Modeling spatial distribution of land use taking into account spatial autocorrelation

Abstract

Land use drivers that best describe land use patterns quantitatively are often selected through regression analysis. A problem using conventional statistical methods in spatial land use analysis is that these methods assume the data to be statistically independent while spatial land use data have the tendency to be dependent, known as spatial autocorrelation. Two different scales of study area, Fujian Province and Longhai county are selected. In this paper, Moran's I is used to describe spatial autocorrelation of dependent and independent variables and spatial autoregressive models which incorporate both regression and spatial autocorrelation are constructed. 5 main land use types in Fujian Province, 9 main land use types in Longhai county and all candidate land use driving factors show positive spatial autocorrelation. The occurrence of spatial autocorrelation is highly dependent on the aggregation level. Results also show that spatial autoregressive models yield residuals without spatial autocorrelation and have a better goodness-of-fit. The spatial autoregressive model is statistically sound in the presence of spatially dependent data in contrast with the standard linear model.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.