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首页> 外文期刊>Journal of Geographic Information System >GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression
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GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression

机译：基于GIS的霍乱发生局部空间统计模型：使用地理加权回归

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Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region. ?
机译：全局统计技术通常假设跨空间因变量和预测变量之间的关系是同质的。统计地理学家批评此假设是一个基本缺陷，当将其应用于具有空间上下文的数据集时可能会产生误导性的结果。为了加强这一弱点，提出了一种解决跨地理空间关系异质性的新方法。这是称为地理加权回归（GWR）的一系列本地空间统计技术。该方法捕获了普通最小二乘（OLS）回归无法解释的空间数据中关系的非平稳性。因此，本文旨在利用基于GIS的GWR探索和分析霍乱发生与家庭供水源之间的空间关系，并比较OLS和GWR的建模适用性。在探索性分析中，使用了州水平的研究区域矢量数据集（空间）和霍乱病例，家庭供水源和人口数据的统计数据（非空间）。结果表明，GWR是对全局模型的重大改进。将两个模型与AICc值和R2值进行比较后发现，对于前者，该值从6​​98.7（对于OLS模型）降低到691.5（对于GWR模型）。对于后者，OLS解释为66.4％，而GWR解释为86.7％。这意味着本地模型的适用性高于全局模型。此外，经验分析表明，研究区域内霍乱的发生与家庭供水来源显着相关。由GWR检测到的这种关系在整个地区有很大不同。 ？

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