SMT201 AY2019-20G2 Ex1 Melanie Tan Ri Fang

Part One: Thematic Mapping

2014 Master Plan Landuse

I categorised the MP14 Landuse into sub-categories based on their area of location. Then upon looking at the attributes, I realised that there are actually any repetitive words such as business and business - 1, therefore I decided to combine such datas together, to make the legend more compact and easier for the user to see.

Level of Schools

From the map, primary and secondary schools are much more in numbers as compared to JC and mixed levels.I have chosen 4 different symbols to represent the different schools. It is evident that there are more primary and secondary schools thus I had to increase the size of the mixed level and JC and centralised institutions to make it more visible from the map. I also chose to use catergorised mapping which is able to show the viewers exactly which area is the schools located at.

Road Network System

I chose to use a thicker line to show the major road and relatively thinner lines to display the slips roads and contour lines. This is to help the user to see the general major roads in Singapore at one glance. In addition, to also make the expressway more visible, I chose a neon color to make it stand out apart from the major roads as I feel these 2 roads are the most commonly used . This is displayed on an outline map of Singapore instead of the MP14 Landuse where some roads which are in the map itself may end up confusing the viewer. Thus, I feel this contrast to show the road hierarchy in Singapore is the best.

Part Two: Choropleth Mapping

2010 Population Aged 65+

calculated elderlies aged 65 and above before adding in to QGIS. The darker the colors, the more densely populated the area is with regards to elderly.

2018 Population Aged 65+

Calculated the aged65 and above in the excel sheet before adding it to QGIS. Then using the graduated function, the display on the map shows the spread of elderlies that are 65 years old and above. As seen on the map, the darker the colours, the more elderlies that are above 65 are staying around that area. It can be seen that there are more darker colours seen on the east side of the map, thus we can safely conclude that the east side of Singapore resides a lot of elderlies.

2010 Proportion Aged 65+

To get the proportion of the Aged population, I took the variable that was previously calculated "Age65+" divided by the total population in that year to find the proportion aged65+ of each subzone. Thus, based on the visual analysis of the map, we can see that the north side of Singapore had a higher density of aged population as compared to the other parts of Singapore.

2018 Proportion Aged 65+

I classified the data into 5 classes by natural breaks as this classification can better represent the trends in the data. Comparing the distribution of aged proportion with year 2010, it can be said that there is indeed a higher density spread of aged population across Singapore as compared to 2010 where it was mainly in the north side of Singapore.

Percentage Change From 2010 and 2018

I joined the 2010 Proportion Aged 65+ and 2018 Aged 65+ columns together and came up with the 2010 to 2010 percentage change of elderly in each subzone. I decided to use the 1 colour scale. As seen on the map, the darker areas are in the center, meaning the proportion aged fell. And the lighter areas are away from the city, meaning the elderly proportion increased.

Discussion

For part 2, I chose to use the "graduated" function as it would be a better representation of the spread of distribution of the elderlies. I joined the CSV file that contains population data of 2010 and 2018 into the Master Plan 2014. As to which, I added a new variable to the table called "Age65+" to sum up the total aged population in their respective years. As for the calculation of proportion of aged populated, I tabulated it through the use of this formula "Age65+/ (Age0-4…+age45-50+age51-55…_age65+) , this helps me to store the proportion of aged population respectively for each years. In the process of joining, any null values that were obtained were treated as 0. Lastly, I calculated the percentage change through the use of the proportion of aged population to obtain a more accurate visualisation and analysis of the difference between the two years. I calculated it using a new variable called "Percentage" by joining the CSV files of 2010 to 2018, because there are more data in 2018 as compared to 2010 else, I might potentially lose some data in the process of joining my files. I did this through using the formula ((2018_proportion - 2010_proportion)/2010_population*100% where the 2018&2010_proportion where previously calculated in part 2. I converted any null values to 0 for better accuracy of visualisation and analysis. I then used the "Graduated" symbology to show the distribution of proportion of aged population for their respective years. I chose to use the single color ramps with the same limits for the same category of maps for easy comparison. Taking into consideration that the percentage change as shown in the map varies between negatives to positives, I chose to use a single colour ramp to show the contrast in colours between the positives and negatives.