Doctoral Dissertation Research: Comparative Regional Income Dynamics: Clustering, Scale, and Geocomputation

区域经济增长和不平等之间的关系很难确定。为了真正揭示区域收入动态中涉及的机制，研究人员必须超越相关性的概念，解决更复杂的方向性和同时性问题。收入趋同和不平等所涉及的空间机制直到最近才开始在文献中引起关注。这一趋势的一部分可以归因于空间统计和计量经济学的进步，这有助于分散人们对经验数据中与空间效应有关的几个推论陷阱的了解。经济增长和不平等对空间的关注在应用方面更加明显，因为有证据表明，贫困陷阱和趋同俱乐部等有趣的社会经济结构。这个博士论文研究项目将使用一些不同的研究方法来研究这些多方面的现象。该项目将概述一个探索性地理可视化框架，该框架用于在存在空间依赖性的情况下对区域增长和不平等之间的功能关系产生新的见解。将提供一种正式的空间计量经济学方法，探讨区域内空间集聚和同时在区域收入演变中的作用。该项目还将在相对分配框架内审查区域经济变化，以便放松经常作为收入数据分析基础的假设。蒙特卡罗技术将被用来评估受不同空间关联度影响的相对分布的性质。将采用几种技术来控制空间效应，从而对区域收入动态进行分解和稳健的展望。这项研究将有助于提高人们对空间在经济系统动态中的重要性的认识。将使用几种新的在空间上明确的工具，进一步发展地理和区域科学在分析经济增长和变化方面的相关性。相对分布方法的空间扩展可应用于研究面状数据的任何分布比较。在人口学领域，这项研究可以揭示不同地理尺度上种族/民族构成的空间差异。参考分布和比较分布之间的关系可以被视为类似于实验设计中的控制组和处理组。因此，来自许多社会科学领域的研究人员可以利用这个框架来分析跨空间边界的各种政策的有效性。最后，本项目中设计的方法将纳入开放源码地球计算工具包STARS(区域系统的时空分析)，该工具包将继续为更广泛的研究界提供替代的动态空间分析工具。最后，这一博士论文研究改进奖将使有前途的研究生能够从事独立研究生涯的第一个重大项目。

The relationship between regional economic growth and inequality is difficult to ascertain. In order to truly uncover the mechanisms involved in regional income dynamics, researchers must move past notions of correlation to address the more intricate issues of directionality and simultaneity. The spatial mechanisms involved in income convergence and inequality have only recently begun to take focus in the literature. A portion of this trend can be attributed to advances in spatial statistics and econometrics, which have helped to diffuse the knowledge of several inferential pitfalls associated with spatial effects in empirical data. The focus on space in economic growth and inequality is further apparent on an applied front, as evidence points to interesting socioeconomic constructs such as poverty traps and convergence clubs. This doctoral dissertation research project will examine these multifaceted phenomena using a number of different research approaches. The project will outline an exploratory geovisualization framework that is used to generate new insights into the functional relationship between regional growth and inequality in the presence of spatial dependence. A formal spatial econometric approach will be provided that addresses the role of intraregional spatial clustering and simultaneity in the evolution of regional incomes. This project also will examine regional economic change in a relative distribution framework in order to relax the assumptions often underlying the analysis if income data. Monte Carlo techniques will be used to evaluate the properties of the relative distribution subject to varying levels of spatial association. Several techniques will be employed to control for spatial effects thereby allowing for a decomposed and robust outlook on regional income dynamics.This research will contribute to the increased awareness of the importance of space in the dynamics of economic systems. Several new spatially explicit tools will be employed that further develop the relevance of geography and regional science in the analysis of economic growth and change. The spatial extensions to the relative distribution methods can be applied to the study of any distributional comparisons of areal data. In the field of demographics, this research could shed light into to spatial variations in race/ethnicity composition at various geographical scales. The relationship between the reference and comparison distribution can be seen as analogous to the control and treatment groups in an experimental design. As such, researchers from many social science fields could utilize this framework to analyze the effectiveness of various policies across spatial boundaries. Lastly, the methods designed in this project will be integrated in the Open Source geocomputational toolkit STARS (Space-Time Analysis of Regional Systems), which continues to provide the broader research community with alternative dynamic spatial analytic tools. Finally, this Doctoral Dissertation Research Improvement award will enable a promising graduate student to pursue the first major project of an independent research career.