Case Study:

Envision Utah

Methodology

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:

Determine the components of existing demand and assess how that demand is currently being met; and
Determine the anticipated components of future demand and identify barriers that may prevent that demand from being met.

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.

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