Geographically weighted regression‐based determinants of malaria incidences in northern China |
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| Authors: | Yong Ge Yongze Song Jinfeng Wang Wei Liu Zhoupeng Ren Junhuan Peng Binbin Lu |
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| Affiliation: | 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China;2. University of Chinese Academy of Sciences, Beijing, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China;4. School of Land Science and Technology, China University of Geosciences, Beijing, China;5. Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China;6. Department of Geography, Michigan State University, East Lansing, USA;7. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China |
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| Abstract: | Geographically weighted regression (GWR) is an important local method to explore spatial non‐stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7‐year period in northern China, a typical mid‐latitude, high‐risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non‐spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7‐year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54. |
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| Keywords: | geographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models |
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