Case Study:

Envision Utah

Conclusions

Limitations

Overall Approach, GIS, And Land Use

The Envision Utah analysis was admittedly an extremely ambitious undertaking. Not all areas will have the resources to undertake such an extensive or coordinated effort.
The GIS analysis relied upon a substantial effort to collect land use and zoning data from numerous local jurisdictions. While this resulted in broadly useful databases, problems of updating the databases are not yet solved. Further technical and institutional barriers must be overcome before local land use and zoning data can be easily combined into a common database and updated on a regular basis.
A qualitative, consensus-based land use forecasting approach has the advantage that it incorporates considerable locally developed expert knowledge. By its nature, however, this type of approach not explicitly account for feedback from transportation investments into development patterns. Application of the transportation-land use model UrbanSim will help to formally incorporate considerations such land prices and transportation accessibility into predictions of future land use patterns.

Transportation And Air Quality

The transportation models' ability to predict the full range of responses to alternative land use and transportation alternatives remains limited. Scenarios C and D in particular represent development patterns that are not in general use in the area. The travel survey data used to calibrate the models are therefore limited in the information they provide on travel behavior associated with these non-typical development patterns. Future enhancements to travel surveys and models in the Greater Wasatch region would help improve the sensitivity of the travel models to transit strategies, urban design variables, and land use changes.
QMOD does not account for the atmospheric chemical reactions that contribute to the accumulation of reactive pollutants such as PM and ozone. Therefore, the concentration of ambient air pollution can be inferred but not simulated.
The air quality metrics showed only small differences among scenarios. DAQ staff felt that this was largely a result of relatively small differences in the distribution of population and employment (totals were fixed by county). If scenarios were developed that differed more significantly, the metrics might also differ more and be more useful.

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.

[TOP]