was found and detected for Constant in night models, Solar 
Radiation and Road Density parameters within most of the 
models of this study during the GWR analysis. The spatial 
variations of the refereed parameters proved the mixed 
correlation between such evaluation variables and local 
{Monte | Constant | ete Solar 
iion s Road 
NDVI Radiati Square | Elevation Densiy 
ed RARE 
*** = significant at .1% level ** = significant at 1% level 
  
  
    
   
    
  
   
  
  
  
  
  
  
  
  
  
  
    
  
surface temperature across the whole study area, which is not 
uniformly stationary within the whole study area and may 
change from negative correlation to positive correlation 
under local circumstance. 
  
* = significant at 5% level 
Table 3. Geograhically weighted regression diagnosis 
Since each environmental measurement has various range of 
value, moreover the NDVI values range from negative to 
positive, this made the direct comparison of parameter 
coefficient difficult in order to measure the importance of 
each parameter impact on local surface temperature variation, 
including the direction (the possible cooling or warming 
effect) and extent (the altitude of local surface temperature 
change induced). For easy comparison between the 
influences of environmental factors on local surface 
temperature variation across space and time, the indicating 
impacts of each environmental parameter can be quantified 
with the statistical variation of surface temperature induced 
by each factor, i.e. here called component contribution using 
the following formula: 
Component Contribution (CC) =factor value * parameter 
coefficient (1) 
Then all the component contribution to surface temperature 
variation corresponding to each factor have been calculated 
in order to measure the influence of each environmental 
factor on local surface temperature change within each image 
time, with positive CC value indicating the possible warming 
effect by increasing local surface temperature with 
corresponding statistical CC value ? C, and negative CC 
value indicating the possible cooling effect by decreasing 
local surface temperature with CC value ? C. 
In order to demonstrate the local variation pattern, the 
illustration would emphasize on parameters whose 
coefficients distribution show significant spatial variation 
along with the seasonal and day-night change. To this end, 
the CC values related to parameters,such as Elevation, 
Distance from coast, in daytime models during 04/17/2006 
and 11/03/2003 together with nighttime model during 
10/28/2003 were chosen as examples and mapped against the 
corresponding VNIR image to facilitate further interpreting 
the spatial varying impact of each factor on surface 
temperature variation which is shown in Figures 3~8. 
Both the nighttime and daytime models during 10/28/2003 
and 11/03/2003, 04/17/2006 demonstrate significant spatially 
varying relationships between Elevation and local surface 
temperature with diverse parameter coefficients of Elevation 
within whole study area, CCs of Elevation at each image 
time are mapped in Figures 3, 4 and 5. Figure 3 shows that 
the nighttime model in 10/28/2003 demonstrates consistent 
negative correlation between Elevation and local surface 
temperature which implies a stable cooling effect of 
Elevation on local warming during nighttime period. The 
cooling effect is becoming stronger with lower negative CC 
values along with the altitude rising, with the lowest located 
in the peak of the hill indicating the strongest cooling effect 
in high altitude area. On the other hand the daytime models 
with seasonal change during 11/03/2003 and 04/17/2006 
demonstrate a more diverse correlation ranging from positive 
to negative within whole study area in Figures 4 and 5, this 
may be induced by the various heating situation created with 
solar radiation during daytime under intensive elevation 
variation of urban canopy. Most of the areas with positive 
CC values indicating a heating effect on local surface 
temperature located in relatively low elevation area, under 
these low elevations area increasing the elevation may 
increase the opportunity to receive more solar radiation and 
then heighten the local surface temperature. At the same time 
it is interesting to find that the area coverage and value of 
positive CC is larger (with the biggest value 2.78) during dry 
season 11/03/2003 than during the growing season 
04/17/2006(with the biggest value 2.25). This may be due to 
the fact that the solar elevation angle during 04/17/2006 is 
larger (67.33 deg) compared with 11/03/2003 (49.39 deg), 
which helps decrease the area coverage and influence of solar 
shading. 
With the comparison of the CC values distribution of 
distance from coast for nighttime 10/28/2003, and daytime 
11/03/2003 and 04/17/2006 shown in Figures 6, 7 and 8, it is 
easy to find that the CC values ranges from negative to 
positive when being far away from coast. Within a certain 
distance to the coast which may vary with local wind speed 
and direction and relative humidity, proximity to coast shows 
a significant negative correlation with the local surface 
temperature which indicates the obvious cooling effect on 
local surface within this extent. This confirmed that GWR 
model revealed the cooling effect of proximity with coast, 
when being further far away from coast the cooling effect 
vanished, instead of warming effect emerging. Comparing 
  
the pattern 
model 11/( 
(the blue s; 
tends to sh: 
negative ( 
04/17/2006 
speed (5.6 
time 04/17 
of coast. | 
blowing fr 
blowing fr 
yellow cir 
positive in 
proximity 
from tem] 
cooling ef 
be due to 
coast, the 
may have 
slightly cc 
temperatu: 
  
Figure 3. 
  
Figure 4