Difference between revisions of "SMT201 AY2019-20T1 EX1 Peh Jin An"

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===Project Overview===
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===School Distribution===
[[File:gombakjin.png|border|center|800x800px|]]
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[[File:School_Distribution.png|border|center|800x800px|]]
In search of location most suitable for building a National Communicable Disease Quarantine Center in Gombak, this project aims to achieve the given task based on the 4 major factors;
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I have decided to use the planning area layer alongside the education layer so that users will be able to locate schools in Singapore. I have also use 4 different colours to represent the 4 different levels of school. Purple representing Junior Colleges and Centralised Institutions, orange representing mixed level schools, red representing secondary schools and finally green representing primary schools. This way users will be able to differentiate the types of schools in its location. I have also used SVG icons to represent the schools to ensure that the icons would not be pixelized when zoomed into.
The Economic, Accessibility, Health Risk and Natural Conservation factors.
 
  
===Features of Gombak===
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===Singapore Road Sections===
[[File:Jin1.png|border|center|800x800px|]]
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[[File:Singapore_Road_Sections(Updated).png|border|center|800x800px|]]
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In this question, I have categorised the roads into 4 main categories. Expressways, major roads, minor roads and local access. This way, the viewer would be able to tell where the roads are in a glance. I have used the filter function on the attribute table for the mentioned attributes to attain the following results.
  
<b>Health Risk</b>
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===Singapore Land Use===
<i>Refer to the screenshot on the top left</i> </br>
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[[File:Singapore Land Use.png|border|center|800x800px|]]
With the main objective of having a site that is quarantined from the contagious disease, housing areas and offices in Gombak should be located further away the Communicable Disease Quarantine Centre. This reduces the opportunities of germs reaching the site through human interaction, wind, objects exposed to the disease etc.
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I have decided to utilize the LU_Desc column from the attribute table as it best fulfils the requirements of the question. I have classified the map into 4 main categories.  
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Blue representing the business and commercial parts
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Orange representing residential and recreational
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Green representing agriculture, open spaces, parks and reserved sites
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Turquoise representing waters
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White representing religious buildings
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Black representing cemeteries
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Pink representing educational institutions
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Red representing health and medical care
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Light green representing transport means
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I have classified them into these categories as I believe it has value when it comes to planning and understanding our land usage.
  
<b>Accessibility</b>
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===Singapore Aged Population for 2010 and 2018===
<i>Refer to the screenshot on the top right</i> </br>
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[[File:Singapore Aged Population 2010.png|border|center|800x800px|]]
The service roads and tracks of Gombak will determine if the Communicable Disease Quarantine Centre is easily accessible. With heavy construction expected to take place during the construction stage, it is vital that the selected site is close to these service roads and tracks.  
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[[File:Singapore Aged Population 2018.png|border|center|800x800px|]]
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This map shows the classification of subzones based on Singapore’s aged population. The higher the number of aged populations, the darker the colour generated. In this case, the 2010 map will reflect a darker red while the 2018 will reflect a darker green. It was noticeable that some subzones that didn’t have darker shades in the 2010 map have emerged in the 2018 map. In addition, the values for the classes increased in the 2018 map, as compared to the 2010 map. This implies that the number of aged people living in those subzones has increased during these 8 years; and since there is a greater number of subzones that is coloured in 2018 as compared to 2010, this presents the trend of an aging population in Singapore.
  
<b>Natural Conservation</b>
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===Singapore Aged Population Proportions for 2010 and 2018===
<i>Refer to the screenshot on the bottom left</i> </br>
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[[File:Proportional of Aged Population in 2010.png|border|center|800x800px|]]
In effort to conserve Singapore’s natural grounds, the Communicable Disease Quarantine Centre should be located away from the forested land, parks and waterways in Singapore.  
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[[File:Proportional of Aged Population in 2018.png|border|center|800x800px|]]
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I added a new column for both the 2010 and 2018 data files for the aged populations in the attributes table for citizens aged 65 and above. I also subsequently added the total populations column for both 2010 and 2018. Note that all these attributes have been added into the MP14 Web layer. I went on to calculate proportion using the field calculator by putting the dividing the total count of citizens aged over 65 for both years over the total population.
  
<b>Economic</b>
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===Percentage Change of Age Population between 2010 and 2018===
<i>Refer to the screenshot on the bottom right</i> </br>
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[[File:Percentage change of Aged Population between 2010 and 2018.png|border|center|800x800px|]]
The different steepness levels of ground around Gombak will determine the Communicable Disease Quarantine Centre’s overall development cost. With step slope constructions tending to involve more ground filling, cutting and levelling, having the site built on slopes that are less steep is recommended.
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For percentage, I added a new column for both the 2010 and 2018 data files for the aged populations in the attributes table for citizens aged 65 and above. I also subsequently added the total populations column for both 2010 and 2018. Note that all these attributes have also been added into the MP14 Web layer. I then went on to calculate the proportion by subtracting the differences between both files and dividing it over the 2010 value and multiplying the answer by 100. I then went on to render the data onto the map. Analyzing the map's data, the darker shades show a large percentage increase in the number of people aged 65 and above living in those subzones, while the lightest shade shows a percentage decrease in the number of people aged 65 and above living in those same subzones. Since there is a significant number of subzones with darker shades as compared to the first shade, it implies that the number of aged people living in these areas have had a increase, or even a significant amount of increase.
  
===Proximity of Gombak Features===
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===Missing Values===
[[File:Jin2.png|border|center|800x800px|]]
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I have assumed for '-' values to be 0
<b>Economic</b> <i>Refer to the screenshot on the bottom left</i></br>
 
In Gombak, the steepness of slopes varies between a range of 0 to 36.4308. This can be visualised with the colour closer to black indicating a closer distance from the slope.
 
 
 
<b>Accessibility</b>
 
<i>Refer to the screenshot on the top left</i></br>
 
In Gombak, the distances from the roads varies between a range of 0 to 708.678. This can be visualised with the colour closer to black indicating a closer distance from the roads
 
 
 
<b>Natural Conservation</b> <i>Refer to the screenshot on the bottom left</i> </br>
 
In Gombak, the distances from the natural places varies between the range of 0 to 863.669. This can be visualised with the colour closer to black indicating a closer distance from nature.
 
 
 
<b>Health Risk</b> <i>Refer to the screenshot on the top left</i> </br>
 
In Gombak, the distances from the buildings varies between the range of 0 to 826.62. This can be visualised with the colour closer to black indicating a closer distance from buildings.
 
 
 
===Standardization of Gombak Features===
 
<b>Reasons for Standardizing</b> </br>
 
[[File:janminmax.png|border|center|400x400px|]] </br>
 
Before deriving the Criterion Score, I have standardized the proximity layers of in the above section to be of the same scale. Unlike the accessibility, natural conservation and health risk factors that measures in meters, the economic factor based on the steepness of slopes measures in degrees. Hence, to standardize the importance into its scores, I have used 1 of the 3 standardization methods to achieve that.
 
 
 
 
 
[[File:Jin3.png|border|center|800x800px|]]
 
After doing so, I proceeded to standardize the ranges amongst all 4 maps; each having an equal interval score of 0.2. By theory, the score of a undesirable site has a color shade of red in the above diagram, with blue representing the more desirable site. In summary, the further the distance, the higher the score. </br>
 
 
 
Not forgetting the requirements of site, it is vital that the site is build far from buildings and nature. It is also crucial for the site to be built far from roads and slopes. </br>
 
 
 
Hence, before the generation of the combined layers containing the combined scores, I proceeded with the following formula:( (1-Standardized Building/Slope) * AHP Weightage)
 
 
 
===Analytical Hierarchical Process(AHP) Input Matrix and Result Report===
 
Before proceeding with the AHP process, I first ranked the importance of each factors.</br>
 
My Rankings are as follows: </br>
 
1) Health Risk (Buildings)</br>
 
2) Natural Conservation</br>
 
3) Accessibility (Roads)</br>
 
4) Economic (Slopes) </br>
 
 
 
[[File:ahpscale.png|border|centre|400x400px|]]
 
 
 
[[File:AHP1jan.png|border|centre|800x800px|]]
 
My Reasons for the rankings: </br>
 
1) Health Risk (Buildings) - With the entire purpose of a quarantine being far from infected places, the ranking for this factor was easy to decide on.</br>
 
2) Natural Conservation - The choice of choosing between natural conservation and accessibility was difficult. However, having natural conservation is ultimately more important than temporary convenience of roads.</br>
 
3) Accessibility (Roads) -  </br>
 
4) Economic (Slopes) - With the main concern being the cost, I personally feel that it is of least importance in comparison to the health of the citizens</br>
 
 
 
[[File:AHPjan.png|border|centre|800x800px|]] </br>
 
 
 
With the above screenshot of the AHP requirements, I proceeded to do a trial and error way to achieve the consistency check of <10%, with the value of my input based of my the rankings I mentioned above. Upon doing so, a consistency check of 5% was achieved, reflecting that the analysis is consistent and rationale.
 
 
 
===The Outcome===
 
[[File:outcomejan.png|border|center|800x800px|]]
 
After using Raster Calculator to combine the scores of the 4 factors into one layer, the above screenshot reflects the outcome.
 
 
 
[[File:vectorizedjan.png|border|center|800x800px|]]
 
I then proceeded to vectorise the combined raster layer. After which, using field calculator, I added a column reflecting the area of the sites, proceeded to categorize the vector map into a computer generated column named "DN". </br>
 
 
 
With the values of column "DN" being in the form of binary (1,0), I proceeded to categorize the vector map using the "DN" column. The value 0 reflecting the undesirable land and 1 reflecting suitable land use.
 
 
 
[[File:proofjan.png|border|center|800x800px|]]
 
Hence, to end off the project, the above screenshot indicates that the selected area has an area of 64975m2, fulfilling the requirement of having a contiguous area of at least 10,000m2.
 
  
 
===Sources===
 
===Sources===
Data.gov, BBBike@Singapore, EarthData Search
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Data.gov
 +
Sing Stat

Latest revision as of 21:19, 2 December 2019

School Distribution

School Distribution.png

I have decided to use the planning area layer alongside the education layer so that users will be able to locate schools in Singapore. I have also use 4 different colours to represent the 4 different levels of school. Purple representing Junior Colleges and Centralised Institutions, orange representing mixed level schools, red representing secondary schools and finally green representing primary schools. This way users will be able to differentiate the types of schools in its location. I have also used SVG icons to represent the schools to ensure that the icons would not be pixelized when zoomed into.

Singapore Road Sections

Singapore Road Sections(Updated).png

In this question, I have categorised the roads into 4 main categories. Expressways, major roads, minor roads and local access. This way, the viewer would be able to tell where the roads are in a glance. I have used the filter function on the attribute table for the mentioned attributes to attain the following results.

Singapore Land Use

Singapore Land Use.png

I have decided to utilize the LU_Desc column from the attribute table as it best fulfils the requirements of the question. I have classified the map into 4 main categories. Blue representing the business and commercial parts Orange representing residential and recreational Green representing agriculture, open spaces, parks and reserved sites Turquoise representing waters White representing religious buildings Black representing cemeteries Pink representing educational institutions Red representing health and medical care Light green representing transport means I have classified them into these categories as I believe it has value when it comes to planning and understanding our land usage.

Singapore Aged Population for 2010 and 2018

Singapore Aged Population 2010.png
Singapore Aged Population 2018.png

This map shows the classification of subzones based on Singapore’s aged population. The higher the number of aged populations, the darker the colour generated. In this case, the 2010 map will reflect a darker red while the 2018 will reflect a darker green. It was noticeable that some subzones that didn’t have darker shades in the 2010 map have emerged in the 2018 map. In addition, the values for the classes increased in the 2018 map, as compared to the 2010 map. This implies that the number of aged people living in those subzones has increased during these 8 years; and since there is a greater number of subzones that is coloured in 2018 as compared to 2010, this presents the trend of an aging population in Singapore.

Singapore Aged Population Proportions for 2010 and 2018

Proportional of Aged Population in 2010.png
Proportional of Aged Population in 2018.png

I added a new column for both the 2010 and 2018 data files for the aged populations in the attributes table for citizens aged 65 and above. I also subsequently added the total populations column for both 2010 and 2018. Note that all these attributes have been added into the MP14 Web layer. I went on to calculate proportion using the field calculator by putting the dividing the total count of citizens aged over 65 for both years over the total population.

Percentage Change of Age Population between 2010 and 2018

Percentage change of Aged Population between 2010 and 2018.png

For percentage, I added a new column for both the 2010 and 2018 data files for the aged populations in the attributes table for citizens aged 65 and above. I also subsequently added the total populations column for both 2010 and 2018. Note that all these attributes have also been added into the MP14 Web layer. I then went on to calculate the proportion by subtracting the differences between both files and dividing it over the 2010 value and multiplying the answer by 100. I then went on to render the data onto the map. Analyzing the map's data, the darker shades show a large percentage increase in the number of people aged 65 and above living in those subzones, while the lightest shade shows a percentage decrease in the number of people aged 65 and above living in those same subzones. Since there is a significant number of subzones with darker shades as compared to the first shade, it implies that the number of aged people living in these areas have had a increase, or even a significant amount of increase.

Missing Values

I have assumed for '-' values to be 0

Sources

Data.gov Sing Stat