Geographically weighted regression-based determinants of malaria incidences in northern China
© 2016 John Wiley & Sons Ltd 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 para...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
WILEY
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/77023 |
| _version_ | 1848763805058727936 |
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| author | Ge, Y. Song, Yongze Wang, J. Liu, W. Ren, Z. Peng, J. Lu, B. |
| author_facet | Ge, Y. Song, Yongze Wang, J. Liu, W. Ren, Z. Peng, J. Lu, B. |
| author_sort | Ge, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 John Wiley & Sons Ltd 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. |
| first_indexed | 2025-11-14T11:09:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-77023 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:09:17Z |
| publishDate | 2017 |
| publisher | WILEY |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-770232019-12-03T01:11:20Z Geographically weighted regression-based determinants of malaria incidences in northern China Ge, Y. Song, Yongze Wang, J. Liu, W. Ren, Z. Peng, J. Lu, B. Social Sciences Geography geographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models CLIMATE-CHANGE TEMPERATURE MODELS TRANSMISSION ENVIRONMENT ASSOCIATION POPULATION INFECTION HIGHLANDS CHILDREN © 2016 John Wiley & Sons Ltd 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. 2017 Journal Article http://hdl.handle.net/20.500.11937/77023 10.1111/tgis.12259 English WILEY restricted |
| spellingShingle | Social Sciences Geography geographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models CLIMATE-CHANGE TEMPERATURE MODELS TRANSMISSION ENVIRONMENT ASSOCIATION POPULATION INFECTION HIGHLANDS CHILDREN Ge, Y. Song, Yongze Wang, J. Liu, W. Ren, Z. Peng, J. Lu, B. Geographically weighted regression-based determinants of malaria incidences in northern China |
| title | Geographically weighted regression-based determinants of malaria incidences in northern China |
| title_full | Geographically weighted regression-based determinants of malaria incidences in northern China |
| title_fullStr | Geographically weighted regression-based determinants of malaria incidences in northern China |
| title_full_unstemmed | Geographically weighted regression-based determinants of malaria incidences in northern China |
| title_short | Geographically weighted regression-based determinants of malaria incidences in northern China |
| title_sort | geographically weighted regression-based determinants of malaria incidences in northern china |
| topic | Social Sciences Geography geographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models CLIMATE-CHANGE TEMPERATURE MODELS TRANSMISSION ENVIRONMENT ASSOCIATION POPULATION INFECTION HIGHLANDS CHILDREN |
| url | http://hdl.handle.net/20.500.11937/77023 |