Difference between revisions of "SMT201 AY2019-20G1 EX2 Lin Xing"

From Geospatial Analytics for Urban Planning
Jump to navigation Jump to search
 
(33 intermediate revisions by the same user not shown)
Line 1: Line 1:
  
== Part One ==
+
== Objective ==
=== Primary School Allocation According to Subzones ===
+
To identify a suitable location for building a National Communicable Disease Quarantine Centre, by using of analytical hierarchy process.</br>
[[File:Primary School-linxing.png|600px|center]]
 
In order to have more information about Singapore School, three different layers were made. Apparently, the further from central of Singapore, the more numbers of primary school are allocated.<br>
 
  
=== Secondary School Allocation According to Subzones ===
+
==Part 1: Visual Analysis on Map View ==
[[File:Secondary-linxing.png|600px|center]]
+
[[File:Visual analysis natural.jpg|400px]]
As you can notice from graph above, allocation of Secondary school is pretty much similar to Primary School. In certain extent, Singapore has put in a lot of efforts to make sure basic education is available for all kids.
+
[[File:Visual analysis health risk factor.jpg|400px]]
 +
[[File:Visual analysis accessibility factor.jpg|400px]]
 +
[[File:Visual analysis Elevation.jpg|400px]] <br />
  
=== Secondary School Allocation According to Subzones ===
+
=== Natural Factor ===
[[File:JC-linxing.png|600px|center]]
+
Ultimately, "water" and "park" are locating near to residents area and they are represented by blue and green color respectively. And "forest" is located at the right side of the graph, and it is represented by orange color. Due to majority of the natural features are near to residential area. Thus, natural factor should be considered together with residential area.
From the thematic map above, you can notice all JCs are located nearer to central area. Each subzones has same number of JCs.
 
  
Overall, we can tell that, Singapore has allocated schools well for its residents. Allocation of primary schools and secondary schools are able to fulfill kid's basic education. On top of that, adding up other types of schools, it will not be too crowded as seen from map.
+
=== Health Risk Factor ===
 +
Avoiding residential area is a most important factor to consider. Due to the possibility of leakage of dangerous virus from building and cause disaster. From the generated graph, we can easily notice that, after exclude residential area (Green color polygon the graph). It still has quite large of the space for us to consider while propose (mainly bottom and right part of the graph). As mentioned in natural factor part, natural features are near to residential area. Thus, natural factor is weighted as 1 (least important), but health risk factor is weighted as 9 (most important).
  
=== Road Network System ===
+
=== Accessibility Factor ===
[[File:road-linxing.png|600px|center]]
+
Finding areas that near to service and track roads will ensure the transportation of construction material, and lead to earlier TOP of the building.and  As mentioned above, after removing of natural features and residential area, we will only have bottom part of the Gombak to consider. However, as the service road (White pipe line) and track road (white pipe line) is not reaching to every corner of Gombak. Therefore, we should try to consider the area that beside service roads and track road.
In order to differentiate types of roads in Singapore, different colors were presented in the map. As different colors are noticeable, it could effectively help the readers knowing the roads without referring to legend too much.
 
  
=== 2014 Land use ===
+
=== Economic Factor ===
[[File:Landuse-linxing.png|600px|center]]
+
From fourth graph. the high elevated area are indicated in blue and light green color, and more flat area re indicated in orange and red color. However, those flat areas are more or less been developed. Thus we may have to consider high elevation areas. And due to there are not much changes we can do, economic factor is weighted as 4 in this case.
Different colors were used to categorize different purpose of land using. Even though there are too many category in the thematic map. We can still observe that:
 
    Most part of land are: 1. Open Space 2. Residential 3. Business
 
  
== Part Two ==
 
  
=== Age Population 2010 ===
+
==Part 2: Proximity ==
[[File:Aged-2010-linxing1.png|600px|center]]
+
[[File:Prox natural1.jpg|400px]]
 +
[[File:Prox building.jpg|400px]]
 +
[[File:Prox road.jpg|400px]]
 +
[[File:Prox slope1.jpg|400px]]
  
[[File:Proportion aged65+ -2010-linxing1.png|600px|center]]
+
Proximity measures the distance to different layers. It provides us the summarized information regarding the layers.
 +
=== Natural Factor ===
 +
Maximum = 852.76 <br />
 +
Mean = 257.74 <br />
 +
Minimum = 0
  
=== Age Population 2018 ===
+
=== Health Risk Factor ===
[[File:Aged-2018-linxing1.png|600px|center]]
+
Maximum = 1146.30 <br />
 +
Mean = 358.62 <br />
 +
Minimum = 0
  
[[File:Proportion aged65+ -2018-linxing1.png|600px|center]]
+
=== Accessibility Factor ===
 +
Maximum = 750 <br />
 +
Mean = 146.20 <br />
 +
Minimum = 0
  
=== Age Population 2018 ===
+
=== Economic Factor ===
[[File:change of aged population-linxing1.png|600px|center]]
+
Maximum = 36.88 <br />
 +
Mean = 9.06 <br />
 +
Minimum = 0 <br />
  
First of all, all the data above are categorized by Subzone. In order to let people easily observe the difference between 2010 and 2018, I used same theme color. However, not only the different between colors, another graph is added at the last to tell the percentage of changes from 2010 to 2018. As you may notice, most of subzones are filles in deep red color. In other words, they had increased more than 50% in aged population.
+
Apparently, four proximity layers are providing measurements in different scale. In order to use AHP calculation, we need to standardize four different layers.
 +
Beside that, we may need to bear in mind, the propose location should be far away from natural features and residential area.
 +
 
 +
== Part 3: Standardization ==
 +
[[File:S natural.jpg|400px]]
 +
[[File:S buildings.jpg|400px]]
 +
[[File:S road.jpg |400px]]
 +
[[File:S elevation.jpg|400px]] <br />
 +
 
 +
Due to we have observed the measurements are in different unit from proximity layers. Thus, the Min-Max method is used The Min-Max method is used to standardize the four proximity layers.
 +
As the result. The first and second graphs in part three are oppositely against part two's layer. and the reason has been mentioned above. Thus, the formula was used to standardize for natural features and residential areas are: 1-"natural"/(Max"natural"-Min"natural") and 1-"residential"/(Max"residential"-Min"residential").
 +
The third and fourth graph are similar to part two's third and fourth graph. Hence, formula: "accessibility"/(Max"accessibility" - Min"accessibility") and "economic"/(Max"econmic" - Min"economic") will be used
 +
Generally, for four standardized graphs. The blue or light green color is not recommended. As contrary, The red and orange color is more recommended.
 +
 
 +
 
 +
 
 +
== Part 4: AHP Calculation ==
 +
 
 +
[[File:Calculated weightage.png|600px|centre]]
 +
[[File:AHP calculation.png|600px|centre]]
 +
 
 +
AHP: as explained in part one. I had weighted four factors differently. And eventually, four factors have four different scores.
 +
The scored is further used to calculate by raster calculator and the formula is: <br />
 +
("s_natural@1"*0.625)+("s_buildings@1"*0.111)+("s_road@1"*0.185)+("s_slope@1"*0.079) <br />
 +
 
 +
== Conclusion ==
 +
[[File:Output.jpg |600px|centre]]
 +
[[File:Recommendation.jpg|600px|centre]]
 +
Ultimately, The area is highlighted in brown in graph recommendation is suitable. And it is indicated by QGiS software that area in brown color has more than 10,000m2. However, I will still recommend the area that near to the road. So that we can save more cost.

Latest revision as of 23:32, 10 November 2019

Objective

To identify a suitable location for building a National Communicable Disease Quarantine Centre, by using of analytical hierarchy process.

Part 1: Visual Analysis on Map View

Visual analysis natural.jpg Visual analysis health risk factor.jpg Visual analysis accessibility factor.jpg Visual analysis Elevation.jpg

Natural Factor

Ultimately, "water" and "park" are locating near to residents area and they are represented by blue and green color respectively. And "forest" is located at the right side of the graph, and it is represented by orange color. Due to majority of the natural features are near to residential area. Thus, natural factor should be considered together with residential area.

Health Risk Factor

Avoiding residential area is a most important factor to consider. Due to the possibility of leakage of dangerous virus from building and cause disaster. From the generated graph, we can easily notice that, after exclude residential area (Green color polygon the graph). It still has quite large of the space for us to consider while propose (mainly bottom and right part of the graph). As mentioned in natural factor part, natural features are near to residential area. Thus, natural factor is weighted as 1 (least important), but health risk factor is weighted as 9 (most important).

Accessibility Factor

Finding areas that near to service and track roads will ensure the transportation of construction material, and lead to earlier TOP of the building.and As mentioned above, after removing of natural features and residential area, we will only have bottom part of the Gombak to consider. However, as the service road (White pipe line) and track road (white pipe line) is not reaching to every corner of Gombak. Therefore, we should try to consider the area that beside service roads and track road.

Economic Factor

From fourth graph. the high elevated area are indicated in blue and light green color, and more flat area re indicated in orange and red color. However, those flat areas are more or less been developed. Thus we may have to consider high elevation areas. And due to there are not much changes we can do, economic factor is weighted as 4 in this case.


Part 2: Proximity

Prox natural1.jpg Prox building.jpg Prox road.jpg Prox slope1.jpg

Proximity measures the distance to different layers. It provides us the summarized information regarding the layers.

Natural Factor

Maximum = 852.76
Mean = 257.74
Minimum = 0

Health Risk Factor

Maximum = 1146.30
Mean = 358.62
Minimum = 0

Accessibility Factor

Maximum = 750
Mean = 146.20
Minimum = 0

Economic Factor

Maximum = 36.88
Mean = 9.06
Minimum = 0

Apparently, four proximity layers are providing measurements in different scale. In order to use AHP calculation, we need to standardize four different layers. Beside that, we may need to bear in mind, the propose location should be far away from natural features and residential area.

Part 3: Standardization

S natural.jpg S buildings.jpg S road.jpg S elevation.jpg

Due to we have observed the measurements are in different unit from proximity layers. Thus, the Min-Max method is used The Min-Max method is used to standardize the four proximity layers. As the result. The first and second graphs in part three are oppositely against part two's layer. and the reason has been mentioned above. Thus, the formula was used to standardize for natural features and residential areas are: 1-"natural"/(Max"natural"-Min"natural") and 1-"residential"/(Max"residential"-Min"residential"). The third and fourth graph are similar to part two's third and fourth graph. Hence, formula: "accessibility"/(Max"accessibility" - Min"accessibility") and "economic"/(Max"econmic" - Min"economic") will be used Generally, for four standardized graphs. The blue or light green color is not recommended. As contrary, The red and orange color is more recommended.


Part 4: AHP Calculation

Calculated weightage.png
AHP calculation.png

AHP: as explained in part one. I had weighted four factors differently. And eventually, four factors have four different scores. The scored is further used to calculate by raster calculator and the formula is:
("s_natural@1"*0.625)+("s_buildings@1"*0.111)+("s_road@1"*0.185)+("s_slope@1"*0.079)

Conclusion

Output.jpg
Recommendation.jpg

Ultimately, The area is highlighted in brown in graph recommendation is suitable. And it is indicated by QGiS software that area in brown color has more than 10,000m2. However, I will still recommend the area that near to the road. So that we can save more cost.