Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant...

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Bibliographic Details
Published in:Environmental modeling & assessment 2007-02, Vol.12 (1), p.55-61
Main Authors: Huang, Qiu-Hao, Cai, Yun-Long, Peng, Jian
Format: Article
Language:English
Subjects:
Agricultural industry
Agricultural land
Agriculture
Case studies
Dependent variables
Economic factors
Environmental changes
farmland spatial pattern model
GDP
GIS
Gross Domestic Product
Independent variables
Karst
Land use
land use pattern
Maotiao River Basin
Mathematical models
Mountain regions
multiple logistic regression
Per capita
Population density
Regression analysis
River basins
Rivers
Simulation
Software
Spatial distribution
Studies
Sustainable development
Variables
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Summary:Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social-economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.
ISSN:1420-2026
1573-2967
DOI:10.1007/s10666-006-9052-8