Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China

Chen, Xiaodie; Emam, Mawlanjan; Li, Wei Min; Rifhat, Ramziya; Zhang, Liping; Zheng, Yanling

doi:10.1016/j.pmedr.2023.102362

Cited by 5 publications

(3 citation statements)

References 34 publications

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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%

The Impact of Human Activity Expansion on Habitat Quality in the Yangtze River Basin

Bian,

Yang,

Zhao

et al. 2024

Land

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Globally, natural habitats have suffered tremendous damage from human activities, a phenomenon that is increasingly evident in basin regions. The management of natural habitats in basin regions is dependent on understanding of the various impacts of human activities on these ecosystems. Despite the various studies that have been conducted on the effects of human activities on habitats in basin regions, there is still a lot of doubt regarding the impact of these activities on the quality of basin ecosystems. To fill this gap, this study employs a series of spatial analysis methods and logistic regression modeling to delve into the spatial and temporal patterns of human activities and habitat quality in the Yangtze River Basin (YRB) as well as the differences in the impacts of human activities on habitat quality in the sub-basins of the YRB. The findings indicate a 0.408% decline in the overall environmental quality of the YRB area from 2000 to 2020, accompanied by a 15.396% surge in human activities. Notably, the southeastern Qilian Mountains and the mountainous regions in the northwestern sector of the Sichuan Basin emerge as pivotal areas for habitat quality restoration. Conversely, the southwestern Qilian Mountains and the urban clusters in the Yangtze River Delta (YRD) face significant habitat quality deterioration. Spatial regression analyses reveal a noteworthy trend: the burgeoning human activities in the Yangtze River region pose a substantial threat to habitat recovery efforts. Further differential analyses focusing on the upper, middle, and lower basin segments underscore that human activities exert the most pronounced impact on habitat quality within the lower basin region, while the upper basin experiences the least influence. The implications of this study are manifold. It furnishes valuable policy insights for the comprehensive management and targeted preservation of habitats across the YRB. By delineating areas of habitat restoration and degradation and highlighting the differential impacts of human activities across basin segments, this research lays a solid foundation for informed decision making in habitat conservation and ecosystem management within the YRB.

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Section: Spatial Regression Modelmentioning

confidence: 99%

The Impact of Human Activity Expansion on Habitat Quality in the Yangtze River Basin

Bian,

Yang,

Zhao

et al. 2024

Land

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“…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%

Modeling the Flood Disaster in South Kalimantan Using Geographically Weighted Regression and Mixed Geographically Weighted Regression

Farida,

Refiana Nurfadila,

Keumala Intan

et al. 2024

ITM Web Conf.

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The flood disaster in South Kalimantan is a crucial problem that needs to be addressed because the impact is relatively severe. So, this study aims to model flood disasters in South Kalimantan based on factors suspected to be the cause, including population density, rainfall, residential area, and forest area. This study uses two methods of spatial statistics, namely the Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) methods. The weighting used is Adaptive Gaussian. The modeling results show that the GWR model is superior in explaining the causes of flood events in South Kalimantan, which is indicated by the highest coefficient of determination value of 95.62% compared to the regression and MGWR models. Nonetheless, the MGWR model can explain the causes of flooding in Kalimantan. The GWR and MGWR models show that the area that is vulnerable to flooding is Balangan District. The results of this study contribute to providing alternative information for disaster mitigation to minimize losses.

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“… 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%

Effectiveness and Determinants of Implementing the “Xinjiang Model” for Tuberculosis Prevention and Control: A Quantitative Study

Zhang,

Wang,

Wang

et al. 2024

IDR

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Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China

Cited by 5 publications

References 34 publications

The Impact of Human Activity Expansion on Habitat Quality in the Yangtze River Basin

The Impact of Human Activity Expansion on Habitat Quality in the Yangtze River Basin

Modeling the Flood Disaster in South Kalimantan Using Geographically Weighted Regression and Mixed Geographically Weighted Regression

Effectiveness and Determinants of Implementing the “Xinjiang Model” for Tuberculosis Prevention and Control: A Quantitative Study

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