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人穷还是地穷?空间贫困陷阱的地统计学检验

1. 1. 兰州大学资源环境学院西部环境教育部重点实验室,兰州 730000
2. 兰州大学资源环境学院中国西部循环经济研究中心,兰州 730000
3. 兰州大学县域经济发展研究院,兰州 730000
4. 甘肃省扶贫开发办公室,兰州 730000
• 收稿日期:2018-04-16 出版日期:2018-10-20 发布日期:2018-11-20
• 作者简介:

作者简介：马振邦（1983- ）,男,甘肃会宁人,讲师,研究方向为景观地理与区域可持续发展。E-mail: zbma@lzu.edu.cn

• 基金资助:
国家自然科学基金项目（41401204,41471462）;中央高校基本科研业务费项目（lzujbky-2013-128）

Poor people, or poor area? A geostatistical test for spatial poverty traps

Zhenbang MA1,2,3(), Xingpeng CHEN1,2, Zhuo JIA1,2, Peng LV4

1. 1. Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2. Research Institute for Circular Economy in Western China, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
3. Institute for Studies in County Economy Development, Lanzhou University, Lanzhou 730000, China
4. Gansu Office of Poverty Alleviation and Development, Lanzhou 730000, China
• Received:2018-04-16 Online:2018-10-20 Published:2018-11-20
• About author:

Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

*Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

E-mail: dengwei@imde.ac.cn

Abstract:

The test for spatial poverty traps (SPTs) is a hot issue in the field of the geography of rural poverty. However, the main existing approaches cannot provide spatial scale-related information, which may be a restriction on gaining a deeper understanding of the mechanism of SPTs. Therefore, we conducted a case study in the Liupan Mountain Region by introducing geostatistical methods. The semivariogram and cross-correlogram were employed to quantitatively describe the spatial pattern of village-level poverty and its relationship with the selected geographical factors respectively, so that the scale-dependent spatial form and underlying reasons for SPTs can be explored. The village-level poor population (PP) and poverty rate (PR) were used as the poverty indicators. The results show that the geostatistical methods can provide satisfactory and reliable performance in the test for SPTs: (1) The semivariogram models can indicate both the spatial structure and the autocorrelation range of the two indicators, which can describe the extent and the range of the spatial form of SPTs (i.e. the spatial aggregation of poverty). The percentages of the random variance (nugget, C0) in the total variance (sill, C0 + C) are 34.4% and 11.5% for PP and PR, respectively. The range of autocorrelation is 9.3 km for PR, and 5 and 48 km for PP. (2) The cross-correlograms further show that the two indicators are significantly (P<0.05) correlated with the geographical factors within different spatial ranges. Generally, the poverty status of a village is mainly in response to three factors (i.e. the distance to the nearest county town, the elevation, and the total population) within a wide range. In conclusion, the evidence of SPTs from our work is consistent with the reality that the study area has suffered persistent poverty in the past three decades.