Population Place And Spatial Interaction PDF Download

Abstract: The objectives of this dissertation are: (1) to develop a commuting spatial interaction model incorporating various spatial structure variables, such as Competing Destinations (CD) and Intervening Opportunities (IO) factors with Tobit regression; (2) to develop optimization planning applications with both linear and non-linear programming utilizing the empirically estimated Tobit models with the goal of minimizing total commuting costs, and (3) to assess multiple regional scenarios combining alternative land development strategies and zonal population density constraints. All the models are applied to the Fredericksburg Area (FAMPO Region) combining the counties of Stafford, Spotsylvania, King George, and Caroline, and the city of Fredericksburg in the State of Virginia. Census Transportation Planning Package (CTPP) 2000 data and Auditor's property data are used. The empirical results support considering the spatial structure of the region in the modeling process. The independent variable of this research, commuting flow, is a censored variable and seems appropriate to be handled with Tobit model. Four different spatial interaction commuting models have been estimated using Tobit regression: (1) Model 1 with basic variables of the gravity model; (2) Model 2 with two more additional variables, which represent spatial structure. Those are intervening opportunities (IO) and competing destinations (CD) variables; (3) Model 3 with further additional socio-economic variables; and (4) Model 4 extends Model 3 by adding quadratic terms. These four models are estimated by Tobit regression, and the results point to improvements by adding spatial structure variables and additional socio-economic variables and the quadratic functional form demonstrates the best goodness-of-fit statistics. To measure the impacts of independent variables on the dependent variable, two further analyses are conducted: (1) elasticity; (2) marginal effect analysis. It is shown that marginal effect of each independent variable on the dependent variable varies depending on other variables in the quadratic model. Linear and non-linear planning optimization models are formalized and implemented using Fredericksburg Area Metropolitan Planning Organization (FAMPO) area data. The models spatially allocate population and employment to minimize total commuting costs. The results demonstrate that density constraints are critical in determining the distribution of population and employment. For a given land development strategy, the highest possible densities allow for minimizing commuting costs. However, commuting costs do not represent all urban and regional development costs. Thus, the results are extended to account for land development costs and congestion costs, leading to a comprehensive analysis of the implications of different density and land development strategies. The results demonstrate that the optimal development densities are very sensitive to the congestion cost function. With increase of congestion level, a land development strategy allowing for sprawl to some extent could be a smart policy to reduce regional vehicle mile travel (VMT) and consequently total pollution level as well.