Model for Effect of Spatial Weighted Matrix on Spatial Autocorrelation
Spatial weight matrix quantificationally explains the spatial relationships between the geographic features, which is an important tool in the analysis of spatial statistics. In this paper, three concepts, i.e., adjacency relationships, distance relationships and comprehensive factors relationships are summarized to characterize the definitions of spatial weight matrix. By selecting four different spatial weight matrixes, we analyze the spatial agglomeration phenomena of flooded area in China. The results show that flooded area in China presents positive spatial autocorrelation. In addition, this phenomenon has a gradually increasing tendency. When different spatial weight matrices are chosen, the results of local spatial autocorrelation show significant spatial differences. Thus, the contrast analysis method may be suitable to research other datasets from different target areas.
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