2023
DOI: 10.1016/j.pmedr.2023.102362
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Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China

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Cited by 5 publications
(3 citation statements)
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“…Unlike the traditional global OLS model, GWR considers the influence of spatial location, thus providing a more effective solution to spatial autocorrelation issues in model residuals [49]. One of the strengths of the GWR model is that it is able to capture the heterogeneity of data across geographic locations, which helps to analyze the causes of spatial heterogeneity [50]. GWR is thus widely used in ecological studies because ecosystems are usually significantly affected by geographic location, e.g., factors such as climate, soil type, and vegetation cover may have different effects in different locations.…”
Section: Spatial Regression Modelmentioning
confidence: 99%
“…Unlike the traditional global OLS model, GWR considers the influence of spatial location, thus providing a more effective solution to spatial autocorrelation issues in model residuals [49]. One of the strengths of the GWR model is that it is able to capture the heterogeneity of data across geographic locations, which helps to analyze the causes of spatial heterogeneity [50]. GWR is thus widely used in ecological studies because ecosystems are usually significantly affected by geographic location, e.g., factors such as climate, soil type, and vegetation cover may have different effects in different locations.…”
Section: Spatial Regression Modelmentioning
confidence: 99%
“…Previous research using the GWR method, such as research by Purwaningsih et al [17] modeling flood cases in Central Java, showed that the highest flood events are in the Cilacap area, where in the Cilacap area, flood cases are caused by rainy days and the room with a coefficient of determination of 0.69. Chen et al [18] analyzed the prevalence of tuberculosis cases in Kashgar, China, by obtaining a coefficient of determination of 0.7452. Wu et al [19] compared the GWR and other spatial statistical methods.…”
Section: Introductionmentioning
confidence: 99%
“… 6 To promote sustainable and effective implementation of the “Xinjiang model”, it is important to promptly identify the factors affecting its effectiveness. Based on the previous study, this study analyzed the effectiveness of the Xinjiang model in Kashgar, a region with a high burden of tuberculosis in Xinjiang, 7 , 8 and its counties and cities, and further explored the factors influencing the effectiveness of the model based on two counties with poor and good implementation. Identifying and improving the factors affecting the effectiveness of the “Xinjiang model” will provide information for the continuous improvement and high quality of the new model of tuberculosis control in Xinjiang.…”
Section: Introductionmentioning
confidence: 99%