地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (1): 58-74.doi: 10.12082/dqxxkx.2021.200628

• 地球信息科学综述 • 上一篇    下一篇

时空统计学在贫困研究中的应用及展望

葛咏1,*(), 刘梦晓1,2, 胡姗1,2, 任周鹏1   

  1. 1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2.中国科学院大学,北京 100049
  • 收稿日期:2020-10-21 修回日期:2020-12-17 出版日期:2021-01-25 发布日期:2021-03-25
  • 通讯作者: 葛咏
  • 作者简介:葛 咏(1972— ),女,新疆奎屯人,博士,研究员,主要从事地理时空统计方法研究。E-mail: gey@lreis.ac.cn
  • 基金资助:
    国家杰出青年基金项目(41725006)

The Application and Prospect of Spatiotemporal Statistics in Poverty Research

GE Yong1,*(), LIU Mengxiao1,2, HU Shan1,2, REN Zhoupeng1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-10-21 Revised:2020-12-17 Online:2021-01-25 Published:2021-03-25
  • Contact: GE Yong
  • Supported by:
    The National Science Fund for DistinguishedYoung Scholars "Geospatial statistical methods"(41725006)

摘要:

消除贫困是人类社会的共同目标。贫困分布具有明显的空间特征,同时呈现出空间异质性和空间相关性。时空统计学以时空分析为优势,在贫困的时空分布及形成机制研究中发挥了重要作用。本文综述了不同时期我国贫困分布的空间特征、贫困数据的空间类型和特征以及贫困时空分布的影响因素,并总结了时空统计学方法在贫困空间研究中的4类应用,包括:探索性空间数据分析,主要为了识别和量化分析贫困的时空分布格局;空间贫困归因分析,通过构建贫困和各地理要素之间的关系模型来分析贫困的影响因素;贫困制图,通过采样区数据得到整个区域的贫困分布;以及贫困的时空变化分析,揭示贫困要素的时空变化过程和其背后的驱动因素。在阐述这些方法的原理基础上,选取了具体案例阐释了时空统计学方法如何应用于空间贫困研究,进一步总结了时空统计学方法在贫困研究中的主要应用和价值;在此基础上,分析了目前空间贫困研究的不足,提出重点应从4个方面拓展时空统计学方法在目前空间贫困领域的研究。

关键词: 贫困, 时空数据, 时空统计, 空间自相关, 空间异质性, 空间贫困陷阱, 时空格局, 时空变化

Abstract:

Eliminating poverty is a common goal of human society. Poverty has the characteristics of spatial heterogeneity and spatial autocorrelation. Spatiotemporal statistical methods dealing with georeferenced or spatiotemporal data have been widely employed for analyzing spatiotemporal poverty data. This paper reviews the applications of spatiotemporal statistical methods in spatiotemporal poverty analysis and classifies the applications into four categories: (1) exploratory analysis of poverty, mainly to identify and quantitatively analyze the spatiotemporal distribution pattern of poverty; (2) identification of spatial determinants of poverty, to analyze the influencing factors of poverty by constructing a model of the relationship between poverty and various geographical elements; (3) spatial mapping of poverty, to obtain the distribution of poverty in the entire region using sampling data; and (4) spatiotemporal analysis of poverty, to reveal the spatiotemporal changes of poverty and their driving factors. On the basis of explaining the principles of these methods, we give examples of recent applications to illustrate how specific spatiotemporal statistical methods are applied to spatial poverty research. On this basis, the shortcomings of current spatiotemporal poverty research and potential development on future poverty research are also summarized.

Key words: poverty, spatiotemporal data, spatiotemporal statistics, spatial autocorrelation, spatial heterogeneity, spatial poverty traps, spatiotemporal pattern, spatiotemporal change