SMT201 AY2019-20G2 EX1 Neo Yin Li Amelia
Contents
Part 1: Thematic Map
Education Type
My cartographic technique makes sense as I did a qualitative thematic map using qualitative data, that is, school types. Since I wanted to categorise the map by school types, I removed the irrelevant information from the excel file such as the principal name. This will allow the geocoding process to run more smoothly. After adding the geocoded layer, I categorised by the different types of schools such as primary, secondary and more. I used point symbol and chose SVG marker to indicate the respective school types by using different symbols such as the whiteboard symbol for primary schools. There are a significant higher number of primary and secondary than the other types of schools in Singapore based on the map.
Road Type
My cartographic technique makes sense as I did a qualitative thematic map using qualitative data, that is, road types. As the dataset did not have a field containing the types of roads, I created a new field and separate the roads into their respective types, which are expressway, semi-expressway, major and minor roads. After which, I categorised by the different types of roads using the values in the new field with different coloured lines to indicate so.
2014 Master Plan Landuse
My cartographic technique makes sense as I did a qualitative thematic map using qualitative data which is amenities type. I categorised the different types of amenities using polygon feature with hue, which is a nominal scale visual variable. This means that different types of amenities are represented with different colours on the map, in order to differentiate them. I also removed the amenities that are not part of the main categories. Main categories are residential, business and more.
Part 2: Choropleth Map
For all the chrolopleth maps, I kept the number of classes to 5 to ensure that the class values should not overlap. Also, I created a new field called “AGE65+” in order to add up all the values that above the age of 65. This is to allow me to make use of the collated values for each subzones to plot out the layers needed. Furthermore, I handled my missing values by setting these values as 0 if possible so that I can include the subzones which did not have any data.
All of the spatial patterns can be seen through the usage of graduated categorisation, where the dark colour indicates the higher number of aged population living in the regions. I chose to use natural breaks for all of my chrolopleth maps to categorise the numbers under AGE65+ due to the little changes to the distribution of values regardless of which mode that was used.
Aged Population (AGE65+) 2010 and 2018
For the 2 maps for 2010 and 2018 aged population distribution, both showed the spatial patterns where the east and north-east region such as Serangoon and Kallang have high density of aged population. For 2018 map, there is a significant increase in aged population as the darkest colour regions are around 9660 to 17670. Whereas in 2010, the darkest colour regions have about 5540 to 9990 people. No assumptions were made when I created these 2 maps.
Proportion of Aged Population 2010 and 2018
For the 2 maps for 2010 and 2018 aged population distribution, both showed the spatial patterns where the east and north-east region such as Serangoon and Kallang have high density of aged population. For 2018 map, there is a significant increase in aged population as the darkest colour regions are around 9660 to 17670. Whereas in 2010, the darkest colour regions have about 5540 to 9990 people. No assumptions were made when I created these 2 maps.
Percentage Change of Aged Population between 2010 and 2018
For the 2 maps of percentage change of aged population, regions at the east such as Paya Lebar has a higher percentage change between 2010 and 2018. I created a new field in each age distribution layers called “percentage change” so that I can derive the values. I derived the percentage change values by using the Math function to subtract each 2010 subzones’ aged proportion values from each 2018 subzones’ aged proportion values. After which, the subtracted values will then be multiplied with 100 to get the percentage change. This formula is an assumption of mine to get the percentage change values between 2010 and 2018.
