Geographically weighted regression-based determinants of malaria incidences in northern China

doi:10.1111/tgis.12259

	Geographically weighted regression-based determinants of malaria incidences in northern China
	Ge, Yong 1,5,7; Song, Yongze 1,2; Wang, Jinfeng 1,3; Liu, Wei 4; Ren, Zhoupeng 1,3,5; Peng, Junhuan 2; Lu, Binbin 6
刊名	TRANSACTIONS IN GIS
	2017-10-01
卷号	21 期号:5 页码:934-953
关键词	geographically weighted regression local determinants examination malaria incidence remote sensing monitoring data spatial analysis models
ISSN号	1361-1682
DOI	10.1111/tgis.12259
通讯作者	Ge, Yong(gey@lreis.ac.cn)
英文摘要	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 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.
资助项目	National ST Major Program[2012CB955503]
WOS关键词	CLIMATE-CHANGE ; TEMPERATURE ; MODELS ; TRANSMISSION ; ENVIRONMENT ; ASSOCIATION ; POPULATION ; INFECTION ; HIGHLANDS ; CHILDREN
WOS研究方向	Geography
语种	英语
出版者	WILEY
WOS记录号	WOS:000412577200006
资助机构	National ST Major Program
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/62308]
专题	中国科学院地理科学与资源研究所
通讯作者	Ge, Yong
作者单位	1.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
推荐引用方式 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.