n each created 
ive a negative 
; depending on 
each selected 
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total test area 
s of calculation 
S 
measurements 
d verify its 
the scenario 
investigate the 
phase of the 
shall also be 
1able buildings 
del and further 
generally with 
also distributed 
oors, the floor 
nitially, further 
calculating the 
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Cultural + 
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U 
Community + 
regional 
Cultural + 
Community 
2. CONDUCTING URBAN SUSTAINABILITY SPATIAL 
ANALYSIS 
As a measure of showing and proving the usefulness of GIS 
spatial analysis in the process of ranking the sustainability in the 
urban design, a process was conducted in a practical manner 
starting with selecting the test arca, selecting some indicator 
related specific factors which are cultural, regional and climate 
specific, selecting the layers of feature classes that relate to 
those indicator factors, then by combining the results scenarios 
of assessments were conducted for the purpose, to finally 
present a result that further assessments and activities shall be 
built upon. 
2.1 Sample Energy Data 
The sample energy data provided was related to municipal 
buildings, a total number of six buildings data provided annual 
energy consumption in kWh and besides this those buildings 
which have used energy reduction plans, has provided also the 
saved amount in kWh. This sample data is extremely small and 
insufficient, but on the other hand can give the bases for 
calculating approximate values of energy consumption, which 
will be refined in a later stage using more data collected. 
But what is important here is not only get the amount of energy 
consumption but also if environmental friendly methods are 
being used in a building to reduce the energy, what type of 
activities are those, how much do they effect the energy 
conservation on annual or periodic bases, and most importantly 
as a result to that how much CO2 is reduced in tones. As an 
example of that one building was producing 21961993 KWh 
per annum and with a reduction baseline of 8 % managed to 
save 1756959 KWh which is equivalent to a measure of 878.5 
tones saving more than 70 thousand dollars for the first year of 
the reduction plan, it is off course anticipated that an investment 
has been made for following such a strategy but its sure 
benefiting for the long run. 
22 Spatial & Statistical Analysis to Calculate Energy 
The geographic places, geometries, and attributes for the 
buildings provided in sample data, were spatially searched and 
those falling within the test area where categorized. The plan is 
to use only those falling in the test area, but the results showed 
the results on table 2 where variance and standard deviation of 
the calculated factors need to be refined. 
  
  
have similarities, uses the same sources of energy, but 
variability will not harm anyway in such a case, perhaps 
variability will make the sample more reliable to represent the 
data. This is presented by Table 3 herein where an improvement 
of the variance and standard deviation can clearly be seen. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
volume | energy fac | Squared | Residual 
tor | deviation 
Build | 7928 1,382,480 | 17 | 5818 -387 
1 4 
Build | 4969 1048860 21 | 12766 -351 
2 1 
Build | 5251 5292610 10 | 827695 445 
3 07 
Build | 368950 | 21962000 | 59 | 1487 -502 
4 
Build | 5462 5555270 10 | 844348 454 
5 16 
Build | 9823 5395370 54 | 203520 -12 
6 9 
389487 | 38205250 | 98 | 315939 Variance 
562 STD 
  
  
Table 3 Larger Sample of Statistic for averaging energy 
For further narrowing the gap embedded within the sample data, 
those values were dropped comprising higher deviations or 
residuals. Further, the analysis was continued until a satisfaction 
of values was reached as follows in table 4: 
  
12052.4732 | Variance 
109.783756 | STD 
  
  
  
  
  
Table 4 
Now using areas of buildings and the number of floors 
attributes in the building data with the assumption of averaging 
The height of each floor to 3 meters, the equation that contain 
the factor value was used and the hypothetical energy 
consumption for all the buildings within the test area were 
calculated as shown by Map 3 on figure 1 
Energy Map 3 
Legend 
Build geodatabasetest 
  
  
  
  
  
  
  
  
  
squared 
vo Encre Factor deviations 
Build 
p. [368950 | 2196200 | so 525681 a 
Build 
Re | sensa | 5052270 10165756 86735178 
Build | 
3 9823.64 | 5395370 34927512 en 
384236. | 3291264 | 85.657316 | 3007063 
2 g : ( Variance) 
Sm 600.81331 
  
  
  
SSCS 
  
Table 2 Statistic of averaging energy consumption 
The next step performed was based on a decision towards using 
the sample data which falls outside the test area to enrich the 
sample without actually changing the test area when performing 
the other activities and analysis, this is valid as the buildings 
  
  
  
Figure 1 Energy Map of the Test Area 
305