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

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    Fulltext not available
    Authors
    Ge, Y.
    Song, Yongze
    Wang, J.
    Liu, W.
    Ren, Z.
    Peng, J.
    Lu, B.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Ge, Y. and Song, Y. and Wang, J. and Liu, W. and Ren, Z. and Peng, J. and Lu, B. 2017. Geographically weighted regression-based determinants of malaria incidences in northern China. Transactions in GIS. 21 (5): pp. 934-953.
    Source Title
    Transactions in GIS
    DOI
    10.1111/tgis.12259
    ISSN
    1361-1682
    Faculty
    Faculty of Humanities
    School
    School of Design and the Built Environment
    URI
    http://hdl.handle.net/20.500.11937/77023
    Collection
    • Curtin Research Publications
    Abstract

    © 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.

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