中国多维贫困的空间识别及空间分异机制研究

Developing methods for measuring multi-dimensional poverty and improving the accuracy of spatial poverty identification have been the hot topics in international poverty research for decades. Identification of poverty and analysis of spatial changes is the subject of science and still challenging governments and scholars. After opening and reform of China, nation and government starts large-scale poverty relief and development strategy in rural areas. China is a developing country with large population, meager heritage, uneven economic development and overall lagging in rural development. Thus one of the most important tasks is to eliminate poverty while Chinese economy increases continuously and society develops harmoniously. Without locating the real poor areas and groups in poverty, various poverty relief measures will lose their due effects. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrative units. As traditional statistical data of social economy is lack of the information of space, time-consuming to be collected, and objectivity hard to be guaranteed, they cannot meet the demand of large-scale, long-term and dynamic research in areas in poverty. So accurate assessments of regional poverty levels are essential for the central government and local policy makers to obtain reliable up-to-date data of the socio-economic situation and tackle regional inequality problems. In light of the academic thoughts of the vulnerability and sustainable livelihood analysis framework, this research will establish an index system and a method for geographical identification of multi-dimensional spatial poverty, and carries out grid-level and county-level identification in China using remote sensing satellite imagery and geographic information technology. This research plans to analyse the relationship between night-time light data and an integrated poverty index to explore the spatially irregular distribution of social wealth of China. Therefore, it will reveal the distribution of absolute poverty, the change of poverty status and evolution law of poverty reduction. Furthermore, this study will make a comparison between the identification result, income poverty and the latest designated poor regions by the Chinese government. The identified multi-dimensional poor counties will be classified by the similarity of poverty reduction measures. Finally, spatial pattern of poverty and its differentiation mechanism will be revealed using spatial analysis, and with the combination of GIS and BP neural network.

发展多维贫困空间度量方法和提高贫困识别精准度是近年国际贫困研究中的热点领域，也是中国未来提高区域扶贫实践质量和效率所面临的关键问题。拟借鉴脆弱性—可持续生计框架模型在贫困研究中的学术思想，建立多维贫困空间测度指标体系，利用遥感与GIS空间分析技术，整合海量的贫困统计数据与空间数据，构建多维贫困程度的测度模型，开展基于栅格和县域尺度的中国贫困地理识别，并与国家最新认定的扶贫开发重点县、连片特困区进行对比分析。拟采用GIS和BP神经网络，对空间贫困格局及其分异机制进行模拟分析。通过以上研究，揭示中国贫困空间格局及演变规律，完成中国贫困类型区划。通过本研究，从理论上建立一种多维贫困空间识别的定量研究框架和体系，在实践上力求创新一种贫困空间整体性认知与评价的框架与方法，并对我国多维贫困空间界定、空间格局及演变规律予以研究和总结归纳，为扶贫战略的制定和实施提供重要理论基础和客观依据。

发展多维贫困空间度量方法和提高贫困识别精准度是近年国际贫困研究中的热点领域，也是中国未来提高区域扶贫实践质量和效率所面临的关键问题。本项目借鉴脆弱性—可持续生计框架模型在贫困研究中的学术思想，建立多维贫困空间测度指标体系，利用遥感与GIS空间分析技术，整合海量的贫困统计数据与空间数据，构建了多维贫困程度的测度模型，开展了基于栅格和县域尺度的中国贫困地理识别，并与国家认定的扶贫开发重点县、连片特困区进行对比分析。采用GIS和BP神经网络，分别模拟了省域、市域和县域3个尺度下的自然致贫指数与社会经济消贫指数，计算获得了3个尺度下的贫困压力指数，对其空间分布格局进行分析。借助Flexible空间扫描探测识别出深度贫困县，运用地理探测器揭示其主导致贫因素，按照主导因素将深度贫困县划分为地形要素制约型、区位交通制约型、经济收入制约型和生态环境制约型4类，并提出差别化的减贫对策建议。通过以上研究，揭示了中国贫困空间格局及演变规律，完成了中国贫困类型区划。研究发现，自然因素是现阶段中国县域主要的致贫原因，全国县域自然致贫指数的分布呈现出明显随纬度和经度地带性分布的规律，自北而南、自西而东逐次呈带状排列分布。社会经济因素对贫困起到一定的缓解作用，全国县域社会经济消贫指数的空间分布较为破碎，各省区内部县域社会经济消贫指数的变异系数均大大高于自然致贫指数的变异系数。全国贫困压力指数以“黑河-百色”一线为界，东中西差异显著，呈现“大分散、小聚集”的空间分布格局。多维贫困县区在空间上表现为东、中、西部岛状、块状、连片状3种地域类型。通过本研究，从理论上建立了一种多维贫困空间识别的定量研究框架和体系，在实践上创新了一种贫困空间整体性认知与评价的框架与方法，并对我国多维贫困空间界定、空间格局及演变规律予以了研究和总结归纳，为扶贫战略的制定和实施提供了重要理论基础和客观依据。