A geographic information system (GIS) framework is appealing to model supply-side decisions because spatial relationships commonly used by developers to evaluate building sites, such as the proximity to transportation facilities, existing land uses, political boundaries, and environmentally sensitive areas, are defined precisely in the GIS layers. The GIS captures spatial synergisms that are lost in tabulations by traffic zone or larger forecasting districts. Further, the results are defined for individual parcels (grids). This method interfaces directly with the concerns of residents and other interest groups. Uncertainty and error in postmodel allocations from zones to parcels in existing land use models can significantly blur and degrade the relevance of forecasts made with existing models. The development patterns predicted by UPlan, a planning model, tend to be realistic and provide a basis for land use planning and evaluation. A GIS land use survey, supplemented with simulation model networks and census data, was used to calibrate the model. The calibrated UPlan model did a reasonably accurate job of allocating the various categories of land uses to predefined composite growth areas. The generalized UPlan model is applicable in a wide variety of rural, suburban, and urban settings. The model, as presented, was configured as a travel simulation integrated land use planning tool, but the method also can be used as the supply-side component within a comprehensive land use modeling framework.

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.

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.

The Sacramento, California region is engaged in an innovative long-range visioning process during 2004 and 2005, where the regional transportation planning agency is defining and modeling several 50-year growth scenarios. We worked with environmental and social equity citizens groups to define policies that would reduce emissions, serve lower-income travelers better, and preserve habitats and agricultural lands in the region. The citizens groups rejected the new freeways planned for the region, as well as the substantial freeway widenings for HOV lanes. In addition, they defined a more-ambitious transit system, involving new Bus Rapid Transit lines and shorter headways for all rail and bus service. This transit-only plan was modeled, by itself, and along with a land use policy for an urban growth boundary and a pricing policy for higher fuel taxes and parking charges for worktrips. We used a new version of the MEPLAN model to simulate these scenarios over 50 years and describe our findings regarding total travel, mode shares, congestion, emissions, land use changes, and economic welfare of travelers.

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.

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.

We are developing a statewide urban growth model for California. It will be run in iteration with the California statewide travel model to evaluate major transportation improvement scenarios, such as freeway widenings and high speed rail. In addition, we will evaluate land use policies intended to provide for more affordable housing and for habitat protection. This model is uniquely capable of providing performance measures for total travel, travel delay, mode shares, economic development, wages, economic welfare, economic equity, rents paid by households and firms, energy use in vehicles and buildings, greenhouse gas emissions, vehicular air pollution, loss of agricultural lands, habitat loss, and erosion potential. We propose a framework for interpreting all of these data, based on recent advances in the theory of well-being for persons and for nations.

Exurbia (rural low-density residential development) is one of the fastest growing types of land-use and can result in habitat fragmentation and loss of farmland. Local zoning restrictions and farmland protection are the most common ways of controlling low-density development in rural areas. While planners have recognized the utility of landuse change models for decision-making, most models do not effectively forecast exurban expansion. To rectify this problem, a spatially explicit model called UPlan that projects exurban development was adapted for Sonoma County California, where an estimated 27% of the people live at low densities (< 1 unit/0.8 ha [2 acres]). The projected pattern and extent of development resulting from three alternate agricultural land protection policies were compared, and the likely impact on natural areas and farmland was assessed. The results reveal that if current farmland is not protected from exurban development, 73% of all Sonoma County’s remaining core forest could be comprised of edge habitat (within 500m of development) by 2010, and as much as 12% of existing farmland will be developed. We demonstrate that some farmland protection policies can have unintended consequences for forest conservation due to increases in exurban residential development. This research represents a real-world application of a new model that can assist planners to assess the impact of zoning and subdivision controls on land conservation where exurban expansion is a concern.