A spatial analysis of disaggregated commuting data: Implications for excess commuting, jobs-housing balance, and accessibility (Indiana, Kentucky, Ohio)

In the standard analysis of jobs-housing balance and excess commuting, the analyst seeks a matching between supposedly homogeneous workers from a place of residence to a place of employment. Unfortunately, much of the analysis to date on commuting deals with total commuting flow, undifferentiated with respect to worker and job characteristics. Measures based on undifferentiated workers often produce misleading results because the assumption of worker homogeneity is violated. Motivated by the needs of differentiating worker types, this dissertation employs a benchmark spatial modeling approach to disaggregating journey-to-work data by type of workers.; The objectives of this dissertation are: (1) to develop a trip distribution model disaggregating journey-to-work data by type of occupation to predict average actual commutes; (2) to develop a disaggregated version of a linear program to measure theoretical minimum commutes; (3) to investigate accessibility and its changes by occupation; and (4) to assess multiple relocation policy scenarios considering intrazonal, inbound, and outbound commuting flows.; All models presented in this dissertation are applied to the tri-state area combining counties across Indiana, Kentucky, and Ohio over the ten-year period between 1990 and 2000. Empirical results verify the existence of variations in the levels of excess commuting, jobs-housing balance, and accessibility by type of occupation. Workers in each occupation react differently to relocation policy scenarios with varying preferences in terms of reduction in minimum commutes.; This dissertation explicitly addresses the disaggregation issue in terms of job and worker heterogeneity and provides a benchmark approach for incorporating such details into the analysis of commuting. The proposed benchmarking models are expected to have a wide range of applications in measurement and assessment of empirical patterns of commuting. The scope of the disaggregation can be extended to other targets such as different types of industry, household structure, income level, ethnic background, education level, transportation mode, and gender. Further dimensions of disaggregation can address spatial interactions of different socio-economic groups in urban areas, and more generally, contribute to exploring urban sprawl with respect to job characteristics and industries.