Difference between revisions of "SMT201 AY2019-20G2 Ex2 JerryTohvan"

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== Part 1: Study Area Map Components ==
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== Part 1: Study Area Map Components ==  
The following shows an overview of the study area and its map components we want to consider in this suitability land analysis for the Communicable Disease Quarantine Centre in Bukit Gombak:
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[[File:Overview map.jpg|700px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>FIGURE I</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>Study Area Map Components</span></span></p>
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<br><br>
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Firstly, the above summarises an overview of the study area and its map components we want to consider in this suitability land analysis for the Communicable Disease Quarantine Centre in Bukit Gombak.
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<br><br>
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[[File:Map Target Roads.jpg|600px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  style='color:windowtext'>FIGURE II</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>Target Road Map</span></span></p>
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Figure II shows Bukit Gombak's Road Network that will be accounted in as an Accesibility factor in weighing the location suitability.
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[[File:Map Buildings.jpg|600px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  style='color:windowtext'>FIGURE III</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>Target Building Map</span></span></p>
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Figure III shows Bukit Gombak's Buildings that will be accounted in as an Health Risk factor in weighing the location suitability. We want the Disease Quarantine Centre to be further away from residential in order to ensure safe-zone. Health Risk factor will be prioritised as for the very fact that a Quarantine Centre exists, to ensure no further disease spreading. Bukit Gombak has mostly residential areas in its'subzone.
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[[File:Image.jpg|700px|center]]
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[[File:Map Natural Features.jpg|600px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  style='color:windowtext'>FIGURE III</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>Target Natural Features Map</span></span></p>
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[[File:Buffered Merge Waterway.jpg|500px|center]]
 
  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW OF THEMATIC MAPPING</span></span></p>
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   style='color:windowtext'>Incomplete Waterway Target Inclusion. Applying buffering and merging of polygons in QGIS.</span></span></p>
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Figure IV shows Bukit Gombak's Natural Features that will be accounted in as an Natural Conservation factor in weighing the location suitability. The Natural features includes forest, park, water, and also waterway(canal, drain, stream). Waterway polygon was merged into the natural features in order to account in medium of disease transmission that could potentially cause disease spreading. We account this as risk factor that needed to be considered.
 
<br>
 
<br>
Firstly, the thematic mapping shown in figure 5 represents the default view of the map. School data points, map line and road network are shown on top of the OpenStreetMap.
 
 
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[[File:Map elevation.jpg|600px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>FIGURE V</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  style='color:windowtext'>Digital Elevation Map</span></span></p>
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Figure V shows Bukit Gombak's Elevation Map that will be accounted in as an Economical factor in weighing the location suitability. Most projects will always assess its cost in building. In estimation, slope inclination of about 15 degrees and beyond could add further costs to increase significantly as the risks become greater and the work becomes more difficult to build.
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== Part 2: Study Area Proximity Maps ==
 
== Part 2: Study Area Proximity Maps ==
[[File:FigureVII.png|400px|center]]
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[[File:Proximity Roads.jpg|600px|center]]
 
  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE VIII</span></span></p>
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   style='color:windowtext'>FIGURE VI</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>LAYERS EXPORTED
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   style='color:windowtext'>Accessibility Factor: Road Proximity Map</span></span></p>
</span></span></p>
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After target map elements have been rasterised. We perform the raster proximity distance on its layer and apply binary model classification on all the factor layers. The legend of Proximity map layer indicates that the furthest distance from the roads is 796.304 metres.
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<br>
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<br>
  
The choropleth mapping developed uses these following data and applied techniques:
 
  
==== Sources and Methods ====
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[[File:Proximity Buildings.jpg|600px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  style='color:windowtext'>FIGURE VII</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>Health Risk Factor: Building Proximity Map</span></span></p>
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<br>
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Applying the same methods used on road proximity map, The legend of Proximity map layer indicates that the furthest distance from the buildings is 910 metres.
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<br>
  
==== Data Interpretation ====
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[[File:Proximity Natural Features.jpg|600px|center]]
===== Aged population (+65) in 2010 and 2018 =====
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
[[File:FigureXXVI.jpg|center|800px]]
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXVI</span></span></p>
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   style='color:windowtext'>FIGURE VIII</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF TOTAL AGED POPULATION IN 2010 BY PLANNING AREA
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   style='color:windowtext'>Natural Conservation Factor: Natural Features Proximity Map</span></span></p>
</span></span></p>
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<br>
  
The plotted map above represents the total aged population (+65) in 2010 by each planning area at the macro level. We observe that there is a tendency that more of the aged population tend to reside in the east area, especially Bedok with an approximate figure of 31,720. Next, areas like Tampines, Hougang, Toa Payoh, Ang Mo Kio, and Bukit Merah were already recorded as areas with many older generations residing in 2010. The data classification excludes 0 as we are not interested in areas with unrecorded data as it doesn’t bring a value.
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Next, the Figure VIII shows the Natural Features Proximity Map where the legend of Proximity map layer indicates that the furthest distance from the natural features is 948.117 metres.
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[[File:FigureXXVII.jpg|center|400px]]
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[[File:Slope Layer.jpg|600px|center]]
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXVII</span></span></p>
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   style='color:windowtext'>FIGURE IX</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF TOTAL AGED POPULATION IN 2018 BY PLANNING AREA
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   style='color:windowtext'>Economic Factor: Slope Layer</span></span></p>
</span></span></p>
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Lastly, Figure IX shows the slope layer. The legend of Slope layer shows that the minimum and maximum values of the slope values are 0 and 34.5339 degrees respectively.
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<br>
  
As we all already know that Singapore is one of the countries that is currently facing an aging population as a nation. In Figure XXVII, we can observe that the problem is currently displayed on the macro view of total aged population in 2018 map. We see areas like Woodlands which had a major increase of aged population from 12,580 to 22,040 in the span of 8 years and more such as:
 
-  Yishun, 12,810 in 2010 and 24,720 in 2018.
 
-  Punggol, 2,880 in 2010 and 10,930 in 2018.
 
-  Sengkang, 8,920 in 2010 and 20,400  in 2018.
 
-  Serangoon, 12,590 in 2010 and 20,150 in 2018.
 
-  Sembawang, 3,790 in 2010 and 7,230 in 2018.
 
-  Bishan, 8,850 in 2010 and 14,430 in 2018.
 
-  Toa Payoh, 18,610 in 2010 and 24,030 in 2018.
 
-  Novena, 6,110 in 2010 and 8,130 in 2018.
 
-  Kallang, 14,290 in 2010 and 19,890 in 2018.
 
-  Bukit Merah, 24,010 in 2010 and 31,490 in 2018.
 
-  Queenstown, 19,400 in 2010 and 19,400 in 2018.
 
-  Bukit Panjang, 8,270 in 2010 and 15,210 in 2018.
 
While the above-mentioned planning areas have shown a significant increase there are locations that have been stagnant perhaps due to no recent surveys conducted yet and some areas experiencing decrease of aged population.
 
 
  
[[File:FigureXXVIII.png.jpg|center|400px]]
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== Criterion Scores for Map Components ==
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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[[File:Binary Roads.jpg|500px|center]]
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[[File:Criteria access.jpg|400px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXVIII</span></span></p>
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   style='color:windowtext'>FIGURE X</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF TOTAL AGED POPULATION IN 2010 BY SUBZONE WITH LABEL
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   style='color:windowtext'>Accesibility Factor: Road Binary & Criteria Score</span></span></p>
</span></span></p>
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Figure X includes the accesibility binary model, where we prefer location under 200 m.
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[[File:Binary buildings.jpg|500px|center]]
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[[File:Criteria health risk.jpg|300px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>FIGURE XI</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>Health Risk Factor: Building Binary & Criteria Score</span></span></p>
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The Figure XI includes the health risk binary model where we prefer that buildings are 250 meters away from the Disease Quarantine Centre.
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<br><br>
  
Figure XXVIII shows the total aged population of each subzone to provide a more detailed view in each subzone. Label attribute has been allocated to represent each subzone and easy reference which for the next map screenshots has been disabled for better classification analysis.
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[[File:Binary natural.jpg|500px|center]]
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[[File:Criteria natural conv.jpg|300px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>FIGURE XII</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>Natural Conservation Factor: Natural Features Binary & Criteria Score</span></span></p>
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Next, Figure XII includes the natural conservation risk binary model, where we prefer public natural features and waterways are 200 meters away from the centre.
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[[File:Binary slope.jpg|500px|center]]
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[[File:Criteria Slope.png|300px|center]]
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>FIGURE XIII</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>Economical Factor: Slope Binary & Criteria Score</span></span></p>
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Lastly, Figure XIII includes the economical factor binary model,where we locations where slope inclination is below or equal to 15 degrees.
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<br><br>
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== Analytical Hierarchical Process Input Matrix & Approach ==
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[[File:AHP Scale.jpg|300px|center]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
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  style='color:windowtext'>FIGURE XIV</span></span></p>
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  <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
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  style='color:windowtext'>SCB Associates' AHP Scoring Framework</span></span></p>
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The Analytical Hierachy Process provides a framework to assist the prioritisation of different factors in decision making. The method applies the pairwise comparison by evaluating relative importance of 2 factors. The matrix mathematics was applied in deriving appropriate weighting ratio by ensuring consistency ratio (consistency index/ratio index) is equal or less than 10%. Figure XIV shows the Pairwise comparson matrix's fundamental scale used.
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[[File:XXIX.png|center|400px]]
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[[File:Pairwise Comparison Matrix.jpg|600px|center]]
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXIX</span></span></p>
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   style='color:windowtext'>FIGURE XV</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF TOTAL AGED POPULATION IN 2010 BY SUBZONE WITHOUT LABEL
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   style='color:windowtext'>AHP Scoring Framework for Accounting Factors</span></span></p>
</span></span></p>
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Next, we evaluate each factor with the corresponding grading scale in which the SCB's AHP model with automatically compute its consistency ratio. In summary, the priority that we will aim will be ensuring firstly that health risk is minimal. Second, the economic factor has to be reasonable as government facilities are built upon contribution of Singapore's citizen, thus we need to be accountable with every cent that we spent in developing the quarantine centre. Third, Natural Conservation comes next and Road Accessibility as the last priority. It would have been the best scenario to get the best of everything. However, when dealing in what we called as a VUCA world we live in, we have to be reasonable in determining whats a higher priority to be considered.
[[File:XXX.png|center|400px]]
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
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[[File:AHP Analysis.jpg|400px|center]]
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[[File:AHP Analysis 2.jpg|400px|center]]
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[[File:AHP Ratio.jpg|400px|center]]
 +
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXX</span></span></p>
+
   style='color:windowtext'>FIGURE XVI</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF TOTAL AGED POPULATION IN 2018 BY SUBZONE WITHOUT LABEL THAT SHOWS INCREASE AGED POPULATION
+
   style='color:windowtext'>AHP Results</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
The above reference listed on Figure XVI lists a summary of the AHP result. The desired consistency consistency rating was achieved thus we will proceed by applying this to the Binary model in identifying non-preferable location, good location, and best location based on this weighthing ratio.
 +
<br>
 +
<br>
  
 
+
[[File:Raster calc AHP.jpg|500px|center]]
Figure XXIX shows the total aged population of each subzone to provide a more detailed view in each subzone. Label attribute has been allocated to represent each subzone and easy reference which for the next map screenshots has been disabled for better classification analysis. The red indicated areas predominantly shown significant additional number of aged population.
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
 
 
 
 
[[File:XXXI.png|center|300px]]
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXXI</span></span></p>
+
   style='color:windowtext'>FIGURE XVII</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>LABELLING FOR TOTAL AGED POPULATION BY PLANNING AREA/SUBZONE
+
   style='color:windowtext'>Weighing AHP Scores into QGIS Raster Calculator</span></span></p>
</span></span></p>
 
  
Figure XXXI shows `Label` attribute created for both the aged population distribution by planning area and subzone  for each of the map.
+
<br>
 
+
<br>
===== Proportional of aged population in 2010 and 2018 =====
+
Based on the derived factors ratio, we plot a binary raster model by computing ratio of the different factor binary models. Since each binary factor model uses a binary scale of 0 or 1. By applying the weighting scale, we will achieve a decimal metric of different preference where nearing number 1 will be the best case scenario.
 
+
<br>
[[File:XXXII.png|center|400px]]
+
<br>
 +
[[File:Reclassify binary AHP 1.jpg|500px|center]]
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXXII</span></span></p>
+
   style='color:windowtext'>FIGURE XVIII</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF THE AGED POPULATION PROPORTION IN 2010 BY SUBZONE
+
   style='color:windowtext'>Reclassifying Raster Values using SAGA's Plugin for Degrees of Suitability</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
Lastly, we use the SAGA's Plugin to classify the degree of suitability unto 3 groups. Decimal 0 - 0.59 as not suitable location, 0.6 - 0.69 as good location, and 0.7 - 1 (inclusive) will the the best scenario.
 +
<br>
 +
<br>
  
+
== Recommendation ==
 
+
[[File:Suitability map overview.jpg|500px|center]]
[[File:XXXIII.png|center|400px]]
 
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXXIII</span></span></p>
+
   style='color:windowtext'>FIGURE XIX</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OF THE AGED POPULATION PROPORTION IN 2018 BY SUBZONE
+
   style='color:windowtext'>Suitability Map Overview</span></span></p>
</span></span></p>
 
  
 
+
<br>
We have seen the increase number of population total number for each planning area and subzone. However, we can’t seem to base the conclusion purely based on the increased number of aged population as we have to consider the proportion of non aged population to conclude that the proportion of the aged population is indeed increasing as to the overall population. We measure the proportion of the aged population with the following formulas:
+
<br>
●  Total of aged population in 2010/total population in 2010.
+
In this analysis for the quarantine centre land suitability. We used both the AHP weighting factor and also the simple standardised binary model at the raster calculator process in deriving locations. The simple standardised binary model was generated by performing an XOR method unto the different factor binary layer through multiplication. A case where a pixel (one area measurement of 5 x 5 m) has 0 value on its factor will be derived as a non preferable location. Next, the generated raster was transformed into a polygon through the Raster's Poligonize (Raster to Vector) function. 0 values pixels were eliminated thus only showing polygons that has 1 binary value or we take it as best location where it has meet all the given scenario as indicated by the orange polygon in Figure XIX.  
●  Total of aged population in 2018/total population in 2018.
+
<br><br>
 
+
On the other hand, the AHP weigthed binary model was visualised using a singleband pseudocolour where the best location (Metric value of 3) is indicated by the bright green zone and the good location (Metric location 2) was indicated by the lighter green pixel.
Contrasting FIGURE XXXII and FIGURE XXXIIII we can see that in fact there is a trend of a spreading aged  population especially in areas which used to be classified under 0% to 13.3% in 2010. In 2010, as we see that the increased proportion of the aged population has gone up to predominantly 6.7% to 19.5% with a spread across the subzones.
+
<br>
 
+
<br>
 
+
[[File:Suitability map zoomed.jpg|500px|center]]
===== Percentage change of aged population between 2010 and 2018 =====
 
 
 
Percentage Change only include those that has non 0 value in year 2010. Thus if 2018 have  value it still doesn’t count as the percentage change is not valid,
 
 
 
 
 
 
[[File:XXXIV.png|center|600px]]
 
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXXIV</span></span></p>
+
   style='color:windowtext'>FIGURE XX</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OFPERCENTAGE CHANGE BETWEEN 2010 & 2018 BY SUBZONE WITH LABEL
+
   style='color:windowtext'>Qualitative Analysis: Zooming into Possible Region Using Google's Satelitte</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
+
Using qualitative obsevation method we can observe that most of the greenzone preferable location were located inside the Bukit Panjang Camp. This is a valid location as the location has a rather empty zones isolated from the factors we have mentioned earlier when determining the suitable location for quarantine disease. Figure XX, shows a zoomed overview of the green zone (best location) using the Google Satellite basemap. We can see that the proposed location is quite well fitted as its further away from public population. In addition, higher security level is achieved by building a quarantine zone in a protected and isolated location while still having road accessibility factor.
 
+
<br>
Figure XXXIV, shows the labeled region for the percentage of change between 2010 to 2018.  
+
<br>
  
  
[[File:XXXV.png|center|600px]]
+
[[File:Recommendation Area.jpg|500px|center]]
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>FIGURE XXXV</span></span></p>
+
   style='color:windowtext'>FIGURE XXI</span></span></p>
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   <p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OVERVIEW MAP OFPERCENTAGE CHANGE BETWEEN 2010 & 2018 BY SUBZONE WITHOUT LABEL
+
   style='color:windowtext'>Making Recommendation based on Availibity of Land</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
To conclude the observation from the visualisation given, we apply percentage change of the aged population between 2010 and 2018. We are interested in knowing whether a particular region is experiencing a drop in the number of aged population or an increase. If there are either case we can to know the amount of increase and decrease. Firstly, we derive the percentage change shown in FIGURE XV. By applying negative and positive classification, we can conclude the extend of its aged population changed. While some areas are experiencing major increase of aged population like Alexandra Hill, Lower Seletar, Sembawang East and Pasir Ris Park, there are also areas that hand a major decrease of aged population like Pasir Panjang, Yio Chu Kang, Hougang Central, Seletar, Seletar Hills, Changi West, Changi Point, and Bugis. Interestingly, most subzones are in fact experiencing an increase of percentage change up to 6.7% indicated by the white subzones. Lastly, to end off it is crucial that the government will take into account the distribution of the aged population and redevelopment of programmes for the older generations. I believe that creating a tight-knit community also comes with being intentional in doing activities together, especially with those who are in the same generation. Next, with the current increasing rate of aging population which can be classified as pretty high, government can boost more initiatives in ensuring that we have enough next generations of leaders and citizens to carry on the Singapore successes in its development and economy as a nation.
+
However, there is still a bigger question to be ask in the possibility of this proposal. Will it be possible to integrate a quarantine centre within the Bukit Panjang Basecamp? Would this place our fellow soldiers in risk of being exposed if an unexpected breakout happens? Will political agenda will be a factor in this current quarantine centre proposal even when analysis has shown that its the most compatible location?
 
+
<br>
 
+
<br>
 
+
Another recommendation would be reallocating the bukit panjang camp's greenzone to another isolated zone just for the quarantine zone which will have a direct road network accessibility, thus quarantine zone will not techincally fall inside the bukit panjang camp zone as ensuring proper access will be difficult since both department has different needs of confidentiality and agendas.
===== Exported Maps from Map Composer =====
+
<br>
[[File:XXXVI.png.jpg|center|800px]]
+
<br>
[[File:XXXVII.png.jpg|center|800px]]
 
[[File:XXXVIII.png.jpg|center|800px]]
 
[[File:XXXIX.png.jpg|center|800px]]
 
[[File:XXXX.png.jpg|center|800px]]
 
[[File:XXXXI.png.jpg|center|800px]]
 
[[File:XXXXII.png.jpg|center|800px]]
 
 
 
== References ==
 
[1] https://data.gov.sg/dataset/school-directory-and-information <br>
 
[2] https://data.gov.sg/dataset/master-plan-2014-land-use?resource_id=ea9f3b26-991f-48ea-ab58-e6b6d5fbaade <br>
 
[3] https://data.gov.sg/dataset/national-map-line <br>
 
[4] https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data <br>
 
[5] https://plugins.qgis.org/plugins/GroupStats/  <br>
 
[6] https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea
 

Latest revision as of 10:57, 4 November 2019

Part 1: Study Area Map Components

Overview map.jpg

FIGURE I

Study Area Map Components



Firstly, the above summarises an overview of the study area and its map components we want to consider in this suitability land analysis for the Communicable Disease Quarantine Centre in Bukit Gombak.

Map Target Roads.jpg

FIGURE II

Target Road Map



Figure II shows Bukit Gombak's Road Network that will be accounted in as an Accesibility factor in weighing the location suitability.

Map Buildings.jpg

FIGURE III

Target Building Map



Figure III shows Bukit Gombak's Buildings that will be accounted in as an Health Risk factor in weighing the location suitability. We want the Disease Quarantine Centre to be further away from residential in order to ensure safe-zone. Health Risk factor will be prioritised as for the very fact that a Quarantine Centre exists, to ensure no further disease spreading. Bukit Gombak has mostly residential areas in its'subzone.

Map Natural Features.jpg

FIGURE III

Target Natural Features Map


Buffered Merge Waterway.jpg

FIGURE IV

Incomplete Waterway Target Inclusion. Applying buffering and merging of polygons in QGIS.



Figure IV shows Bukit Gombak's Natural Features that will be accounted in as an Natural Conservation factor in weighing the location suitability. The Natural features includes forest, park, water, and also waterway(canal, drain, stream). Waterway polygon was merged into the natural features in order to account in medium of disease transmission that could potentially cause disease spreading. We account this as risk factor that needed to be considered.

Map elevation.jpg

FIGURE V

Digital Elevation Map




Figure V shows Bukit Gombak's Elevation Map that will be accounted in as an Economical factor in weighing the location suitability. Most projects will always assess its cost in building. In estimation, slope inclination of about 15 degrees and beyond could add further costs to increase significantly as the risks become greater and the work becomes more difficult to build.



Part 2: Study Area Proximity Maps

Proximity Roads.jpg

FIGURE VI

Accessibility Factor: Road Proximity Map



After target map elements have been rasterised. We perform the raster proximity distance on its layer and apply binary model classification on all the factor layers. The legend of Proximity map layer indicates that the furthest distance from the roads is 796.304 metres.


Proximity Buildings.jpg

FIGURE VII

Health Risk Factor: Building Proximity Map



Applying the same methods used on road proximity map, The legend of Proximity map layer indicates that the furthest distance from the buildings is 910 metres.

Proximity Natural Features.jpg

FIGURE VIII

Natural Conservation Factor: Natural Features Proximity Map




Next, the Figure VIII shows the Natural Features Proximity Map where the legend of Proximity map layer indicates that the furthest distance from the natural features is 948.117 metres.


Slope Layer.jpg

FIGURE IX

Economic Factor: Slope Layer




Lastly, Figure IX shows the slope layer. The legend of Slope layer shows that the minimum and maximum values of the slope values are 0 and 34.5339 degrees respectively.


Criterion Scores for Map Components

Binary Roads.jpg
Criteria access.jpg

FIGURE X

Accesibility Factor: Road Binary & Criteria Score



Figure X includes the accesibility binary model, where we prefer location under 200 m.

Binary buildings.jpg
Criteria health risk.jpg

FIGURE XI

Health Risk Factor: Building Binary & Criteria Score



The Figure XI includes the health risk binary model where we prefer that buildings are 250 meters away from the Disease Quarantine Centre.

Binary natural.jpg
Criteria natural conv.jpg

FIGURE XII

Natural Conservation Factor: Natural Features Binary & Criteria Score



Next, Figure XII includes the natural conservation risk binary model, where we prefer public natural features and waterways are 200 meters away from the centre.

Binary slope.jpg
Criteria Slope.png

FIGURE XIII

Economical Factor: Slope Binary & Criteria Score



Lastly, Figure XIII includes the economical factor binary model,where we locations where slope inclination is below or equal to 15 degrees.

Analytical Hierarchical Process Input Matrix & Approach

AHP Scale.jpg

FIGURE XIV

SCB Associates' AHP Scoring Framework



The Analytical Hierachy Process provides a framework to assist the prioritisation of different factors in decision making. The method applies the pairwise comparison by evaluating relative importance of 2 factors. The matrix mathematics was applied in deriving appropriate weighting ratio by ensuring consistency ratio (consistency index/ratio index) is equal or less than 10%. Figure XIV shows the Pairwise comparson matrix's fundamental scale used.

Pairwise Comparison Matrix.jpg

FIGURE XV

AHP Scoring Framework for Accounting Factors



Next, we evaluate each factor with the corresponding grading scale in which the SCB's AHP model with automatically compute its consistency ratio. In summary, the priority that we will aim will be ensuring firstly that health risk is minimal. Second, the economic factor has to be reasonable as government facilities are built upon contribution of Singapore's citizen, thus we need to be accountable with every cent that we spent in developing the quarantine centre. Third, Natural Conservation comes next and Road Accessibility as the last priority. It would have been the best scenario to get the best of everything. However, when dealing in what we called as a VUCA world we live in, we have to be reasonable in determining whats a higher priority to be considered.

AHP Analysis.jpg
AHP Analysis 2.jpg
AHP Ratio.jpg

FIGURE XVI

AHP Results



The above reference listed on Figure XVI lists a summary of the AHP result. The desired consistency consistency rating was achieved thus we will proceed by applying this to the Binary model in identifying non-preferable location, good location, and best location based on this weighthing ratio.

Raster calc AHP.jpg

FIGURE XVII

Weighing AHP Scores into QGIS Raster Calculator



Based on the derived factors ratio, we plot a binary raster model by computing ratio of the different factor binary models. Since each binary factor model uses a binary scale of 0 or 1. By applying the weighting scale, we will achieve a decimal metric of different preference where nearing number 1 will be the best case scenario.

Reclassify binary AHP 1.jpg

FIGURE XVIII

Reclassifying Raster Values using SAGA's Plugin for Degrees of Suitability



Lastly, we use the SAGA's Plugin to classify the degree of suitability unto 3 groups. Decimal 0 - 0.59 as not suitable location, 0.6 - 0.69 as good location, and 0.7 - 1 (inclusive) will the the best scenario.

Recommendation

Suitability map overview.jpg

FIGURE XIX

Suitability Map Overview



In this analysis for the quarantine centre land suitability. We used both the AHP weighting factor and also the simple standardised binary model at the raster calculator process in deriving locations. The simple standardised binary model was generated by performing an XOR method unto the different factor binary layer through multiplication. A case where a pixel (one area measurement of 5 x 5 m) has 0 value on its factor will be derived as a non preferable location. Next, the generated raster was transformed into a polygon through the Raster's Poligonize (Raster to Vector) function. 0 values pixels were eliminated thus only showing polygons that has 1 binary value or we take it as best location where it has meet all the given scenario as indicated by the orange polygon in Figure XIX.

On the other hand, the AHP weigthed binary model was visualised using a singleband pseudocolour where the best location (Metric value of 3) is indicated by the bright green zone and the good location (Metric location 2) was indicated by the lighter green pixel.

Suitability map zoomed.jpg

FIGURE XX

Qualitative Analysis: Zooming into Possible Region Using Google's Satelitte



Using qualitative obsevation method we can observe that most of the greenzone preferable location were located inside the Bukit Panjang Camp. This is a valid location as the location has a rather empty zones isolated from the factors we have mentioned earlier when determining the suitable location for quarantine disease. Figure XX, shows a zoomed overview of the green zone (best location) using the Google Satellite basemap. We can see that the proposed location is quite well fitted as its further away from public population. In addition, higher security level is achieved by building a quarantine zone in a protected and isolated location while still having road accessibility factor.


Recommendation Area.jpg

FIGURE XXI

Making Recommendation based on Availibity of Land



However, there is still a bigger question to be ask in the possibility of this proposal. Will it be possible to integrate a quarantine centre within the Bukit Panjang Basecamp? Would this place our fellow soldiers in risk of being exposed if an unexpected breakout happens? Will political agenda will be a factor in this current quarantine centre proposal even when analysis has shown that its the most compatible location?

Another recommendation would be reallocating the bukit panjang camp's greenzone to another isolated zone just for the quarantine zone which will have a direct road network accessibility, thus quarantine zone will not techincally fall inside the bukit panjang camp zone as ensuring proper access will be difficult since both department has different needs of confidentiality and agendas.