SMT201 AY2019-20G1 EX1 Jessica Low Hui Chen

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Part One: Thematic Mapping

Distribution of Public Education Institutions by School Types

School Information

I classified the schools according to MOE’s classification system – Primary, Secondary, Junior College/Centralised Institute & Mixed Levels (schools with mixed Primary/Secondary or Secondary/ Junior College). I used different coloured circles to represent schools of each category.

MOE’s Classification System: https://sis.moe.gov.sg/SchoolDirectory.aspx


Hierarchy of Road Network System

Road Network Hierarchy

I created a new field “RD_CLASS” & followed URA’s guidelines on road nomenclature to filter the data into the various road network hierarchies.

  • Expressways – If they have ‘Expressway’ in their name
  • Major roads – Cat 1 & 2 roads
  • Minor roads – Cat 3-5 roads

I used different coloured lines to represent roads of each category. I changed the width of the roads according to the hierarchy, the expressways being the thickest & minor roads being the thinnest.

URA Handbook on Guidelines for Naming of Streets: https://www.ura.gov.sg/-/media/Corporate/Resources/Publications/Streets-and-Building-Names/SBNB_handbook_streets.pdf?la=en


2014 Master Plan Landuse

2014 Master Plan

I largely followed URA’s Use Groups to zone the different sections of the map. I decided to partially detract from some of the groupings as I felt that the land uses were too distinct to put together.

  • Eg. I kept ‘Reserve Site’ out of Use Group F and gave it a category of its own

I used categorised symbology to indicate different colours based on land use.

URA Purposes and Master Plan Zones Within Use Groups: https://www.ura.gov.sg/Corporate/Guidelines/Development-Control/Planning-Permission/Folder/DC-Charge-Rates/DC-rates/2000-2005/use-group-tables/u-groups2002-sep2004


Part Two: Choropleth Mapping

On obtaining the csv files from Singstat, I summed the number of residents aged 0 to 64 years old under the field ‘0-64’ and residents aged 65 and above to ‘65+’. Next, I summed the two newly created fields as ‘Total’ to indicate the total number of residents in each subzone planning region.

There were some regions in the files where there was a dash ‘-’ – I took this to indicate that there were no residents living in the region, and used the Excel replace function to create a blank cell so as to not confuse QGIS with regards to the data type. I altered both the 2010 & 2018 csv files in this manner, after which I uploaded them onto QGIS and named them Residents_2010 & Residents_2018 respectively.

I joined the Residents_2010 and/or Residents_2018 csv files (as the join layer/s) with the MP14_SUBZONE_NOSEA_PL layer, depending on which map I wished to create. Next, I joined the csv files & layer via Subzone name, then exported each of the joins as a new layer. There were a number of subzones in the MP14_SUBZONE_NOSEA_PL layer that did not join with the csv files – I took it that Singstat did not use the same categories as URA and continued making my Choropleth maps, disregarding the subzone areas that did not join with the csv files.

I used the following calculations for each map:

  1. Aged Population (+65) in 2010 and 2018 – No. of residents aged 65+ in each area. I took this value wholesale from the ‘65+’ field.
  2. Proportion of Aged Population in 2010 and 2018 – No. of residents aged 65+ divided by the total residents in each area * 100. I created a new field to input these calculations.
  3. Percentage Change of Aged Population Between 2010 and 2018 – (No. of residents aged 65+ in 2018 – No. of residents aged 65+ in 2010) / No. of residents aged 65+ in 2010 * 100. I created a new field to input these calculations.

I then used graduated classification to create a Choropleth map based on the calculated values stated above. I chose the classes for each map using the same criteria – whichever class gave a map that showed greater differentiation between each category.

  • Aged Population (+65) in 2010 and 2018 – Natural Breaks (Jenks)
  • Proportion of Aged Population in 2010 and 2018 – Natural Breaks (Jenks)
  • Percentage Change of Aged Population Between 2010 and 2018 – Quantile (Equal Count)


Aged Population (+65) in 2010

2010 Population Above 65


Aged Population (+65) in 2018

2018 Population Above 65


Proportion of Aged Population in 2010

2010 Population Percentage Above 65


Proportion of Aged Population in 2018

2018 Population Percentage Above 65


Percentage Change of Aged Population Between 2010 & 2018

Percentage Change of Population Above 65 Between 2010 & 2018