Figure 8. Coefficients contribution of Distance from coast in 
daytime model 04/17/06 
The local comparisons of the relative magnitudes of CC 
among environmental variables are carried out to investigate 
the relative contributions of the independent variables to 
local surface temperature variation. The local dominant 
factors which contribute most to location specific surface 
temperature variation represented with the factor possessing 
the site specific biggest CC can be mapped and compared to 
differentiate the relative contribution of environmental 
factors. It was found that the local dominant factors to 
surface temperature variation are varying in space and 
time.This uncovered the local disparities of surface heating 
mechanism, which also indicates the dynamics of local effect 
of environmental factors on local surface heating in space 
and time, at one specific location, the order in relative 
magnitudes of environmental indicators contributing to local 
surface temperature variation is varied with geographical 
location and time. Especially in urban area it is difficult to 
generate a local pattern regarding the relative importance of 
environmental factor on site specific surface temperature due 
to the highly variation of the local dominant factors, which 
may in part due to the relative low resolution in sampling and 
measurement of some factors like population density and 
road density comparing with the intensive variation of urban 
landscape. 
The varying relationships between urban surface temperature 
and local environmental setting in space and time represented 
with the referred environmental factors made the emerged 
pattern of local dominant factors in terms of component 
contribution (CC) challenging to be interpreted and explained 
which may be due to limitations in the definition and 
measure of environmental indicators (Holt and Lo, 2008). It 
also made the clear delineation in the relative importance of 
environmental indicators on surface temperature variation, in 
particular surface warming difficult due to the complexity 
induced by the local varying effect in space and time. 
5. CONCLUSIONS 
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. the site 
specific relation patterns related to each environmental 
parameter are mapped and compared in order to analyze the 
  
local impact of each factor on surface temperature 
variation .Through analysis, we found that the local dominant 
factor accounted for most to the site specific surface 
temperature variation was highly varied in space and time 
which prevented a general delineation of the relative 
association among environment factors to surface 
temperature disparities. This implied that the effective 
adaptive measures should be devised locally with reference 
to day-night needs in the identification of this feature. 
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. 
REFERENCES 
Brunsdon, C., Fotheringham, S. and Charlton, M., 1998. 
Geographically Weighted Regression-Modelling spatial non- 
stationarity. The Statistician, 47( 3),pp. 431-443. 
Fotheringham, A.S., Brunsdon, C. and Chartlon, M., 2002. 
Geographically Weighted Regression: the analysis of 
spatially varying relationships. John Wiley & Sons Ltd, 
England. 
Holt, J. B. and Lo, C. P., 2008. The geography of mortality 
in the Atlanta metropolitan area. Computers, Environment 
and Urban Systems, 32(2), pp.149-164. 
Huang, Y. , 2000. Regional economic development in 
yangtze river delta since 1978: Jiangsu province as a 
particular case. PHD thesis, CUHK. 
Huang, Y. and Leung, Y., 2002. Analyzing regional 
industrialization in Jiangsu province using geographically 
weighted regression. Journal of Geographical Systems, 4, 
pp.233-249. 
Miller, P. C., Stoner, W. A. and Tieszen, L. L. , 1976. A 
model of stand photosynthesis for the wet meadow tundra at 
Barrow, Alaska. Ecology, 57(3), pp.411-430. 
Oke, T.R., 1982. The energetic basis of the urban heat island. 
Quarterly Journal of Royal Meteorology Society, 108, pp.1- 
24. 
Su, B. S., Chong, A. K., Moore, A. ,2005. Geostatistical 
Modeling, Analysis and Mapping of Epidemiology of 
Dengue Fever in Johor State, Malaysia. Paper Presented at 
SIRC 2005 — The 17th Annual Colloquium of the Spatial 
Information Research Centre, University of Otago, Dunedin, 
New Zealand, November 24th-25th 2005. 
Voogt, J.A. How Researchers Measure Urban Heat Islands. 
http://www.epa.gov/heatisland/resources/pdf/ 
EPA How to measure a UHI.pdf, | EPA presentation. 
    
    
  
   
     
   
  
    
    
  
   
   
   
   
   
   
    
     
   
   
   
    
     
   
    
   
     
   
    
     
      
   
   
    
   
   
   
    
   
   
   
    
   
    
  
   
   
   
   
   
    
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