New Approaches for Spatial Distribution Dynamics

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This research project will improve understanding of spatial inequality dynamics through methodological advances in measurement and modeling. Understanding the nature of spatial inequality dynamics is vital to both basic social science and to public policy, yet existing methods and models provide incomplete views of these spatial dynamics. While a central focus of inequality research has been on the evolution of the aggregate income distribution, much less attention has been directed at the spatial pattern of inequality and pattern dynamics. Spatial inequalities can have important implications for social cohesion, economic growth, and the design of policies targeted at reducing the level of inequality. The advances produced in the project will have wide applicability. In addition to spatial income inequality dynamics, many other social and economic phenomena have distributions that evolve in space and time. Software packages will be delivered as open-source projects and accompanied with extensive tutorials and documentation to facilitate broad dissemination across the social sciences.This research project will develop new analytical methods for the study of spatial dynamics of income inequality; specifically, new approaches will be developed to measuring changes in the distributional characteristics of those dynamics that incorporate their spatial dependence and heterogeneity. The new approaches will include both global measures that report summary properties of the spatial dynamics as well as local indicators that can be used to identify hot-spots of locations that are important drivers of the overall dynamics or are outliers from the global trends. Analytical and simulation based evaluations of the statistical properties of the new measures will be conducted, and empirical applications involving regional income inequalities will be carried out. These new analytics will be incorporated into enhanced versions of two open-source spatial analysis packages: Python Spatial Analysis Library and Space-Time Analysis of Regional Systems. The former provides social scientists who wish to develop custom applications with access to a modular library that can be used in conjunction with existing software to enable the new space-time analytics. The latter is a user-friendly analytical and visualization package that can facilitate exploratory investigation of spatial distribution dynamics.