Difference between revisions of "SMT201 AY2019-20G1 Ex2 Lim Shen Jie"

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(Created page with "== Part 1: Thematic Mapping == === Schools === 800px|thumb|center|Schools ==== Description ==== The cartographic technique used to categor...")
 
 
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== Part 1: Thematic Mapping ==
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== Part 1: Standard view of study areas ==
=== Schools ===
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[[File:LSJ Standard four.png|800px|thumb|center|Standard view of study areas]]
[[File:Map1 schools zoomed out.png|800px|thumb|center|Schools]]
 
 
==== Description ====
 
==== Description ====
The cartographic technique used to categorize the schools by their main_level was hue. That is because the main_level attribute is a nominal data. Point symbols are the best for schools because it would be able to pinpoint their exact location.
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1. Buildings - Classified buildings by color into clinic, construction, garage, place of worship, public, residential, train station, and others. <br/>
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2. Natural - Classified natural as one single color.<br/>
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3. Roads - Classified roads into service and track. Any other type of roads were excluded from the study.<br/>
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4. Elevation - Classified the elevation into colors, representing different heights<br/>
  
===== Sources =====
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== Part 2: Raster view of the study areas ==
1. [https://data.gov.sg/dataset/master-plan-2014-land-use/ Master Plan 2014 Land Use]
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[[File:LSJ Raster four view.png|800px|thumb|center|Raster view of the study areas]]
2. [https://data.gov.sg/dataset/school-directory-and-information School Directory and Information]
 
 
 
=== Roads ===
 
[[File:Map2 roads zoomed out.png|800px|thumb|center|Roads]]
 
 
==== Description ====
 
==== Description ====
As the roads were not categorized by their road type, a calculated field was needed to generate the road type for the roads based on the name of the road. The cartographic technique used to categorize the roads by the RD_DESC was hue. That is because the RD_DESC data is nominal. To represent the roads on the map, lines are the best way to pinpoint the exact location of the roads.
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1. Buildings - Classified proximity by color. Numbers are in metres. <br/>
 
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2. Natural - Classified proximity by color. Numbers are in metres.<br/>
===== Sources =====
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3. Roads - Classified proximity by color. Numbers are in metres.<br/>
1. [https://www.mytransport.sg/content/dam/datamall/datasets/Geospatial/RoadSectionLine.zip/ Road Section Line from DataMall]
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4. Elevation - Classified proximity by color. Numbers are in metres.<br/>
2. [https://data.gov.sg/dataset/master-plan-2014-land-use/ Master Plan 2014 Land Use]
 
 
 
=== Land Usage ===
 
[[File:Map3 Land usage zoomed out.png|800px|thumb|center|Land Use]]
 
  
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== Part 3: Raster view showing the criterion scores of the study areas ==
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[[File:LSJ Minmax.png|800px|thumb|center|Raster view showing the criterion scores of the study areas]]
 
==== Description ====
 
==== Description ====
The cartographic technique used to categorize the land usage was hue. That is because the land use attribute is a nominal. Areas is the best way to to pinpoint their exact location, because every piece of land has a unique shape.
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1. Buildings - Classified proximity by color. Numbers are standardized by reclassifying by table. <br/>
 
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2. Natural - Classified proximity by color. Numbers are standardized using reclassifying by table.<br/>
== Part 2 Choropleth Mapping ==
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3. Roads - Classified proximity by color. Numbers are standardized using reclassifying by table.<br/>
=== 2010 Aged population 65+ years old ===
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4. Elevation - Classified proximity by color. Numbers are standardized using reclassifying by table.<br/>
[[File:Map4 2010 population aged 65.png|800px|thumb|center|Schools]]
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== Part 4: AHP tables ==
=== 2018 Aged population 65+ years old ===
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[[File:LSJ AHP3.JPG|800px|thumb|center|Pair-wise comparison]]
[[File:Map5 2018 population aged 65.png|800px|thumb|center|Schools]]
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[[File:LSJ AHP column totals2.JPG|800px|thumb|center|Resulting weightage]]
 
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[[File:LSJ Consistency check2.JPG|800px|thumb|center|Consistency check]]
 
==== Description ====  
 
==== Description ====  
The number of people that were older than 65 years increased from 2010 to 2018
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1. Health Risk - It is the most important factor to take into consideration as this will affect people staying the area, thus it is scored very highly as compared to the other 3 factors. <br/>
=== 2010 Proportion of aged population 65+ years old ===
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2. Accessibility - It is the second most important factor. Accessibility to the site would affect the cost required to transport the construction tools and materials for the new building. Making it cost-effective would be important.<br/>
[[File:Map7 2010 proportion.png|800px|thumb|center|Schools]]
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3. Slope angle - As mentioned, slope angle is the third most important factor. Building on steep angle would increase costs of construction and potentially increase the duration of construction. Thus, ensuring the project is finished quickly and cost-effectively would be important.<br/>
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4. Natural Conservation - it is the least important factor compared to the others. Failure to consider the presence of forests and lakes would endanger the plants and animals in these habitats. However, the costs of these damages are minor compared to the previously mentioned factors. Thus, it's score is relatively low compared to the rest.<br/>
  
=== 2018 Proportion of aged population 65+ years old ===
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== Part 5: Suitable land ==
[[File:Map6 2018 proportion of people 65.png|800px|thumb|center|Schools]]
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[[File:LSJ Suitable land2.png|800px|thumb|center|from suitable_land layer, RankedwithAHP layer, Gombak_Outline layer]]
The proportion of people that were older than 65 years old as compared to the rest of the population increased in some areas of Singapore from 2010 to 2018.
 
 
 
=== Percentage of aged population change from 2010 to 2018 ===
 
[[File:Map8 percentage.png|800px|thumb|center|Schools]]
 
Overal, majority of the subzones in Singapore had an increase in the aged people.
 
  
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==== Discussion ====
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[[File:LSJ Raster calculation3.JPG|800px|thumb|center|Raster calculation by rating model]]
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The raster layer showing the suitable plot of land uses a rating model to calculate the score of every part of the land.
  
==== Discussion ====
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The purple marked zone is the most suitable land lot to build the structure. The area is scored roughly between 0.65 to 0.8 depending on which part of the area that we're inspecting. The land is roughly 72,000 square metres in area. Thus, there should be enough space to build the new Quarantine Centre.
===== Classification =====
 
  
==== Sources ====
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== Sources ==  
1. [https://data.gov.sg/dataset/master-plan-2008-subzone-boundary-no-sea/ Master Plan 2008 Subzone Boundary (No Sea)]  
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1. [https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea Master Plan 2014 Subzone (No Sea)] <br/>
2. [https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea/ Master Plan 2014 Subzone Boundary (No Sea)]
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2. [https://www.bbbike.org/Singapore/ BBBike@Singapore]<br/>
3. [https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data/ Singapore Residents by Planning Area/Subzone, Age Group and Sex, June 2000 - 2018]
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3. [https://search.earthdata.nasa.gov/search?m=-7.175!25.59375!1!1!0!0%2C2 ASTER Global Digital Elevation Model (GDEM) dataset]<br/>

Latest revision as of 22:18, 10 November 2019

Part 1: Standard view of study areas

Standard view of study areas

Description

1. Buildings - Classified buildings by color into clinic, construction, garage, place of worship, public, residential, train station, and others.
2. Natural - Classified natural as one single color.
3. Roads - Classified roads into service and track. Any other type of roads were excluded from the study.
4. Elevation - Classified the elevation into colors, representing different heights

Part 2: Raster view of the study areas

Raster view of the study areas

Description

1. Buildings - Classified proximity by color. Numbers are in metres.
2. Natural - Classified proximity by color. Numbers are in metres.
3. Roads - Classified proximity by color. Numbers are in metres.
4. Elevation - Classified proximity by color. Numbers are in metres.

Part 3: Raster view showing the criterion scores of the study areas

Raster view showing the criterion scores of the study areas

Description

1. Buildings - Classified proximity by color. Numbers are standardized by reclassifying by table.
2. Natural - Classified proximity by color. Numbers are standardized using reclassifying by table.
3. Roads - Classified proximity by color. Numbers are standardized using reclassifying by table.
4. Elevation - Classified proximity by color. Numbers are standardized using reclassifying by table.

Part 4: AHP tables

Pair-wise comparison
Resulting weightage
Consistency check

Description

1. Health Risk - It is the most important factor to take into consideration as this will affect people staying the area, thus it is scored very highly as compared to the other 3 factors.
2. Accessibility - It is the second most important factor. Accessibility to the site would affect the cost required to transport the construction tools and materials for the new building. Making it cost-effective would be important.
3. Slope angle - As mentioned, slope angle is the third most important factor. Building on steep angle would increase costs of construction and potentially increase the duration of construction. Thus, ensuring the project is finished quickly and cost-effectively would be important.
4. Natural Conservation - it is the least important factor compared to the others. Failure to consider the presence of forests and lakes would endanger the plants and animals in these habitats. However, the costs of these damages are minor compared to the previously mentioned factors. Thus, it's score is relatively low compared to the rest.

Part 5: Suitable land

from suitable_land layer, RankedwithAHP layer, Gombak_Outline layer

Discussion

Raster calculation by rating model

The raster layer showing the suitable plot of land uses a rating model to calculate the score of every part of the land.

The purple marked zone is the most suitable land lot to build the structure. The area is scored roughly between 0.65 to 0.8 depending on which part of the area that we're inspecting. The land is roughly 72,000 square metres in area. Thus, there should be enough space to build the new Quarantine Centre.

Sources

1. Master Plan 2014 Subzone (No Sea)
2. BBBike@Singapore
3. ASTER Global Digital Elevation Model (GDEM) dataset