Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations

Abstract

Past studies focused on the relationships between land cover and urban temperature have commonly assumed stationarity and used conventional (global) regression analysis. In this study, geographically weighted regression (GWR) was used to test the spatial stationarity of the relationships between a set of land cover types (built-up, water, paddy field, and other vegetation) and the surface temperature in TaoYuan, Taiwan. By adopting the GWR approach, significant spatial non-stationarity of these relationships was observed and the strength of these relationships was markedly higher than from a conventional regression analysis. The differences have large impacts. If the regression models were used to derive an estimate of the urban heat island intensity for TaoYuan this would equate to 2.63°C and 3.17°C for the global and GWR models, respectively. This result showed that the urban heat island was underestimated by global model and this, therefore, increased potential to underestimate the risk of ill-health and discomfort for urban populations. The mapped parameters derived from GWR analyses provided useful information for planning temperature mitigation and adaptation strategies especially for the very young and elderly that are particularly sensitive to temperature.