Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression |
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| Authors: | Shuangcheng Li Zhiqiang Zhao Xie Miaomiao Yanglin Wang |
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| Affiliation: | 1. MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China;2. Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada;3. State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China;1. Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong, China;2. Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, China;3. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China;4. School of Architecture, Tsinghua University, Beijing 100871, China;5. Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China;1. College of Urban and Environmental Sciences, Peking University, Laboratory for Earth Surface Processes, Ministry of Education, Beijing 100871, China;2. Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518005, China;1. Université Paris 8, LADYSS, UMR 7533 CNRS, France;2. Equipe de Recherche en Epidémiologie Nutritionnelle, U1153 Inserm, INRA, CNAM, Université Paris 13, Sorbonne Paris Cité, Centre de Recherche en Epidémiologie et Biostatistiques, CRNH IdF, Bobigny, France;3. Université Paris 1, Géographie-Cités, UMR 8504 CNRS, France;4. Université de Strasbourg, LIVE, UMR 7562 CNRS, France;5. Luxembourg Institute of Socio-Economic Research, Esch/Alzette, Luxembourg;6. Institut des Systèmes Complexes de Paris Île-de-France, France;7. CRNH Rhône-Alpes, Lyon University, Laboratoire CarMeN, INSERM U1060, INRA U1235, Université Claude Bernard Lyon 1, INSA-Lyon, France;8. Université Paris Est, Lab Urba, UPEC, Créteil, France;9. Department of Nutrition, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital (AP-HP), Pierre et Marie Curie University — Sorbonne Université, Paris, France |
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| Abstract: | Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology. |
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