Integrating Data Across Misaligned Spatial Units
Integrating Data Across Misaligned Spatial Units
复制标题
DOI：
10.1017/pan.2023.5
复制
发表时间：
2023-03-23
影响因子：
5.4
通讯作者：
Kollman, Ken
中科院分区：
文献类型：
--
作者：
Zhukov, Yuri M.;Byers, Jason S.;Kollman, Ken
感兴趣的理论单位通常与可用数据的空间单位不一致。这个问题在政治学中普遍存在，特别是在地方实证研究中，需要整合不兼容的地理单元（例如行政区域、选区和网格单元）的数据。克服这一挑战不仅需要研究人员调整经验和理论单位的规模，还要了解这种对测量误差和统计推断的支持变化的后果。我们展示了变换值的准确性和回归系数的估计如何取决于嵌套程度（即，单元是否完全且整齐地彼此内部）以及源单元和目标单元的相对规模（即聚合、分解和混合）。我们引入了简单的、非参数的相对嵌套和尺度测量，作为空间变换复杂性和误差敏感性的事前指标。使用选举数据和蒙特卡罗模拟，我们表明这些措施可以强有力地预测多种支持变更方法的转型质量。我们提出了多种验证程序并提供开源软件，以使转换选项更易于访问、可定制和直观。
Theoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, as ex ante indicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive.