The study of economic inequality and convergence continues to attract attention becoming a dynamic academic landscape where an interdisciplinary literature has evolved. This interest has been reflected in the spatial and temporal thinking in this research domain, that is, the analysis of spatial patterns of economic convergence and the temporal dynamics of geographical inequality. However, the literatures of process analysis (time series analysis) and form analysis (spatial pattern analysis) are largely isolated from one another. Consequently, the integration of these two rich and growing literatures offers opportunities for a truly spatially integrated social science. Hence, the aims of this project are two-fold: first, to develop new space-time measurements and inferential approaches to compare regional economic structure and interactions; second, to further the understanding of the role spatial dependence plays in economic growth. The objective of this project is the cross-fertilization of distributional dynamics and spatial pattern analysis using geometric indicators. This research will apply these new methods to compare the Chinese space-time economic structure with that of the United States. In addition, these methods will be implemented in the Open Source software package STARS: Space-Time Analysis of Regional Systems that facilitates the study of space-time economic process.
The increasing availability of space-time data has outpaced the development of space-time analytical techniques across social sciences. The integration of space and time would generate much closer interactions among geography and other social sciences in general, while providing new perspectives for the role of geography in economic development. Comparative analysis of spatial economic growth will help to narrow the gap between growth theories and their empirical testing. This will better our understanding of the role of space in different regional economies. From a policy perspective, the development of space-time explicit indicators will provide policy makers and urban planners with new tools to design and evaluate poverty eradication programs by identifying space-time clusters of poor regions. While the substantive focus of this research is on regional income dynamics, the methodological issues examined are relevant to the study of a wide class of phenomena that have spatial and temporal dimensions. The developed methods are expected to have implications in areas such as comparative space-time dynamics of land use evolution, disease diffusion, crime hot spots, socioeconomic inequalities, among others.
