SMT201 AY2018-19T1 EX1 Lim Jia Khee

Part One: Thematic Mapping

Distribution of Public Education Institutions in Singapore

My classification choice for this map is through the Categorized " approach, using 'mainlevel_' as the classification variable. I have chosen to differentiate the different classes through colour differentiation, with a fixed symbol size. I did not manipulate the categories within the chosen variable as the fields within the column were already well sorted out with no missing variables. I included an Open Street Map as the background for this will provide a macro understanding of the distribution of the various institution types in a quick glance.

Hierarchy of Road Network System in Singapore

In the raw 'Road Section Line' dataset downloaded off data.gov, there was an absence in categorisation for road names. Using an article (link in reference [5]), I sorted Singapore's road names into five major categories. I then used the 'Categorization' approach, with my newly created variable to plot. I differentiated the different categories through colour and width of road types - with Expressway type of roads being the thickest and 'Others' road types being the thinnest. Also, I assigned brighter colours to Expressway, Major Road types such that it will be more obvious to the reader at one glance.

2014 Master Plan Landuse

In plotting this polygon qualitative thematic map, the variable I used for classification is “Lu_Desc”. I did not reduce the number of categories when plotting for three reasons. One, “Lu_Desc” is a categorical not numerical variable. Unlike numerical variables, there is no need to consider about distribution. Secondly, there are no missing values within the “Lu_Desc” variable. Lastly, without understanding the context behind each category, it is very difficult to merge/concise some categories without losing the meaning behind every individual category.

Part Two: Choropleth Mapping

Singapore's Aged Population (65+) in 2010 and 2017

Proportion of Singapore's Aged Population (65+) in 2010 and 2017

Percentage Change of Aged Population From 2010 to 2017

Reasoning behind classification choices, new derived variables, missing values and assumptions

I decided to work with Singapore's Aged Population (65+) for years 2010 and 2017 as I could not find any dataset on data.gov that is for Singapore's residents by aged group and gender, year 2018. The next best alternative is dataset for Singapore's residents by aged group and gender in year 2017. Also, the dataset was only available in .kml extension as there was no files available for .shp or .csv extensions.

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References and data sources

1. https://www.data.gov.sg/search?q=school+information
2. https://www.data.gov.sg/dataset/master-plan-2014-land-use
3. https://www.data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea
4. https://www.data.gov.sg/dataset/master-plan-2008-subzone-boundary-no-sea
5. https://www.remembersingapore.org/2018/08/15/singapore-street-suffixes/
6. SMT201 elearn - Week 4 Hands on Ex 4 - Coastal Outline Shapefiles from SLA
7. https://www.mytransport.sg/content/mytransport/home/dataMall/search_datasets.html?searchText=road - Road Section Line
8. https://www.data.gov.sg/dataset/singapore-residents-by-subzone-age-group-and-sex-jun-2017-gender
9. https://www.data.gov.sg/dataset/singapore-residents-by-subzone-age-group-and-sex-june-2010-gender