SMT201 AY2019-20T1 EX1 Gui yuqi

Part 1

Singapore Public School Distribution by School Type

Source: Data.gov.sg

I used School-directory-and-information data from Data.gov and MP14_PLNG_AREA_NO_SEA_PL to achieve this task. I geocoded the school information data into the planning area data so that each school's location can be showed on the map. In order to show the number of school distribution on the map, I added new column called School_Num to calculation the school number for each Planning area.

Hierarchy of Road Network System of Singapore

Source: Data.gov.sg & LTA
In order to display the hierarchy of road network system of Singapore, I found a data called National-map-line from Data.dov which includes a column called "FOLDERPATH", and this column indicates the category of this road. There are 5 types of roads, range from Contour, Expressway, Expressway Slip road to International boundary and Major Road. In order to display the road hierarchy clearer on the map, I applied different stroke size and different color for each type of roads.

2014 Master Plan Landuse

Source: data.gov.sg

For a better and clearer visualization of 2014 Master Plan Landuse, I used categorized Symbology and distinct colors to represent different categories of land use, so that different land use area can be easily distinguished. In addition, since there are a lot of different landuse categories, I reduces stroke width so that each area can be clearly visualized.

Part 2

Aged Population(+65) Distribution in 2010 and 2018

Source: Data.gov.sg & Singstat.gov.sg
For the distribution of Aged population in 2010 and 2018, firstly I tidy up the data I got from Singstat.gov.sg and only use the data from 2010 and 2018, at the same time, I added a column called Aged_Pop_SZ for both 2010 and 2018 to add up all the aged population. As you can see from the maps above, the aged population is increased and some area in 2010 didn't have aged population, while it appears to have aged population in 2018, which shows that the population aging trend. In order to show the distribution clearly, I used Graduated Symbology and Labeled all the aged population number for each Subzone.

Proportional of aged population in 2010 and 2018

Source: Data.gov.sg & Singstat.gov.sg
For the proportional of aged population of 2010 and 2018, I added a column called Total_pop to calculate the total population for each subzone for both 2010 and 2018, so that I can derive the proportion of aging population by using aged population number to divide total population number. As you can see from the maps above, we can tell that the proportion of aged population increased a lot along the years , while the aged population number didn't increase much(we can tell from the previous maps comparison).

Percentage Change of Aged Population (65 and Above) in Singapore by Subzone from Year 2010 to 2018

Source: Data.gov.sg & Singstat.gov.sg

For the percentage change of aged population between 2010 and 2018, I use Graduated Symbology and I chose red and grey color ramp, so that it can show both negative change and positive change clearly. As you can see from the map above, the red color shows that there is a aged population percentage decrease in those area, some pink color area means there is a minor increase in these area. While for those grey or dark grey area, it indicates are significant increase in aged population percentage(blue color are 0 percentage change)

Classification Choice, New Variables, Relevant Judgement and Assumptions

For part 2, I used Graduated Symbology on all 5 maps mentioned above. For the first 4 maps, I used Quantile classification mode for all of them except for the Aged Population distribution map of 2010 I used equal interval mode. The reason for choosing these modes i mentioned was to have a more spread out histogram graph so that the visualization can be clearer and easy distinguished. In order to generate the 5 maps in part 2, there are some necessary to be taken before getting the result.

Step 1: Data cleaning

When I got the respopagesex2000to2018(resident population classified by age and gender) data from Singstat, it contains large amount of data from 2000 to 2018, so I cleaned them up to left only 2010 and 2018 data.

Step 2: Adding missing column

after adding the data to qgis, I added few columns to the table before I can proceed for the next step. Firstly, I transfer all the subzone name form lower case to upper case so that I can join with the MP14 Subzone data (Subzone name are all in uppercase in the MP14 data), them I use field calculator to add 2 new columns which are Aged_pop_SZ and Total_pop, represents Aged population number and total population number.

Step 3: Joining attribute tables and calculate

Last step was to join the cleaned resident population data with the MP14 subzone data, after that I added a new column called Percentage changed which is calculated by use Aged population number to divide total population number.