TY - JOUR
T1 - Geographically weighted regression models for ordinal categorical response variables
T2 - An application to geo-referenced life satisfaction data
AU - Dong, Guanpeng
AU - Nakaya, Tomoki
AU - Brunsdon, Chris
N1 - Funding Information:
This work was funded by the UK Economic and Social Research Council (ESRC) through the project of Dynamics of Health & Environmental Inequality in Hebei Province, China, grant number ES/P003567/1 .
Publisher Copyright:
© 2018 The Authors
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Air pollution
KW - Beijing
KW - Environmental valuation
KW - Geographically weighted regression
KW - Ordinal response variables
KW - Spatial heterogeneity
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U2 - 10.1016/j.compenvurbsys.2018.01.012
DO - 10.1016/j.compenvurbsys.2018.01.012
M3 - Article
AN - SCOPUS:85041687412
VL - 70
SP - 35
EP - 42
JO - Computers, Environment and Urban Systems
JF - Computers, Environment and Urban Systems
SN - 0198-9715
ER -