云南省反贫困地理信息系统建设初探

A TENTATIVE EXPLORATION ON THE ESTABLISHMENT OF ANTIPOVERTY GEOGRAPHIC INFORMATION SYSTEM IN YUNNAN PROVINCE

本文从信息系统为决策支持服务的角度出发 ,以GIS、RS为手段 ,综合自然、经济、社会、环境等因素 ,多学科交叉 ,设计反贫困GIS的总体框架结构 ,研究和解决面向管理和决策的数据模型 ,实现致贫基本因素、贫困类型分布与贫困形势变化监测等方面的信息查询与辅助决策分析为一体的综合地理信息系统 ,为区域反贫困决策的各层次提供辅助分析与决策支持 。

Abstract: Poverty is a global issue which hinders social progress and human prosperity, therefore, shaking off poverty and attaining prosperity has become a common desire for mankind. Yunnan Province situated in the southwestern border of China has the most impoverished population of the country with complicated natural conditions and inhabited by 25 ethnic groups. Of the 129 cities and counties, 73 are state aid poverty-stricken counties, accounting for 57%. They mostly concentrate in mountainous and ethnic group inhabited areas. The antipoverty action started in the mid 1980s in China has gained historical achievement. The geographic information system (GIS) as a comprehensive integrated technique can be fully utilized in the aspects of ① describing and identifying spatial distribution law and dynamic change of poverty-stricken population; ② describing and identifying spatial distribution factors inducing impoverishment; ③ describing and analyzing spatial distribution of population and resources and their interactions; and ④ the follow-up management of the implementation of poverty-aid projects. Starting from the viewpoint that information system is a decision support service system, this paper, using GIS and remote sensing (RS) techniques, adopts multidisciplinary approach by combining natural, social, economic, and environmental factors to design a general framework for antipoverty GIS, studies and deals with management and decision-making oriented data model for the establishment of an integrated GIS covering aspects of information inquiry and supplementary decision analysis on poverty-induced basic factors distribution, poverty types distribution and poverty situation change monitoring so as to provide supplementary analysis and decision support for various levels of regional antipoverty policies as well as to provide scientific basis for antipoverty decision making.