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 thematic mapping developed uses these following data and applied techniques: <br>
 
{| class="wikitable"
 
|-
 
! Data !! Visualisation & Processing Technique
 
|-
 
| General information of schools from the “School Directory and Information” dataset retrieved from data.gov.sg.|| Layer: School
 
  
Symbology: Categorised by `mainlevel_` attribute which indicates if an indicated point belongs to either centralised institute, junior college, mixed level, secondary or primary school. Each category is labeled with following color:<br>
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[[File:Overview map.jpg|700px|center]]
<br>
 
[[File:Fig1.png|300px|center]]<br>
 
 
  <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
 
   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>ROAD SELECTION LINE AND MAP LINE
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   style='color:windowtext'>Study Area Map Components</span></span></p>
</span></span></p>
+
<br><br>
 +
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.
 +
<br><br>
  
 +
[[File:Map Target Roads.jpg|600px|center]]
 +
<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'>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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  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 +
  style='color:windowtext'>Target Road Map</span></span></p>
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<br>
 +
<br>
 +
Figure II shows Bukit Gombak's Road Network that will be accounted in as an Accesibility factor in weighing the location suitability.
 
<br>
 
<br>
Color choices were contrasted differently from other components/layers for easy reference.
 
 
Processing: The initial dataset was geocoded using the MMQIS by its `address` field in order to retrieve `latlong` projection of the data points.
 
 
Data not found: <br>
 
- RAFFLES INSTITUTION, 1 RAFFLES INSTITUTION LANE.<br>
 
- BOWEN SECONDARY SCHOOL ,2 LORONG NAPIRI.
 
 
<br>
 
<br>
|-
 
| “Masterplan 2014 Landuse” dataset retrieved from data.gov.sg.|| Layer: Land Use
 
Symbology: Light Grey simple fill
 
  
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[[File:Map Buildings.jpg|600px|center]]
[[File:Fig2.png|250px|center]]<br>
 
 
  <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'>FIGURE II</span></span></p>
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   style='color:windowtext'>FIGURE III</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;
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>SCALE VISIBILITY
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   style='color:windowtext'>Target Building Map</span></span></p>
</span></span></p>
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<br>
<br><br>
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<br>
Visualisation Rule: The visibility of Land Use layer is automatically displayed as the user zooms in approximately 1-2 times.  
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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.
 +
<br>
 +
<br>
  
|-
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[[File:Map Natural Features.jpg|600px|center]]
| SLA’s National Map Line retrieved from data.gov.sg.  <br> Road Selection Line dataset retrieved from SLA provided by Prof Kam (HandsOnEx1). || Layer: Map Line & Road Network
 
<br>
 
Visualisation Rule:
 
 
[[File:Fig3.png|250px|center]] <br>
 
 
  <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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   style='color:windowtext'>ROAD SELECTION LINE AND MAP LINE
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   style='color:windowtext'>Target Natural Features Map</span></span></p>
</span></span></p>
 
Processing: 2 datasets was used to represent different types of road. The national map line only provides expressway, major roads, international boundary and contour lines, the road selection data provides overall road network in Singapore. The  Map Line layer highlights its road types using the categorisation rule, applying different colours and line width to emphasize type of road. The minor road can be implied by excluding road network that belongs to express way, intersections, and major roads.
 
 
 
|-
 
| “MP14_SUBZONE_NO_SEA_PL” by URA retrieved from data.gov.sg.|| Layer: MP14_SUBZONE_NO_SEA_PL
 
Symbology: Light Brown simple fill
 
 
 
Was included to provide a macro view base layer as an optional display. The layer represents subzones that could be useful in interpreting where road networks or school is located.
 
 
 
OpenStreetMap view could also be used, however the subzone layer better express the subzone boundaries through a simple display.
 
 
 
|}
 
 
<br>
 
<br>
[[File:Image.jpg|700px|center]]
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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>
 +
 
 
<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.
 
 
<br>
 
<br>
[[File:Img2.png|300px|center]]
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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>
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 +
[[File:Map elevation.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
 
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   style='color:windowtext'>FIGURE IV</span></span></p>
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   style='color:windowtext'>FIGURE V</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;
 
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   style='color:windowtext'>MICRO VIEW OF LAND USE VISIBILITY WITH ROAD NETWORKS AND SCHOOL DATA POINTS
+
   style='color:windowtext'>Digital Elevation Map</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
 +
<br>
 +
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.
 +
<br>
 
<br>
 
<br>
The land use data was set with automatic scale visibility, the overall land use layer will only be clearly visible as the user zooms in for interpretation. The land use data provides a micro level data of the indicative polygon of each development land parcel. Thus, there’s no need for this layer to be displayed in a more macro view as lines will not be value-adding to visualisation interpretation.
 
 
<br>
 
<br>
 
Next, the national map line only provides expressway (blue line), major roads (magenta line), international boundary and contour lines (excluded), while the road selection data provides overall road network in Singapore. Thus, achieving an overview of all types of road can be done by overlapping the road networks to retrieve minor road (red line) through overlapping as shown in figure V.
 
 
<br>
 
<br>
  
[[File:Img3.png|700px|center]]
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== Part 2: Study Area Proximity Maps ==
 +
[[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
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>SUBZONE VIEW
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   style='color:windowtext'>Accessibility Factor: Road Proximity Map</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
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.
 +
<br>
 +
<br>
 +
 
  
[[File:Img4.png|700px|center]]
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[[File:Proximity Buildings.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
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>OPENSTREETMAP VIEW
+
   style='color:windowtext'>Health Risk Factor: Building Proximity Map</span></span></p>
</span></span></p>
 
 
 
 
<br>
 
<br>
The `MP14_SUBZONE_NO_SEA_PL` and OpenStreetMap layer (Figure VI & VII) were added as I believe that it might help in terms of data interpretation, eg: finding out where a junior college is located by subzones and its distance to major road where it's usually major road provides better transportation option/accessibility.
 
 
<br>
 
<br>
 +
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.
 
<br>
 
<br>
 
<br>
 
<br>
== Part 2: Choropleth Mapping ==
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[[File:FigureVII.png|400px|center]]
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[[File:Proximity Natural Features.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
 
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   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'>Natural Conservation Factor: Natural Features Proximity Map</span></span></p>
</span></span></p>
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<br>
  
The choropleth mapping developed uses these following data and applied techniques:
+
<br>
 +
<br>
 +
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.
 +
<br>
 +
<br>
  
==== Sources and Methods ====
 
  
{| class="wikitable"
+
[[File:Slope Layer.jpg|600px|center]]
|-
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<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
! Dataset !! Visualisation & Processing Technique
 
|-
 
| “Singapore Residents by Planning Area/Subzone, Age Group and Sex, June 2000 - 2018” from Department of Statistics Singapore.
 
||
 
Layer: respopagsex2000to2018_unfiltered
 
 
 
Processing:
 
1. The initial dataset is the base population data yet to be processed with the map layer data.
 
 
[[File:FigureVIII.png|center|600px]]
 
<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'>FILTERING AGED POPULATION
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   style='color:windowtext'>Economic Factor: Slope Layer</span></span></p>
</span></span></p>
+
<br>
  
2. Layer`respopagsex2000to2018_aged_pop` was achieved by filtering attribute `AG` which represents the age groups.
+
<br>
+
<br>
 +
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.
 +
<br>
 +
<br>
  
[[File:FigureIX.png|center|500px]]
+
 
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
+
== Criterion Scores for Map Components ==
 +
[[File:Binary Roads.jpg|500px|center]]
 +
[[File:Criteria access.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;
 
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   style='color:windowtext'>AGGREGATING DATA USING GROUPSTATS
+
   style='color:windowtext'>Accesibility Factor: Road Binary & Criteria Score</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
Figure X includes the accesibility binary model, where we prefer location under 200 m.
 +
<br><br>
  
+
[[File:Binary buildings.jpg|500px|center]]
[[File:FigureX.png|center|400px]]
+
[[File:Criteria health risk.jpg|300px|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;
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>IMPORTING GROUPSTATS GENERATED CSV USING CUSTOM DELIMITER
+
   style='color:windowtext'>Health Risk Factor: Building Binary & Criteria Score</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
The Figure XI includes the health risk binary model where we prefer that buildings are 250 meters away from the Disease Quarantine Centre.
 +
<br><br>
  
3. Plugin `Group Stats` was used to group by data with a simply drop-and-drag feature. In which could perform operations such as general table operations with group by, selection of columns, and data aggregation. The plugin helps to produce these following files and layers:
+
[[File:Binary natural.jpg|500px|center]]
a. `sum_aged_pop_pa` and `sum_aged_pop_sz` from file `/GroupStats/sum_aged_pop.csv`
+
[[File:Criteria natural conv.jpg|300px|center]]
and `/GroupStats/sum_aged_pop_sz.csv` respectively which was achieved by a sum aggregation of aged population grouped by year and subzones/planning areas.
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
b. Layer `total_population_sv` from file `GroupStats/total_population_sz.csv` was a product of a sum aggregation of all population grouped by year and subzones.
 
 
[[File:FigureXI.png|center|400px]]
 
<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
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
   style='color:windowtext'>DATA OVERVIEW OF IMPORTED GROUPSTATS DATA
+
   style='color:windowtext'>Natural Conservation Factor: Natural Features Binary & Criteria Score</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
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.
 +
<br><br>
  
 
+
[[File:Binary slope.jpg|500px|center]]
+
[[File:Criteria Slope.png|300px|center]]
[[File:FigureXII.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;
 
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   mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
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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'>DATA OVERVIEW OF IMPORTED GROUPSTATS DATA
+
   style='color:windowtext'>Economical Factor: Slope Binary & Criteria Score</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
4. Layer `sum_aged_pop_pa` and `sum_aged_pop_sz` were used as a base data for getting the aged population (+65) in 2010 and 2018 data on each subzones and planning area filtered using the `Time` attribute.
+
Lastly, Figure XIII includes the economical factor binary model,where we locations where slope inclination is below or equal to 15 degrees.
a. Thus, producing layer `sum_aged_pop_2010_pa`, `sum_aged_pop_2018_pa`,`sum_aged_pop_2010_sz` and `sum_aged_pop_2018_sz`.
+
<br><br>
b. `Zone_ID_SZ` and `Zone_ID_PA` were an additional attribute for primary keys needed to join with the subzone and planning area data. We applied expression `upper(“SZ”)` and `upper(“PA”) for an uppercase reference of the attribute included.
+
== Analytical Hierarchical Process Input Matrix & Approach ==
5. We derive the layer `propotion_aged_pop_2010` and `propotion_aged_pop_2018` by layer joining of `total_population_sz` and `sum_aged_pop_sz` through`SZ` attribute as keys and applying `Time` filter for respective layers.
+
[[File:AHP Scale.jpg|300px|center]]
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
[[File:FigureXIII.png|center|400px]]
 
<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
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   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'>PROPORTION FIELD CREATION
+
   style='color:windowtext'>SCB Associates' AHP Scoring Framework</span></span></p>
</span></span></p>
+
<br>
 +
<br>
 +
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.
 +
<br>
 +
<br>
  
 
+
[[File:Pairwise Comparison Matrix.jpg|600px|center]]
 
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
a. Using the `Field Calculator`, we add new attribute `Proportion` by dividing subzone aged population by its subzone total population.
 
 
[[File:FugreXIV.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
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   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'>DERIVING PERCENTAGE CHANGE
+
   style='color:windowtext'>AHP Scoring Framework for Accounting Factors</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
6. Lastly, the `2010_2018_percentage_change` layer was achieved simply by layer joining from `sum_aged_pop_sz_2010` and `sum_aged_pop_sz_2018`. Data clean up was done to replace any `NULL` values to 0. Above figure represents the expression formula used to derive the percentage change from 2010 to 2018.
+
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.
 
+
<br>
 
+
<br>
 
+
[[File:AHP Analysis.jpg|400px|center]]
 
+
[[File:AHP Analysis 2.jpg|400px|center]]
 
+
[[File:AHP Ratio.jpg|400px|center]]
 
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
 
 
|-
 
| Singapore Master Plan 2014 Subzone and Planning Area 2014 boundary data retrieved from data.gov
 
||  
 
 
 
 
 
 
 
1. `SumAgedPopulation2010_PA` layer joined with `sum_aged_pop_2010_pa` by matching attribute `PLN_AREA_N` and `Zone_ID_PA`.
 
a. Symbology (Natural Jenks):
 
 
 
[[File:FigureXV.png|center|400px]]
 
<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
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   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'>CATEGORISATION OF PLANNING AREA SUM AGED POPULATION DATA
+
   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]]
 
+
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
 
 
2. `SumAgedPopulation2018_PA` layer joined with `sum_aged_pop_2018_pa` by matching attribute `PLN_AREA_N` and `Zone_ID_PA`.
 
a. Symbology (Natural Jenks):
 
 
 
[[File:FigureXVI.png|center|400px]]
 
<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
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   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'>CATEGORISATION OF PLANNING AREA SUM AGED POPULATION DATA
+
   style='color:windowtext'>Weighing AHP Scores into QGIS Raster Calculator</span></span></p>
</span></span></p>
 
  
 
+
<br>
3. `SumAgedPopulation2010_SZ` layer joined with `sum_aged_pop_2010_sz` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`.
+
<br>
a. Symbology (Natural Jenks):
+
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:FigureXVII.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;
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   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'>CATEGORISATION OF SUBZONE SUM AGED POPULATION DATA
+
   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>
  
4. `SumAgedPopulation2018_SZ`layer joined with `sum_aged_pop_2018_sz` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`.
+
== Recommendation ==
a. Symbology (Natural Jenks):
+
[[File:Suitability map overview.jpg|500px|center]]
 
[[File:FigureXVIII.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;
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   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'>CATEGORISATION OF SUBZONE SUM AGED POPULATION DATA
+
   style='color:windowtext'>Suitability Map Overview</span></span></p>
</span></span></p>
 
  
5. `ProportionAgedPopulation2010_SZ` layer joined with `propotion_aged_pop_2010` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`.
+
<br>
a. Symbology (Natural Jenks):
+
<br>
 
+
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.  
+
<br><br>
[[File:FigureXIX.png|center|400px]]
+
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.
 +
<br>
 +
<br>
 +
[[File:Suitability map zoomed.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;
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   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'>CATEGORISATION OF SUBZONE PROPORTION AGED POP DATA
+
   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>
 +
<br>
  
6. `ProportionAgedPopulation2018_SZ` layer joined with `propotion_aged_pop_2018` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`.
+
 
a. Symbology (Natural Jenks):
+
[[File:Recommendation Area.jpg|500px|center]]
 
[[File:FigureXX.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;
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   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'>CATEGORISATION OF SUBZONE PROPORTION AGED POP DATA
+
   style='color:windowtext'>Making Recommendation based on Availibity of Land</span></span></p>
</span></span></p>
+
<br>
 
+
<br>
7. `Percentage_Change_SZ` layer joined with `2010_2018_percentage_change` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`.
+
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?
a. Symbology: Below is the configuration used for percentage change of aged population. The legend classification intervals were split into 2 ways, negative changes which represents a decrease change were categorised using an equal distribution from the minimum decrease value of -100% to 0. Next, Natural Breaks (Jenks) were used to classify the 5 next categories for the positive values to indicate. Due to its high variance value, the Jenks classification represents best for this case. Additionally, 2 distinct colours (red and blue) were used to appropriately display the nature of percentage change of the aged population from 2010 to 2018.
+
<br>
[[File:FigureXXI.png|center|500px]]
+
<br>
<p class=MsoNormal align=center style='text-align:center;line-height:normal'><span
+
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.
  lang=EN-GB style='font-size:8.0pt;font-family:"Times New Roman",serif;
+
<br>
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
+
<br>
  style='color:windowtext'>FIGURE XXII</span></span></p>
 
  <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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA
 
</span></span></p>
 
 
[[File:FigureXXII.png|center|500px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXIII</span></span></p>
 
  <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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>3 BASE COLOR PICK FOR SUBZONE PERCENTAGE CHANGE DATA
 
</span></span></p>
 
 
 
 
[[File:FigureXXIII.png|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXIV</span></span></p>
 
  <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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>DATA LABELLING
 
</span></span></p>
 
 
 
For each respective boundary map object, we created `Label` attribute for data visualisation in which was disabled for better macro level view and observation.
 
 
 
 
[[File:FigureXXIV.png|center|200px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXV</span></span></p>
 
  <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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA
 
</span></span></p>
 
 
 
 
 
Enabling each layer’s label can be done via `Layer Properties`.
 
 
 
|}
 
 
 
==== Data Interpretation ====
 
===== Aged population (+65) in 2010 and 2018 =====
 
 
 
[[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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXVI</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
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.
 
 
 
 
 
[[File:FigureXXVII.jpg|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXVII</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
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]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXVIII</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
 
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.
 
 
 
 
 
 
[[File:XXIX.png|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXIX</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
[[File:XXX.png|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXX</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
 
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.
 
 
 
 
 
 
 
[[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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXXI</span></span></p>
 
  <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;
 
  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
 
</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.
 
 
 
===== Proportional of aged population in 2010 and 2018 =====
 
 
 
[[File:XXXII.png|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXXII</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
 
 
[[File:XXXIII.png|center|400px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXXIII</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
 
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:
 
●  Total of aged population in 2010/total population in 2010.
 
●  Total of aged population in 2018/total population in 2018.
 
 
 
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.
 
 
 
 
 
===== 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
 
  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
 
  style='color:windowtext'>FIGURE XXXIV</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
 
 
 
Figure XXXIV, shows the labeled region for the percentage of change between 2010 to 2018.
 
 
 
 
 
[[File:XXXV.png|center|600px]]
 
<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;
 
  mso-fareast-font-family:"Times New Roman";font-variant:small-caps;color:white'><span
 
  style='color:windowtext'>FIGURE XXXV</span></span></p>
 
  <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;
 
  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
 
</span></span></p>
 
 
 
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.
 
 
 
 
 
 
 
===== Exported Maps from Map Composer =====
 
[[File:XXXVI.png.jpg|center|800px]]
 
[[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.