Spatio temporal dynamics of the urban heat island in singapore 5

140
• In terms of monthly values, rainfall is found not to have a strong influence
on mean and maximum UHI intensities. Air temperature and wind speed are
better predictors of monthly mean and maximum UHI intensities.
• In this study area, landscape factors do not influence the nocturnal UHIraw
and may only be slightly influential in terms of the daytime UHIraw .

Limitations of the study and potential future work
There are several limitations to this study that can be improved upon given
more time, resources and expertise. These also serve as potential extensions for
future work.
• The lack of more site-specific meteorological information such as wind speed,
solar radiation and soil moisture means that detailed analysis on the weather,
moisture or antecedent factors cannot be achieved.
• While this study provides a comprehensive documentation and analysis of
the urban thermal environment in Singapore, it can be built upon with multidimensional statistical or numerical methods that allow for more accurate
modelling of UHI where sensors may not be available.
Another potential extension is the impact analysis of the variability of the thermal
environment on building energy consumption and human health, creating normative
value in the research.

(23604 words)


141

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158

S43

Mock
Village

1.394

103.69

6A

Not used in analysis of urban morphology

S44

Waterloo
Street

1.299

103.85

12

Not used in analysis of urban morphology

S45

Eng Kong

1.335

103.77

3

Not used in analysis of urban morphology

S46

Opp
Paragon

1.304

103.84

12

Not used in analysis of urban morphology

Selected metadata for observation sites. Note that LCZ = Local Climate Zone. Parent
classes can be found in Appendix C and LCZs with subscript (secondary) classes are
considered mixed land use or land cover (For more details, refer to Appendix C and
Stewart and Oke, 2012).


159
Appendix C: LCZ classes

The parent LCZ classes for built environments and land cover. Source: Stewart and
Oke (2012, pp 38)


160
Appendix D: Fish-eye images for sensor localities

161

Full circular fish-eye images used to determine sky view-factor. Top row: S01, S02, S03,
S04. Row 2: S05, S06, S07, S08. Row 3: S09, S10, S11, S12. Row 4: S13, S14, S15, S16.
Row 5: S17, S18, S19, S20. Row 6: S21, S22, S23, S24. Row 7: S25, S28, S29, S30. Row
8: S31, S32, S34, S36. Bottom row: S37, S38, S39, S40.


162
Appendix E: Selected codes and scripts used in the study
Linux shell script for cloud/rain days extraction and cropping
# Extraction of cloud and rainfall overlay images (2008-2011)
# from Wunderground database using nested loops
for y in {2008..2011}; do for m in {01..12}; do for d in {01..31};
do wget
" \
-O "$y"_"$m"_"$dover".png;
done; done; done
# Singapore boundaries and scaling obtained from QGIS georeferencing of
# image template
# Perform crop using known scaling
for i in *.png; do convert "$i".png -crop 21x10+107+189 "$i"_cropped.png
# List of files compared with date vector to ensure no missing data
find . -name "*cropped.png" -printf "%f\n" > imagelist
diff imagelist datevector
# Cloudless/rainless images have limited data size in terms of bytes (S)
# Sample listing of days with any amount of rain.
find . -name "*cropped.png" -printf "%f %s\n" | awk ’{if ($2 > $S) print}’

R code for plotting faceted plots (e.g. Figure 4.11)
# To order from noon to midnight
with(dataset, factor(hour,levels=c("12","13","14","15","16","17","18","19",
"20","21","22","23","00","01","02","03","04","05","06",
"07","08","09","10","11"))) -> dataset$hour
# Faceted plot
ggplot(dataset,aes(hour,value,colour=value,group=variable)) +
geom_line() + facet_wrap(~ month,ncol=3) +
scale_colour_gradient(low="green", high="red", name="") +
scale_x_discrete(breaks=c("12","18","00","06")) +
geom_hline(y=0,alpha=0.5, linetype=2) +
ylab(expression(paste("UHI"[raw], Celsius))) +
xlab("Hour (Local Time)") + opts(legend.position="none")


163
GRASS script for spatial interpolation (IDW)
# Region and resolution declared with preset mapset
g.region vect=singavect@PERMANENT
g.region res=0.001
# Import CSV created in R with coords and value
v.in.ascii --overwrite input=input_csv output=interpolated@PERMANENT \
fs=, x=2 y=1
# Apply masking with Singapore outline
r.mask input=sg_outline@PERMANENT maskcats=*
# Perform IDW and create contour lines
v.surf.idw -n --overwrite --verbose input=interptest@PERMANENT
output=surface column=dbl_3
r.contour --overwrite input=surface@PERMANENT output=contour step=0.2
# Set monitor for printing

d.mon x0
# Displays relevant layers and outputs as PNG file
d.rast surface@PERMANENT
d.vect contour@PERMANENT
d.vect interptest@PERMANENT
d.out.file output=outputfilename format=png compression=9 --overwrite -m

R function for LULC variables against UHI variables linear function fitting
urbanf <-function(x,y){
a="FAIL";
b="FAIL";
c="FAIL";
d="FAIL";
summary(lm(x ~ poly(y,2))) -> a;
summary(lm(x ~ sqrt(y))) -> b;
summary(lm(x ~ y)) -> c;
try(summary(lm(x ~ log10(y))) -> d, silent=T);
list(c,a,d,b) -> out;
return(out)
}