New Approaches for Spatial Distribution Dynamics

这一研究项目将通过在测量和建模方面的方法学进步来提高对空间不平等动态的理解。理解空间不平等动态的本质对于基础社会科学和公共政策都是至关重要的，然而现有的方法和模型对这些空间动态提供了不完整的观点。虽然不平等研究的中心焦点一直是总收入分配的演变，但对不平等的空间格局和格局动态的关注要少得多。空间不平等可能对社会凝聚力、经济增长以及旨在降低不平等程度的政策的设计产生重要影响。该项目所取得的进展将具有广泛的适用性。除了空间收入不平等的动态，许多其他社会和经济现象的分布在空间和时间上演变。这一研究项目将为收入不平等的空间动态研究开发新的分析方法；具体地说，将开发新的方法来衡量这些动态分布特征的变化，纳入其空间依赖性和异质性。新办法将既包括报告空间动态的概要属性的全球衡量标准，也包括可用于确定作为整体动态的重要驱动因素或全球趋势的异常值的地点热点的地方指标。将对新措施的统计性质进行分析和模拟评估，并将进行涉及区域收入不平等的经验应用。这些新的分析将被合并到两个开放源码空间分析包的增强版本中：Python空间分析库和区域系统的时空分析。前者为希望开发定制应用程序的社会科学家提供了访问模块化程序库的权限，该程序库可以与现有软件结合使用，从而实现新的时空分析。后者是一个用户友好的分析和可视化程序包，可以促进对空间分布动态的探索性调查。

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.