Technique and methods for spatialization of socioeconomic data

Abstract

This paper firstly summarizes the research progress of the spatialization about regional statistic data. It is concerned with problems arising when a region is divided into different sets of zones for different purposes, and data available for one set of zones are needed for a different set. The areal interpolation is usually used to solve this problem of statistic data. In the study, we take Beijing Chao Yang District as study area (source zone), and we successfully apply three methods to translate the industrial output value from the administrative zones of Chao Yang (source zones) to regular zones of 1km grid lattice (target zones), including areal weighting; point-in-polygon and raster representation based on zone centroid locations.It shows that the spatialization result can express the spatial characteristic of socioeconomic assets more accurately and objectively.