Research Article
Geographically weighted regression-based determinants of malaria incidences in northern China
Yong Ge,
Yongze Song,
Jinfeng Wang,
Wei Liu,
Zhoupeng Ren,
Junhuan Peng,
Binbin Lu,
Corresponding Author
Yong Ge
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence Yong Ge, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China. Email: [email protected]Search for more papers by this authorYongze Song
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
School of Land Science and Technology, China University of Geosciences, Beijing, China
Search for more papers by this authorJinfeng Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
Search for more papers by this authorWei Liu
Department of Geography, Michigan State University, East Lansing, USA
Search for more papers by this authorZhoupeng Ren
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorJunhuan Peng
School of Land Science and Technology, China University of Geosciences, Beijing, China
Search for more papers by this authorBinbin Lu
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorYong Ge,
Yongze Song,
Jinfeng Wang,
Wei Liu,
Zhoupeng Ren,
Junhuan Peng,
Binbin Lu,
Corresponding Author
Yong Ge
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
University of Chinese Academy of Sciences, Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Correspondence Yong Ge, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China. Email: [email protected]Search for more papers by this authorYongze Song
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
School of Land Science and Technology, China University of Geosciences, Beijing, China
Search for more papers by this authorJinfeng Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
Search for more papers by this authorWei Liu
Department of Geography, Michigan State University, East Lansing, USA
Search for more papers by this authorZhoupeng Ren
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorJunhuan Peng
School of Land Science and Technology, China University of Geosciences, Beijing, China
Search for more papers by this authorBinbin Lu
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Search for more papers by this authorAbstract
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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