Using census block groups data on socio-demographics, land use, and travel behavior, we test the cutoffs suggested in the literature for trustworthy estimates and hypothesis testing statistics, and evaluate the efficacy of deleting observations as an approach to improving multivariate normality, in structural equation modeling. The results show that the measures of univariate and multivariate non-normalities will fall into the acceptable ranges for trustworthy maximum likelihood estimation after a few true outliers are deleted. We argue that pursuing a multivariate normal distribution by deleting observations should be balanced against loss of model power in the interpretation of the results.

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Urban models useful in transportation planning are reviewed, focusing especially on ones that are based on geographic information system (GIS) software. Then UPlan, a simple model written by the authors in the ArcView GIS, is described. Several different applications of UPlan are outlined, involving transportation planning and analysis of the growthinducing effects of new facilities, to demonstrate its use. Such models are coming into use for National Environmental Policy Act of 1969 assessments and for joint land use and transportation planning.

a b s t r a c tFuture air pollution emissions in the year 2030 were estimated for the San Joaquin Valley (SJV) in central California using a combined system of land use, mobile, off-road, stationary, area, and biogenic emissions models. Four scenarios were developed that use different assumptions about the density of development and level of investment in transportation infrastructure to accommodate the expected doubling of the SJV population in the next 20 years. Scenario 1 reflects current land-use patterns and infrastructure while scenario 2 encouraged compact urban footprints including redevelopment of existing urban centers and investments in transit. Scenario 3 allowed sprawling development in the SJV with reduced population density in existing urban centers and construction of all planned freeways. Scenario 4 followed currently adopted land use and transportation plans for the SJV. The air quality resulting from these urban development scenarios was evaluated using meteorology from a winter stagnation event that occurred on December 15th, 2000 to January 7th 2001. Predicted base-case PM2.5 mass concentrations within the region exceeded 35 mg m À3 over the 22-day episode. Compact growth reduced the PM2.5 concentrations by w1 mg m À3 relative to the base-case over most of the SJV with the exception of increases (w1 mg m À3 ) in urban centers driven by increased concentrations of elemental carbon (EC) and organic carbon (OC).Low-density development increased the PM2.5 concentrations by 1e4 mg m À3 over most of the region, with decreases (0.5e2 mg m À3 ) around urban areas. Population-weighted average PM2.5 concentrations were very similar for all development scenarios ranging between 16 and 17.4 mg m À3 . Exposure to primary PM components such as EC and OC increased 10e15% for high density development scenarios and decreased by 11e19% for low-density scenarios. Patterns for secondary PM components such as nitrate and ammonium ion were almost exactly reversed, with a 10% increase under low-density development and a 5% decrease under high density development. The increased human exposure to primary pollutants such as EC and OC could be predicted using a simplified analysis of population-weighted primary emissions. Regional planning agencies should develop thresholds of population-weighted primary emissions exposure to guide the development of growth plans. This metric will allow them to actively reduce the potential negative impacts of compact growth while preserving the benefits.

Using structural equation modeling, this study empirically examines the connections between job accessibility, workers per capita, income per capita, and autos per capita at the aggregate level with year 2000 census tract data in Sacramento County, CA. Under the specification of the conceptual model, the model implied covariance matrix exhibits a reasonably good fit to the observed covariance matrix. The direct and total effects are largely consistent with theory and/or with empirical observations across a variety of geographic contexts. It is demonstrated that structural equation modeling is a powerful tool for capturing the endogeneity among job accessibility, employment, income, and auto ownership.