Paper Info
Title
Land Cover Pattern Simulation Using An Eigenvector Spatial Filtering Method In Hubei Province
Abstract
This paper proposes an eigenvector spatial filtering-based (ESF-based) regression model for land cover pattern simulation in China's Hubei province. The significance and influence of biophysical, climatic, and socio-economic factors have been detected and analyzed in the study region. The ESF-based multinomial logistic regression (spatial model) is constructed for discrete choices to take spatial autocorrelation into consideration. For the massive raster pixels, a segmentation processing (grid-based partition) approach is employed to resolve the large datasets to smaller ones to improve calculation efficiency. Both 32 x 32 and 64 x 64 cell sizes are used to compare the differences and influence of these approaches. For the 32 x 32 cell size, the hitting ratio increased from 0.70 to 0.89 and the deviance decreased 65.6%. For the 64 x 64 cell size, the hitting ratio increased from 0.68 to 0.77 and the deviance decreased 33.2%. The fitted results and maps show that spatial autocorrelation (SA) plays an important role in land cover patterns. Besides, the ESF-based spatial model can isolate SA in land cover pattern simulation, and therefore can improve the fitting accuracy and decrease the model uncertainty. The experiment shows that ESF-based multinomial logistic regression method provides a promising approach for discrete choice regression for massive raster datasets.
Year
DOI
Venue
2020
10.1007/s12145-020-00483-4
EARTH SCIENCE INFORMATICS
Keywords
DocType
Volume
Land cover pattern simulation, Eigenvector spatial filtering (ESF), Spatial autocorrelation, Environmental determinants
Journal
13
Issue
ISSN
Citations 
4
1865-0473
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jiaxin Yang1488.07
Yumin Chen211317.11
John P. Wilson36911.71
Huangyuan Tan400.68
Jiping Cao501.01
Zhiqiang Xu601.01