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Geographically weighted regression-based determinants of malaria incidences in northern China
Ge, Yong1,5,7; Song, Yongze1,2; Wang, Jinfeng1,3; Liu, Wei4; Ren, Zhoupeng1,3,5; Peng, Junhuan2; Lu, Binbin6
2017-10-01
Source PublicationTRANSACTIONS IN GIS
ISSN1361-1682
Volume21Issue:5Pages:934-953
Corresponding AuthorGe, Yong(gey@lreis.ac.cn)
AbstractGeographically 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 R-2=0.243 and AICc=837.99, while significant improvement has been made by the GWR calibration with R-2=0.800 and AICc=618.54.
Keywordgeographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models
DOI10.1111/tgis.12259
WOS KeywordCLIMATE-CHANGE ; TEMPERATURE ; MODELS ; TRANSMISSION ; ENVIRONMENT ; ASSOCIATION ; POPULATION ; INFECTION ; HIGHLANDS ; CHILDREN
Indexed BySCI
Language英语
Funding ProjectNational ST Major Program[2012CB955503]
Funding OrganizationNational ST Major Program
WOS Research AreaGeography
WOS SubjectGeography
WOS IDWOS:000412577200006
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62309
Collection中国科学院地理科学与资源研究所
Corresponding AuthorGe, Yong
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.China Univ Geosci, Sch Land Sci & Technol, Beijing, Peoples R China
3.Chinese Ctr Dis Control & Prevent, Key Lab Surveillance & Early Warning Infect Dis, Beijing, Peoples R China
4.Michigan State Univ, Dept Geog, E Lansing, MI 48824 USA
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Hubei, Peoples R China
7.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Ge, Yong,Song, Yongze,Wang, Jinfeng,et al. Geographically weighted regression-based determinants of malaria incidences in northern China[J]. TRANSACTIONS IN GIS,2017,21(5):934-953.
APA Ge, Yong.,Song, Yongze.,Wang, Jinfeng.,Liu, Wei.,Ren, Zhoupeng.,...&Lu, Binbin.(2017).Geographically weighted regression-based determinants of malaria incidences in northern China.TRANSACTIONS IN GIS,21(5),934-953.
MLA Ge, Yong,et al."Geographically weighted regression-based determinants of malaria incidences in northern China".TRANSACTIONS IN GIS 21.5(2017):934-953.
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