QMOD is as follows:

  1. Develop the emissions inventory from mobile, point, and area sources;
  2. Assign the emissions to four-kilometer grid cells;
  3. Use GIS to track hourly emissions based on windfield analysis; and
  4. Tabulate the results.

In addition to differences in mobile source emissions, the various scenarios in the Envision Utah analysis also differed to some extent in point and area sources. While large emitters - such as power plants and major industries - were held fixed, the location of small emitters was allowed to vary in proportion to the amount of industrial development. Area sources dependent upon population (for example, lawn and garden equipment) were also distributed in proportion to population.

Data Requirements

Data available to DAQ to estimate the air quality implications of each growth scenario included:

During the model run an hourly activity adjustment, based on VMT profiles obtained from UDOT, was made at the beginning of each hourly iteration to characterize a more true-to-life temporal profile of emissions. The resulting hourly pollutant concentrations by four-kilometer grid cell were measured for total ozone precursors, the sum of volatile organic compounds (VOC) and oxides of nitrogen (NOx); carbon monoxide (CO); and particulate matter (PM).

Impact Measures

Typically, estimates of pollution concentration derived from airshed models are compared to national health standards to develop regulatory policy. Since QMOD estimates the spatial distribution of emissions which contribute to the concentration of air pollution, rather than the concentration of pollution in the ambient air, the evaluation of a scenario's effect on air quality cannot be directly measured against the national health standards for air pollution.

As an alternative, the Envision Utah analysis produced a matrix of three evaluation criteria for individual pollutants for each scenario. The performance ranking of each scenario became an ordinal measure of that scenario's effect on air quality. The three metrics include:

  1. Total emissions inventory;
  2. Inequality of distribution; and
  3. Coincidence of population and pollution.

The first metric is simply the sum of emissions across the entire region. The second metric quantifies the localization of build-up of pollutants. This was developed by ordering the four-kilometer grid cells from lowest to pollutant emissions density, then plotting the cumulative percentage of total land area against the cumulative percentage of pollutants (when plotted graphically, this produces a "Lorenz curve"). For example, in Scenario A, 58 percent of the average hourly emissions occur in 10 percent of the geographic region. In Scenario D the share of pollution increases to 66 percent of the emissions in 10 percent of the region. The underlying hypothesis is that a greater geographic concentration of pollutants is more likely to lead to an exceedance of standards in some particular area.

The third evaluation metric compares the proximity of people to air pollution. Metric 3 was created by overlaying a grid of population density onto a grid of average daily emissions after the air quality model was run. Population density by cell was then multiplied by the daily average emissions density, and the values summed across all cells. The higher the final value of this metric, the greater the likelihood that high population density and high pollution density occur in the same proximity.

Level of Effort

QMOD was developed in-house by DAQ. According to DAQ, the model took about three person-months to develop, although this included "lots of talking" to figure out what to do. A similar model could be developed in less time by another air quality agency or MPO assuming they have staff experienced in air quality modeling and also have the required GIS data and skills. DAQ is also potentially willing to provide the QMOD algorithms to others who may be interested. For comparison, DAQ believes that applying state-of-the-science Model 3 Airshed Model, which is capable of modeling ozone and particulate formation, would have required 10 to 100 times more effort.

Infrastructure Costs


The purpose of the infrastructure cost assessment was to provide reasonable approximations of the infrastructure costs associated with new residential development at the scale of the Greater Wasatch Area. The infrastructure cost analysis was conducted by the Utah Governor's Office of Planning and Budget, which developed the Infrastructure Cost Assessment Model in collaboration with Psomas Engineering and the Utah Division of Water Resources. Project sponsors note that there are various uncertainties associated with the cost estimates. The estimates provided by this tool are not considered to be exact predictions of infrastructure costs, but instead are reasonable estimates that provide a relative understanding of how land use effects public investment in infrastructure.

Three levels of infrastructure costs were identified:

  1. Regional infrastructure, including regional roads, transit, and water supply facilities. These projects are planned by regional or state governments and financed by state and or federal funds.
  2. Subregional (off-site) infrastructure, maintained by the municipality or service district. This includes water and waste water treatment facilities along with distribution lines, storm drain lines and basins, and minor arterial roads. These projects are financed by local governments through the sale of bonds, levying of impact fees, or use of tax revenues.
  3. On-site infrastructure provided by developers. On-site infrastructure is classified into the categories of roads, water transmission lines, sewer transmission lines, dry utilities (telephone, electric, etc.), and storm drains. Private developers generally finance the bulk of on-site infrastructure and reclaim their money through the sale of improved lots.

Separate methods were developed to assess infrastructure costs at each of these levels. For off-site and on-site infrastructure, a two-step procedure was followed: first, cost estimates were produced on a per-unit basis by type and density of development; and second, total regional costs were estimated from per-unit costs, according to development characteristics under each scenario.

Regional Infrastructure

The road and transit networks and their associated costs under each scenario were developed by the Wasatch Front Regional Council and Mountainland Association of Governments. Transportation modeling results for the various scenarios indicated that much of the same road network will be required as for the baseline scenario, but total costs will be lower because some road expansion projects are not needed. Transit costs are greater under Scenarios C, D, and the Quality Growth Strategy, although the cost-effectiveness of this service is also greater since population is clustered around transit hubs.

The water supply facilities needed and the cost of these facilities under each scenario were developed by the Division of Water Resources, in cooperation with various municipal water agencies. Savings in water demand from the Quality Growth Strategy were substantial enough to delay some parts of the baseline regional water projects, thus reducing the regional component of water supply costs.

Off-Site Infrastructure Costs per Unit

Per unit estimates were prepared by Psomas Engineering through a collaborative effort with the Division of Water Resources and the Governor's Office of Planning and Budget. Experts working in local, regional and state government also provided input into the development of these estimates. The bulk of the information used in estimating municipal costs came from municipal impact fee studies of selected municipals and special districts, in conjunction with an analysis sponsored by the Utah League of Cities and Towns on the compliance of municipals with the Utah Impact Fee Act. Based on this information and on professional judgment, the median impact fees for roads, water, sewer and storm drains were estimated.

Infill and reuse developments benefit in that much of the needed off-site infrastructure in the form of roads and storm drains are already in place. Psomas Engineering found that municipal roads and storm drains make up 20 to 23 percent of the total impact fees levied. Based on these findings it was recommended that the median impact fees be reduced by 20 percent for infill and 15 percent for reuse. The end result of this process was the schedule of off-site cost by density and land use shown in Table 1. These costs were estimated from local data in the Greater Wasatch region of Utah and are not necessarily applicable to other parts of the country.

Table 1. Off-Site Cost Schedule in Envision Utah Study1

Dwelling Units per Gross Acre

Raw Land





















1Off-site costs include water and waste water treatment facilities, distribution lines, storm drain lines and basins, and minor arterial roads maintained by the municipality or service district.

Work is planned in 2000 to develop an engineering cost model for off-site costs, similar to the on-site cost model. The purpose is to develop a model that can estimate costs for specific development proposals and locations, rather than just considering average regional characteristics by development density and type.

On-Site Infrastructure Costs per Unit

On-site infrastructure costs proved to be the most difficult and complex level of costs to estimate on a per-unit basis. The complexity and difficulty is due to the multiple ways subdivisions can be designed based on size, available land, and the negotiating that takes place between developers and municipalities. To simplify these and other variables, a simulation model was designed by Psomas Engineering to predict a mean cost estimate per unit. The model has the ability to create estimates by density and land type.

The simulation model was prepared based on actual estimates and sketch designs. These estimates and sketches are composed of detailed information of the infrastructure that is necessary to prepare a parcel of land for residential development based on conformity to Salt Lake City building code. The simulation model uses input data for parcel size and density, and then calculates a standardized lot size (net lot size, depth, and width), the relationship of the lots within the parcel (rows, tiers, block length, and street width), and the quantity of materials required (square feet of roads and linear feet of water, sanitary, storm drain, dry utilities). Cost coefficients per quantity of material are then applied. The on-site cost model was developed for the most stringent local development standards in the region (Salt Lake City), but was found to be 98 percent accurate when applied to other site plans.

Because this model is built out of mathematical relationships, it is possible to insert a variety of densities into the model and receive varied per unit costs by density. Four models in total were developed for different land uses. An index model serves as the base model; a raw land model accounts for peripheral roads that are general to raw land development; the reuse model accounts for demolition costs and a parcel size of 10 acres; and the infill model is based on a parcel size of five acres. The resulting on-site cost schedule is shown in Table 2. Once again, these costs were estimated from local data in the Greater Wasatch region of Utah and are not necessarily applicable to other parts of the country.

Table 2. On-Site Cost Schedule in Envision Utah Study1

Dwelling Units per Gross Acre

Raw Land





















1On-site costs include roads, water transmission lines, sewer transmission lines, dry utilities, and storm drains provided by developer.

Off-Site and On-Site Infrastructure Costs by Scenario

To estimate total off-site and on-site infrastructure costs for each scenario, the GIS land use data contained in the 50.3-meter grid cell format were utilized. Each cell contains a value that indicates the number of units located within that cell. The baseline was prepared by taking current land use and populating cells to represent the baseline population controls. Average household size was used to match population with the number of new units.

The Quality Growth Strategy represents development as occurring through eight broad development types. Each type is defined by specific land use characteristics such as residential area, density, jobs, and population. GIS cells received a code representing one of these eight development types. The cost model used the GIS to count up cells based on development and type, to derive the number of new housing units by density and type of land use (raw, infill, reuse). Reuse is defined as cells with new population in 2020 in addition to existing population as of 1997, while infill development is defined as cells surrounded by cells that were heavily developed as of 1997. Raw land is all development occurring on the peripheral of existing 1997 population.

The GIS exercise resulted in around 30,000 cell aggregations of like cells next to one another for the baseline and about 50,000 for the Quality Growth Strategy. Piecewise log linear mathematical functions were fit to the on- and off-site cost schedules. Clusters were then sorted by land use and by density, and were assigned to functions based on density and type.

The on-site infrastructure cost model continues to be improved. It was refined between the analysis of the four scenarios and the analysis of the Quality Growth Strategy, and will continue to be refined in future work.

Level of Effort

The development of the off-site and on-site infrastructure cost functions involved roughly two-thirds of a staff person's time for over a year, as well as roughly one person-week each for a water quality and wastewater engineer. Costs for an outside engineering firm included roughly $10,000 for the municipal (off-site) analysis and $20,000 for development of the on-site cost analysis models. The on-site model went through a number of iterations before staff were satisfied with its accuracy.

To apply the cost models to the land use data for each scenario, two approaches were possible. The first was to code the cost functions in GIS, which took roughly two to three person-days to write, check, and run the script and analyze results. The second approach was to use GIS to do a quick aggregation of the land use data (e.g., land development by type and density), and then apply cost functions in an Excel spreadsheet. This approach turned out to be quicker, taking roughly a half day.

Other Analysis Components

Transportation Modeling

The analysis process used the available travel models for the three urbanized areas in the Greater Wasatch region. The models were combined together to allow for analysis of the travel between areas and to report area-wide indicators. Any feedback from transportation to land use was assumed to be addressed in the development of the alternatives. For the area outside that covered by travel models, simplified factors (such as VMT per capita) were used to estimate some values.

An additional procedure was applied to estimate non-motorized trips. The bike/walk trip estimate was based on the Portland Metro bike/walk model generated from the 1995 Metro Activity Survey. Non-motorized trips could not be estimated from the most current Salt Lake Regional Household Travel Survey because the amount of information collected on these types of trips was too small to be statistically significant. This adjustment procedure was developed by Fregonese Calthorpe Associates, in consultation with Portland Metro.

The Portland Metro Travel Model estimates the number of trips taken from and to each travel analysis zone by bicycling or walking depending on urban design characteristics, distance of the trip, and number of cars per household versus workers per household. The urban design characteristics are defined by intersections per acre (a measure of street connectivity) and a mixed-use index. The mixed-use index is based on population and employment within a one-half to one-mile radius of the station. The index measures both the "balance" between jobs and households and the density of uses. A TAZ on the edge of the urban area which is balanced in terms of jobs and housing but has a low density would score poorly on this index, since the distance between uses is not conducive to walking or bicycling.

The Quality Growth Strategy analysis differed from the scenario analysis in that it included more detailed transit networks. The more detailed networks allowed the system to be optimized; in particular, direct service was provided to the areas that the QGS designated for higher density and walkable developments. The QGS analysis also estimated walk and bike usage based on a more detailed evaluation of the characteristics of the developed area, as opposed to the average for traffic analysis zones that include developed and undeveloped area. This was done by identifying the portion of the TAZ that was developed, and then allocating the entire population and employment of the TAZ to the grid cells within the developed area.

Water Supply/Demand

The Wasatch Front Water Demand/Supply Model (WFWDSM) was developed and applied to the four alternative scenarios. A GIS approach used in the model provides individual demand estimates for water use (i.e., residential, commercial, industrial, etc.) and water supply within each service zone of the 66 water service entities in the four Wasatch Front counties.

The WFWDSM uses maps that interact with water demand functions for water-use categories to calculate water demand for any future time period. The desired year and population projections are input into the model and the corresponding demand is calculated. Residential demands are calculated as a function of persons per household, lot size, assessed value of the property, soil type, and season of the year. Industrial and commercial demands are calculated as a function of a parameter (usually employment) which is projected for groups of industries identified by the Standard Industrial Classification (SIC) codes.

Indoor water use was held constant for all scenarios. The major differences among scenarios in the water demand analysis came from outdoor watering, which is affected primarily by lot size and related green area. Consumption of water by agricultural uses also differed, depending upon how much agricultural land was converted to urban land. Higher-density scenarios generally have lower water use, as a result of less outdoor watering, but this is partially offset by the fact that more agricultural lands remain in production.

Housing Demand Analysis

A key input to developing the final Quality Growth Strategy was a regional housing demand analysis. The analysis was conducted in mid-1999 by ECO Northwest, an economics firm based in Oregon, and Free & Associates, a Utah appraisal firm.

The main objectives of the study were to:

The analysis led to the development of two different simulations of the distribution of housing in 2020: a baseline simulation based on the continuation of trends in the 1990s, and an alternative simulation that reflects expectations about the way housing demand will shift in response to projected demographic shifts in the Greater Wasatch Area. In both simulations, an average of almost 20,000 housing units per year are needed between now and 2020 to keep up with the forecasted growth in households. In the baseline simulation, over 70 percent of new housing is single-family. In the alternative simulation, the single-family share drops to about 60 percent, with a corresponding increase in the multi-family share; and the number of smaller lot (less than 5,000 square foot) single-family units increases by an average of about 500 units per year.

The more detailed breakdowns of housing type by county provided a market driven check on the assumptions used to allocate population to different development types in the Quality Growth Strategy. The conclusion of those working on the development of the Quality Growth Strategy was that its allocations are consistent with the alternative simulation of housing types in the housing demand analysis. The identified barriers to meeting future demand included cultural perspectives, misperceptions of abundant land resources, lack of consistent growth, lack of education regarding sustainable planning practices, land ownership patterns, and development industry restraints.

Level of Effort

The Envision Utah technical analysis was a multimillion-dollar effort conducted over a period of roughly three years. QGET estimates that state agencies provided approximately 50,000 person-hours of time for technical support, of which 20,000 were to develop the baseline scenario and 30,000 were to develop and analyze alternatives and the quality growth strategy. The other major component of Envision Utah - not included in this time estimate - was the process and public outreach, which also involved a significant amount of work, including visits with 99 local governments and several hundred public meetings.

Obtaining a more precise estimate of the staff time, cost, or resource requirements of the Envision Utah analysis or its various components is difficult. The analysis was a combination of staff time at state and local agencies and contracts with consultants, including some activities that would have been conducted normally. Funding came from a variety of sources. Also, some elements of the analysis relied heavily on other elements; for example, the establishment of data in a Raster environment facilitated the infrastructure cost, water supply/demand, transportation, and air quality analyses.

While Envision Utah was conducted as an integrated study, elements of the approach could be extracted and applied individually or in combination. Also, while the Envision Utah process involved an extensive public outreach and involvement effort, the technical methods could be applied separately. Resource requirements for some of the individual modeling elements are described above in their respective sections.


Table 3 summarizes the various modeling results for the four scenarios. This table has been presented on Envision Utah's web site, in conjunction with maps and a basic description of each scenario, to help the public compare the four scenarios.

The impacts of the Quality Growth Strategy were determined using similar methods, in some cases with refinements. These impacts were then compared to the impacts of the baseline scenario. Some of the highlights of the findings include:

Figure 7. Transportation Impacts

Fig. 7 Transportation Impacts

Source: Quality Growth Efficiency Tools Technical Committee, Utah Governor's Office of Planning and Budget (2000).

Figure 8. Transit Impacts

Fig. 8 Transit Impacts

Source: Quality Growth Efficiency Tools Technical Committee, Utah Governor's Office of Planning and Budget (2000).

Figure 9. Infrastructure Cost Impacts

Fig. 9 Infrastructure Cost Impacts

Source: Quality Growth Efficiency Tools Technical Committee, Utah Governor's Office of Planning and Budget (2000).

Table 4. Air Quality Evaluation Metrics


CO PM VOC + NOx Total
Emissions, tons per day






Quality Growth





Concentration Measure Average*






Quality Growth





Population * Emissions Average*






Quality Growth





*Lower is better.

A more extensive set of impacts for the Quality Growth Strategy versus the baseline scenario is shown in Table 5.

As a result of this process, Envision Utah identified a number of strategies, classified by goal area, to achieve the Quality Growth Strategy. Examples include promoting mixed-use and walkable development and neighborhood zoning; promoting telework; and encouraging reversible lanes where feasible. For each strategy, the Envision Utah also identified why it is important, who is responsible for it, and the actions required to accomplish the strategy.



Overall Approach

The Envision Utah technical analysis illustrates methods for testing a range of impacts of regional transportation and land use scenarios. In one sense, the analysis is integrated as it relies on a common underlying GIS data structure and land use scenario analysis. For areas that are not ready to undertake an effort of the same scale, however, individual model elements could be adopted depending upon locally available data and assessment needs.

The land use, transportation, air quality, and water supply/demand assessments were conducted through a collaborative process among a number of state and local agencies. The Envision Utah analysis shows the benefits of developing a common set of tools and databases. In addition, beyond the technical analysis components, Envision Utah illustrates the value of an extensive outreach campaign to educate and obtain feedback from local officials, developers, and the public. This campaign has resulted in the framing of realistic scenarios and the development of strategies that are potentially feasible both from a political and a market standpoint.

GIS Fundamentals

GIS data management and analysis techniques greatly facilitated the Envision Utah analysis. In particular:

Land Use Analysis

Transportation And Air Quality

Infrastructure And Water


Overall Approach, GIS, And Land Use

Transportation And Air Quality

It is interesting to note that the air quality concentration and exposure indices were slightly worse for scenarios in which total emissions decreased, as a result of greater coincidence of population with emissions. Some limitations of the metrics must be noted, however. First, the 'inequality of distribution' measure (which measures the spatial concentration of pollution) does not tell anything about whether the cells with the highest levels of pollutants are also coincident with the highest levels of population. Second, the inequality measure is relative rather than absolute. A higher concentration index may not reflect actual higher exposure levels, if total emissions are lower.


Published References

Envision Utah (1999). Envision Utah Quality Growth Strategy. Salt Lake City, Utah, November 1999. (Available in PDF format from Envision Utah web site.)

Utah Governors Office of Planning and Budget (1999). Scenario Analysis: Tools for Analysis. Quality Growth Efficiency Tools Technical Committee. (Available in PDF format from Envision Utah web site.)

Utah Governors Office of Planning and Budget (2000). Strategy Analysis. Quality Growth Efficiency Tools Technical Committee, draft, April 2000. (Available in PDF format from Envision Utah web site.)


Organization Person Phone
Envision Utah Natalie Gochnour Technical Director 801-538-1544

Internet Sites

Envision Utah:

Governor's Office of Planning and Budget:

Quality Growth Efficiency Tools:

Updated: 10/20/2015
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