Poor people, or poor area? A geostatistical test for spatial poverty traps
MA Zhenbang;CHEN Xingpeng;JIA Zhuo;LV Peng;Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University;Research Institute for Circular Economy in Western China, College of Earth and Environmental Sciences, Lanzhou University;Institute for Studies in County Economy Development, Lanzhou University;Gansu Office of Poverty Alleviation and Development;
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, C_0) in the total variance(sill, C_0+ 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(P0.05) 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.