The development and dispersion of industries at the county scale in the United States, 1969--1996: An integration of geographic information systems (GIS), location quotient, and spatial statistics

The objective of this dissertation is two-fold. Firstly, the dissertation tests whether GIS can be integrated and/or how well it can be integrated with spatial-temporal analytical tools for conducting empirical regional economic analyses. Based on a loose-coupling of ArcView as the GIS module and Excel, Visual Basic, SPSS, and SAS as the spatial-temporal regional economic analysis modules, the dissertation develops a framework for integrating GIS with various analytical tools such as the location quotient (LQ) technique, Moran's I statistic, shift-share analysis, and Simpson's index.; Secondly, the dissertation operationalizes this integrated framework and applies it to the analysis of regional economic development. The dissertation traces and analyzes both spatial and temporal clustering patterns and spillover effects of economic changes and development across the 3,140 counties in the United States over the past three decades. In particular, the dissertation examines the impacts of regional industrial concentration and its change on regional economic risk, on regional economic performance, and on regional competitive advantage.; The dissertation shows that the application of this integrated framework to regional economic analysis has facilitated both the visual and statistical exploration of the spatial autocorrelation and spillover effects of a region's industrial concentration, changing industrial structure, competitive advantage, and economic diversity across space and over time. Moreover, the dissertation finds that the current commercially available GIS such as ArcView do not allow interactive modeling, sophisticated spatial and temporal statistical analysis, and dynamic visualizations across space and time.; The dissertation finds that the evolution of industrial distribution exhibits a statistically significant spatial clustering. Empirical evidence suggests that regional industrial concentration and its rate of change have significant impacts on a region's economic risk, on a region's economic performance, and on a region's competitive advantage in terms of a county's growth, competitive component, and industry mix. The implications of these findings can assist regional economic planners and decision-makers in prescribing guidelines and policies for regional economic development.