AN INVESTIGATION OF LOCAL EFFECTS ON SURFACE WARMING WITH 
GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) 
Y. Xue *"*, T Fung?, J. Tsov* 
? Satellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and 
Geoinformation, P.R.China- xueyc(@sasmac.cn 
"Department of Geography and Resource Management, the Chinese University of Hong Kong, Shatin, Hongkong SAR, 
P.R.China - tungfung@cuhk.edu.hk 
* School of Architecture, the Chinese University of Hong Kong, Shatin, Hongkong SAR, P.R.China - 
jinyeutsou@cuhk.edu.hk 
KEY WORDS: Urban Warming, Thermal Landscape, Geographically Weighted Regression (GWR), Thermal Infrared Remote 
Sensing, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) 
ABSTRACT: 
Urban warming is sensitive to the nature (thermal properties, including albedo, water content, heat capacity and thermal conductivity) 
and the placement (surface geometry or urban topography) of urban surface. In the literature the spatial dependence and 
heterogeneity of urban thermal landscape is widely observed based on thermal infrared remote sensing within the urban environment. 
Urban surface warming is conceived as a big contribution to urban warming, the study of urban surface warming possesses 
significant meaning for probing into the problem of urban warming.The urban thermal landscape study takes advantage of the 
continuous surface derived from thermal infrared remote sensing at the landscape scale, the detailed variation of local surface 
temperature can be measured and analyzed through the systematic investigation. At the same time urban environmental factors can 
be quantified with remote sensing and GIS techniques. This enables a systematic investigation of urban thermal landscape with a link 
to be established between local environmental setting and surface temperature variation. The goal of this research is utilizing 
Geographically Weighted Regression (GWR) to analyze the spatial relationship between urban form and surface temperature 
variation in order to clarify the local effects on surface warming, moreover to reveal the possible dynamics in the local influences of 
environmental indicators on the variation of local surface temperature across space and time. In this research, GWR analysis proved 
that the spatial variation in relationships between environmental setting and surface temperature was significant with Monte Carlo 
significance test and distinctive in day-night change. Comparatively, GWR facilitated the site specific investigation based on local 
statistical technique. The inference based on GWR model provided enriched information regarding the spatial variation of local 
environment effect on surface temperature variation which global model cannot approach. 
1. INTRODUCTION 
During the process of worldwide urbanization along with 
high rise and high density housing development in large 
cities, urban warming has received growing concern among 
the environmental issues corresponding with urban landscape 
change. Due to the complicate interplay between urban 
environment and local climate, it is far from being certain 
about the urban effects on local warming, few attention has 
been paid to the variations of local effects on urban thermal 
performance at a scale over whole city area during urban 
development, even endeavors is quite scarce to characterize 
and analyze the spatial heterogeneity and dependency of 
urban thermal landscape aiming at delimitation of local 
effects on surface warming at city scale combining the 
measurement of urban surface and geometry via a viewpoint 
of local variation in space and time. 
The urban thermal landscape study takes advantage of the 
continuous surface derived from thermal infrared remote 
sensing at the landscape scale, the detailed variation of local 
surface temperature can be measured and analyzed through 
* Corresponding author. 
the systematic investigation. At the same time urban 
environmental factors can be quantified with remote sensing 
and GIS techniques. This enables a systematic investigation 
of urban thermal landscape with a link to be established 
between local environmental setting and surface temperature 
variation. Then the mechanism which made the heterogeneity 
and complexity of urban surface thermal landscape can be 
studied in depth based on local statistical technique. 
Geographically Weighted Regression (GWR) is part of a 
growing trend in GIS towards local spatial analysis which 
intends to understand the spatial data in more detail through 
local statistics evaluation. In the literature a number of recent 
publications have demonstrated the analytical utility of GWR 
for various urban studies (Huang and Leung, 2002), while it 
has been scarcely utilized in urban climatic studies especially 
urban thermal landscape analysis. The interpretation of the 
local mechanism that affects and determines urban surface 
thermal abnormalities with the use of GWR presents 
significant potential for the in-depth investigation of local 
effects on urban surface temperature variation. The resulting 
outcome of GWR may help to examine the existence of 
potential correlation pattern in spatial inconstancy within