地理学报 ›› 2022, Vol. 77 ›› Issue (10): 2650-2667.doi: 10.11821/dlxb202210015

• 研究进展 • 上一篇    下一篇

社会经济统计数据空间化研究进展

郭红翔1,2(), 朱文泉1,2()   

  1. 1.北京师范大学地理科学学部 遥感科学国家重点实验室,北京 100875
    2.北京师范大学地理科学学部 遥感科学与工程研究院 北京市陆表遥感数据产品工程技术研究中心,北京 100875
  • 收稿日期:2021-10-25 修回日期:2022-06-06 出版日期:2022-10-25 发布日期:2022-12-25
  • 通讯作者: 朱文泉(1975-), 男, 博士, 教授, 主要从事植被与生态遥感研究。E-mail: zhuwq75@bnu.edu.cn
  • 作者简介:郭红翔(1996-), 男, 博士生, 主要从事空间数据分析与应用研究。E-mail: 202131051035@mail.bnu.edu.cn
  • 基金资助:
    国家重点研发计划(2020YFA0608504)

A review on the spatial disaggregation of socioeconomic statistical data

GUO Hongxiang1,2(), ZHU Wenquan1,2()   

  1. 1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2021-10-25 Revised:2022-06-06 Published:2022-10-25 Online:2022-12-25
  • Supported by:
    National Key R&D Program(2020YFA0608504)

摘要:

社会经济统计数据通常是以各级行政区为单位的汇总数据,它虽然能反映统计单元之间的差异但却不能反映统计单元内部的异质性,在实际应用中,无法满足统计任意区域内的社会经济数据的需求,而社会经济统计数据空间化则是有效解决该问题的一条重要途径。本文对现有社会经济统计数据的空间化方法、社会经济统计数据空间化过程所依赖的辅助数据、现有主要的社会经济空间化数据产品进行了归纳总结,并从空间化方法的制约因素和改进方向、新型辅助数据的探索和多源辅助数据的综合利用、高时空分辨率和高精度数据产品研发3个方面展望了社会经济统计数据空间化的未来发展趋势。研究结果可为社会经济统计数据空间化方法的选择与改进、辅助数据的选择与综合利用、社会经济空间化数据产品的选择与改进提供参考。

关键词: 社会经济数据, 空间化, 人口, GDP, 数据产品

Abstract:

Socioeconomic statistical data is usually aggregated in units of administrative regions. The socioeconomic statistical data can reflect the heterogeneity between statistical units, but it cannot reflect the heterogeneity within a statistical unit. The socioeconomic statistical data cannot meet the needs of socioeconomic departments concerned in arbitrary regions. The spatial disaggregation of socioeconomic statistical data is an effective way to solve this problem. This study summarizes the existing methods of spatial disaggregation of socioeconomic statistical data, the auxiliary data used in methods for obtaining spatial disaggregation of socioeconomic statistical data, and the main socioeconomic grid data products. This study also predicts future development trends of the spatial disaggregation of socioeconomic statistical data in three aspects: the constraints and improvement directions of methods, the exploration of new auxiliary data and the comprehensive utilization of multi-source auxiliary data, the development of high temporal and spatial resolution and high-precision grid data products. The research results can provide references for the selection and improvement of spatial disaggregation methods of socioeconomic statistical data, the selection and comprehensive utilization of auxiliary data, and the selection and improvement of socioeconomic grid data products.

Key words: socioeconomic data, spatial disaggregation, population, GDP, data product