MURAL - Maynooth University Research Archive Library



    Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data


    Dong, Guanpeng and Nakaya, Tomoki and Brunsdon, Chris (2018) Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data. Computers, Environment and Urban Systems. ISSN 0198-9715

    [img]
    Preview
    Download (1MB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Ordinal categorical responses are commonly seen in geo-referenced survey data while spatial statistics tools for modelling such type of outcome are rather limited. The paper extends the local spatial modelling framework to accommodate ordinal categorical response variables by proposing a Geographically Weighted Ordinal Regression (GWOR) model. The GWOR model offers a suitable statistical tool to analyse spatial data with ordinal categorical responses, allowing for the exploration of spatially varying relationships. Based on a geo-referenced life satisfaction survey data in Beijing, China, the proposed model is employed to explore the socio-spatial variations of life satisfaction and how air pollution is associated with life satisfaction. We find a negative association between air pollution and life satisfaction, which is both statistically significant and spatially varying. The economic valuation of air pollution results show that residents of Beijing are willing to pay about 2.6% of their annual income for per unit air pollution abatement, on average.

    Item Type: Article
    Keywords: Geographically weighted regression; Spatial heterogeneity; Ordinal response variables; Air pollution; Environmental valuation; Beijing;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Faculty of Social Sciences > Geography
    Item ID: 13056
    Identification Number: https://doi.org/10.1016/j.compenvurbsys.2018.01.012
    Depositing User: Prof. Chris Brunsdon
    Date Deposited: 16 Jun 2020 17:11
    Journal or Publication Title: Computers, Environment and Urban Systems
    Publisher: Elsevier
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year