Hierarchical Geographically Weighted Regression Model

• 作者: Fengchang Xue
• 来源期刊: 量子计算杂志(英文)
• 学科: 数学
• 关键词: GEOGRAPHIC INFORMATION regression analysis SCALE effect SPATIAL DEPENDENCE
• 年，卷（期）: 2019,（1）
• 页码范围: 9-20
• 页数: 12页
• 分类号: O17

摘要

In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines, forming the Geographical WeightedRegression (GWR). Based on classic statistical techniques, GWR model has ascertainsignificance in solving spatial dependence and spatial non-uniform problems, but it hasno impact on the integration of the scale effect. It does not consider the interactionbetween the various factors of the sampling scale observations and the numerous factorsof possible scale effects, so there is a loss of information. Crossing a two-stage analysisof “return of regression” to establish the model of Hierarchical Geographically WeightedRegression (HGWR), the first layer of regression analysis reflects the spatial dependenceof space samples and the second layer of the regression reflects the spatial relationshipsscaling. The combination of both solves the spatial scale effect analysis, spatialdependence and spatial heterogeneity of the combined effects.