ABSTRACT

This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogeneity by adopting geographically weighted principal component analysis at a fine spatial resolution. Moreover, it focuses on dependence by introducing a novel approach based on spatial filtering. These methods are applied in order to derive a composite indicator of socioeconomic deprivation in the Italian province of Rome while considering two spatial scales: municipalities and localities. The results show that considering spatial information uncovers a range of issues, including neighbourhood effects, which are useful in order to improve local policies.

摘要

本文研究了空间依赖性，空间异质性和空间规模在地理分布数据的主成分分析中的作用。它通过在精细的空间分辨率下采用地理加权主成分分析来考虑空间异质性。此外，它通过引入一种基于空间滤波的新颖方法来专注于依赖性。应用这些方法是为了得出意大利罗马省社会经济匮乏的综合指标，同时考虑两个空间尺度：市政和地方。结果表明，考虑空间信息会发现一系列问题，包括邻里效应，这对于改善当地政策很有用。