SMT201 AY2019-20G2 Ex1 Soh Bai He

Part One: Thematic Mapping

Public Education Institutions

Data Source: data.gov.sg / File: general-information-of-schools.csv

Handling of Data: general-information-of-schools.csv is geocoded into school_information.shp using geocode.py

Choice of Classification:
1) Categorisation by school type (school_type). Junior College and Centralised Institute are grouped together as both offers pre-university courses and lead to the ‘A’ Level examinations.
2) Categorisation by region to facilitate easier visualisation of the distribution of schools by region.

Visual Variable: SVG marker of a book is used to symbolise schools. Different colours are used for each school type/region for easier identification.

Feature count: Total (344), Primary (181), Secondary (138), Mixed Level (14), Junior College/Centralised Institute (11)

Observation: Of all school types, Junior College/Centralised Institute has the least number. However, the existing ones are well distributed across Singapore with every region covered.

Road Network System

Data Source: mytransport.sg / File: RoadSectionLine.shp

Handling of Data & Choice of Classification: RoadSectionLine.shp is exported into .csv format and new columns RD_CAT_NO, RD_MAIN_CAT are added on excel (road-section-category-sorted.csv). Roads are then sorted with reference to the table below. Thereafter, I analysed the remaining roads (that do not include the street name descriptors in the table below) on the OpenStreetMap and grouped them into Arterial/Primary Access as they are minor roads that provide access to developments.

Road Category	Street Name Descriptor
Expressway and Semi-Expressway (Cat 1)	Expressway, Highway, Parkway
Major Arterial (Cat 2)	Boulevard, Avenue, Way
Arterial & Primary Access (Cat 3 & 4)	Drive, Street, Road
Local Access Roads (Cat 5)	Walk, Lane, Link

Source: URA’s Handbook on Guidelines for Naming of Streets (p.13, Annex 1).

Visual Variable: Line symbols with different colours are used to represent each road type. A warm colour scheme (red > orange > yellow > white) is chosen to highlight the hierarchy of road types. Expressway is given the thickest width as they form the primary network in the road system.

2014 Master Plan Landuse

Data Source: data.gov.sg / File: G_MP14_LAND_USE_PL.shp

Choice of Classification: Categorisation by type of land use (LU_DESC). To minimise the number of categories and colours that might overwhelm the user, several sub categories of the same type of development are grouped into its main category.

Visual Variable: Colour fill as the respective type of land use. I referenced URA’s Past Concept Plan colours for each development. Planning areas are labelled to aid in the visualisation of the distribution of land use in Singapore.

Observation: The West region is dominated by industrial developments along with the Western Water Catchment. There are fewer residential areas in the West, however, a new HDB town called Tengah will be built soon.

Part Two: Choropleth Mapping

Discussion (Handling of Data, Classification Choices)

2010 Data: SUBZONE_AGE_GENDER_2010.shp

(for Part A): Computed column ‘Above65’ which sums up the number of aged per subzone
(for Part B): Computed column ‘Aged_Pptn’ using the formula (Above65/TOTAL)*100

2018 Data: SGResidentPopulationAgeGroupSex_2018.csv
Data cleaning is performed on excel and saved as SGResidentPopulationAgeGroupSex_2018_cleaned.csv. ‘ZONE_N’ and ‘SUBZONE_N’ are converted to uppercase to match the data in MP14_Subzone. Null values are replaced with 0. After the data is cleaned, it is joined with MP14_Subzone by ‘SUBZONE_N’.

(for Part A): Computed column ‘SG2018_AGED_TOTAL’ which sums up the number of aged per subzone.
(for Part B): Computed column ‘SG2018_AGED_PPTN’ using the formula (SG2018_AGED_TOTAL/SG2018_TOTAL)*100
(for Part C): Both the 2010 and 2018 data are joined together by ‘SUBZONE_N’. Computed column ‘Change’ using the formula (SG2018_AGED_TOTAL - Above65)/Above65 

When there is null values during the computation of the new columns, I replaced them with 0. The computed column is then used as the classification variable for each part. I chose to classify by Graduated symbol with Natural Breaks (Jenks) as the classes are based on natural groupings inherent in the data. For a clearer visualisation, I used single colour ramps for each map.

A: Count of Aged Population (+65) in 2010 and 2018

Data Source: data.gov.sg / File: SUBZONE_AGE_GENDER_2010.shp

Data Source: singstat.gov.sg / File: SGResidentPopulationAgeGroupSex_2018.csv

 There is a growing trend in the number of aged, such that the range has increased to over 10000 from 2010 to 2018. The East and North-East regions have a high aged population density, where sub-regions Bedok North (15160) and Tampines East (17670) house the highest number of aged in 2018.

B: Proportion of Aged Population in 2010 and 2018

Data Source: data.gov.sg / File: SUBZONE_AGE_GENDER_2010.shp

Data Source: singstat.gov.sg / File: SGResidentPopulationAgeGroupSex_2018.csv

 In contrast to the count of aged population in (A), the proportion of aged population shows that the Central region has a higher aged population density. Proportion of aged population is a more accurate representation of population density as it considers the total population per sub-region. 

C: Percentage change of Aged Population between 2010 and 2018

Data Source: data.gov.sg, singstat.gov.sg / File: SUBZONE_AGE_GENDER_2010.shp, SGResidentPopulationAgeGroupSex_2018.csv

 Although there are subzones with negative change in aged population, more than half of the subzones faced a positive increase in aged population. It is surprising that the Southern Islands have a high increase in aged population. 

References

Urban Redevelopment Authority’s Handbook on Guidelines for Naming of Streets
Urban Redevelopment Authority’s Past Concept Plan