The study of regional economic growth, inequality, divergence and convergence attracts considerable interest across multiple social sciences. By definition, these analyses rely on data that are spatially referenced. Only a few very recent studies, however, have given attention to the role of spatial dependence and spatial heterogeneity in the empirical analysis of regional economic evolutions. Research in the fields of geographical information systems (GIS), spatial statistics, and spatial econometrics has generated new methods designed to treat these spatial effects, but these methods do not address the dynamic dimensions of regional economic change. A truly integrated social science requires a toolkit that integrates both the spatial and temporal dimensions of socioeconomic phenomena. The objectives of this research project are to develop such a toolkit by (1) examining the implications of spatial clustering and spatial heterogeneity for the application of exploratory data analysis (EDA) techniques in a dynamic context; (2) developing new statistical methods for exploratory space-time data analysis (ESTDA); and (3) implementing these methods in an Open Source package for exploratory space-time analysis of social processes. The methods to be used include exploratory spatial data analysis (ESDA), exploratory temporal data analysis (ETDA), Monte Carlo simulation studies of the empirical properties of the new ESTDA methods, object-oriented programming, and dynamic geovisualization.
This project will make significant contributions to the practice of spatial social science, the modeling of human dynamics, and to basic understanding of the nature of regional growth and inequality. The incorporation of space and time into models of regional inequality and growth will provide more comprehensive and accurate descriptions of human social and economic behavior. The project will develop an exploratory space-time toolkit that will be Open Source and accessible to a broad array of social science researchers to enhance the analysis of human and social dynamics. As such, this research is expected to have implications in areas such as studies of urban segregation patterns, space-time epidemiology and public health, criminology, housing market dynamics, socioeconomic inequalities, among others. From a policy perspective, the development of new spatially explicit measures will provide planners and analysts with capabilities to design policy interventions targeted at key individual geographical areas. By taking spatial spillovers into account, this spatially focused strategy will leverage the impact of such policy programs across the boundaries of a single area, thereby increasing the effectiveness of the policy. This project is supported by an award resulting from the FY 2004 NSF-wide competition on Human and Social Dynamics (HSD). Coordinated management of the HSD competition and the portfolio of HSD awards involves all NSF directorates and offices.
