Geographically weighted regression-based determinants of malaria incidences in northern China
文献类型：期刊论文
| 作者 | Ge, Yong1,5,7; Song, Yongze1,2; Wang, Jinfeng1,3; Liu, Wei4; Ren, Zhoupeng1,3,5; Peng, Junhuan2; Lu, Binbin6 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 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. |
| WOS关键词 | CLIMATE-CHANGE ; TEMPERATURE ; MODELS ; TRANSMISSION ; ENVIRONMENT ; ASSOCIATION ; POPULATION ; INFECTION ; HIGHLANDS ; CHILDREN |
| 资助项目 | National ST Major Program[2012CB955503] |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:000412577200006 |
| 出版者 | WILEY |
| 资助机构 | National ST Major Program |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。