PROGRESS IN GEOGRAPHY ›› 2006, Vol. 25 ›› Issue (3): 79-85.doi: 10.11820/dlkxjz.2006.03.010

• Original Articles • Previous Articles     Next Articles

Spatial Simulation Using GIS and Ar tificial Neur al Network for Regional Pover ty —A Case Study of Maotiaohe Water shed, Guizhou Province

XU Yueqing1,2, LI Shuangcheng2, CAI Yunlong2   

  1. 1. College of Resources and Environment, China Agricultural University, Beijing 100094, China|
    2. College of Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2006-01-01 Revised:2006-04-01 Online:2006-05-25 Published:2006-05-25


Owing to the fragile eco- environment, terrain fragmentation, and serious soil erosion, the karst area in Southwest China is one of the distinct poor regions. Selecting Maotiaohe watershed as study area, taking villages and towns as studying unit, and using GIS and ANN model, this paper simulates the spatial distribution of natural impoverishing index and socio- economic alleviating impoverishing index, calculates the poverty degree of villages and towns, and reveals the spatial distribution of poverty in order to provide scientific basis for eliminating poverty and ecological reconstruction. The results show that the natural factors such as soil erosion and so on are the main impoverishing indexes, and socio- economic factors are the main alleviating impoverishing indexes. The villages and towns with smaller poverty degree are mainly distributed in the middle and east area of Maotiaohe watershed, and those with larger poverty degree are mainly distributed in the southern and northern area of Maotiaohe watershed. The results also indicate that application of BP neural network to simulating regional poverty is convenient, precise and feasible, which can be an alternative approach to simulating regional poverty.

Key words: artificial neural network, regional poverty, spatial simulation

CLC Number: 

  • N945.1