SMT201 AY2019-20G2 Ex1 JerryTohvan

Part 1: Thematic Mapping

The thematic mapping developed uses these following data and applied techniques:

Data	Visualisation & Processing Technique
General information of schools from the “School Directory and Information” dataset retrieved from data.gov.sg.	Layer: School Symbology: Categorised by `mainlevel_` attribute which indicates if an indicated point belongs to either centralised institute, junior college, mixed level, secondary or primary school. Each category is labeled with following color: FIGURE I ROAD SELECTION LINE AND MAP LINE Color choices were contrasted differently from other components/layers for easy reference. Processing: The initial dataset was geocoded using the MMQIS by its `address` field in order to retrieve `latlong` projection of the data points. Data not found: - RAFFLES INSTITUTION, 1 RAFFLES INSTITUTION LANE. - BOWEN SECONDARY SCHOOL ,2 LORONG NAPIRI.
“Masterplan 2014 Landuse” dataset retrieved from data.gov.sg.	Layer: Land Use Symbology: Light Grey simple fill FIGURE II SCALE VISIBILITY Visualisation Rule: The visibility of Land Use layer is automatically displayed as the user zooms in approximately 1-2 times.
SLA’s National Map Line retrieved from data.gov.sg. Road Selection Line dataset retrieved from SLA provided by Prof Kam (HandsOnEx1).	Layer: Map Line & Road Network Visualisation Rule: FIGURE III ROAD SELECTION LINE AND MAP LINE Processing: 2 datasets was used to represent different types of road. The national map line only provides expressway, major roads, international boundary and contour lines, the road selection data provides overall road network in Singapore. The Map Line layer highlights its road types using the categorisation rule, applying different colours and line width to emphasize type of road. The minor road can be implied by excluding road network that belongs to express way, intersections, and major roads.
“MP14_SUBZONE_NO_SEA_PL” by URA retrieved from data.gov.sg.	Layer: MP14_SUBZONE_NO_SEA_PL Symbology: Light Brown simple fill Was included to provide a macro view base layer as an optional display. The layer represents subzones that could be useful in interpreting where road networks or school is located. OpenStreetMap view could also be used, however the subzone layer better express the subzone boundaries through a simple display.

FIGURE IV

OVERVIEW OF THEMATIC MAPPING

Firstly, the thematic mapping shown in figure 5 represents the default view of the map. School data points, map line and road network are shown on top of the OpenStreetMap.

FIGURE IV

MICRO VIEW OF LAND USE VISIBILITY WITH ROAD NETWORKS AND SCHOOL DATA POINTS

The land use data was set with automatic scale visibility, the overall land use layer will only be clearly visible as the user zooms in for interpretation. The land use data provides a micro level data of the indicative polygon of each development land parcel. Thus, there’s no need for this layer to be displayed in a more macro view as lines will not be value-adding to visualisation interpretation.

Next, the national map line only provides expressway (blue line), major roads (magenta line), international boundary and contour lines (excluded), while the road selection data provides overall road network in Singapore. Thus, achieving an overview of all types of road can be done by overlapping the road networks to retrieve minor road (red line) through overlapping as shown in figure V.

FIGURE VI

SUBZONE VIEW

FIGURE VII

OPENSTREETMAP VIEW

The `MP14_SUBZONE_NO_SEA_PL` and OpenStreetMap layer (Figure VI & VII) were added as I believe that it might help in terms of data interpretation, eg: finding out where a junior college is located by subzones and its distance to major road where it's usually major road provides better transportation option/accessibility.

Part 2: Choropleth Mapping

FIGURE VIII

LAYERS EXPORTED

The choropleth mapping developed uses these following data and applied techniques:

Sources and Methods

Dataset	Visualisation & Processing Technique
“Singapore Residents by Planning Area/Subzone, Age Group and Sex, June 2000 - 2018” from Department of Statistics Singapore.	Layer: respopagsex2000to2018_unfiltered Processing: 1. The initial dataset is the base population data yet to be processed with the map layer data. FIGURE IX FILTERING AGED POPULATION 2. Layer`respopagsex2000to2018_aged_pop` was achieved by filtering attribute `AG` which represents the age groups. FIGURE X AGGREGATING DATA USING GROUPSTATS FIGURE XI IMPORTING GROUPSTATS GENERATED CSV USING CUSTOM DELIMITER 3. Plugin `Group Stats` was used to group by data with a simply drop-and-drag feature. In which could perform operations such as general table operations with group by, selection of columns, and data aggregation. The plugin helps to produce these following files and layers: a. `sum_aged_pop_pa` and `sum_aged_pop_sz` from file `/GroupStats/sum_aged_pop.csv` and `/GroupStats/sum_aged_pop_sz.csv` respectively which was achieved by a sum aggregation of aged population grouped by year and subzones/planning areas. b. Layer `total_population_sv` from file `GroupStats/total_population_sz.csv` was a product of a sum aggregation of all population grouped by year and subzones. FIGURE XII DATA OVERVIEW OF IMPORTED GROUPSTATS DATA FIGURE XIII DATA OVERVIEW OF IMPORTED GROUPSTATS DATA 4. Layer `sum_aged_pop_pa` and `sum_aged_pop_sz` were used as a base data for getting the aged population (+65) in 2010 and 2018 data on each subzones and planning area filtered using the `Time` attribute. a. Thus, producing layer `sum_aged_pop_2010_pa`, `sum_aged_pop_2018_pa`,`sum_aged_pop_2010_sz` and `sum_aged_pop_2018_sz`. b. `Zone_ID_SZ` and `Zone_ID_PA` were an additional attribute for primary keys needed to join with the subzone and planning area data. We applied expression `upper(“SZ”)` and `upper(“PA”) for an uppercase reference of the attribute included. 5. We derive the layer `propotion_aged_pop_2010` and `propotion_aged_pop_2018` by layer joining of `total_population_sz` and `sum_aged_pop_sz` through`SZ` attribute as keys and applying `Time` filter for respective layers. FIGURE XIV PROPORTION FIELD CREATION a. Using the `Field Calculator`, we add new attribute `Proportion` by dividing subzone aged population by its subzone total population. FIGURE XV DERIVING PERCENTAGE CHANGE 6. Lastly, the `2010_2018_percentage_change` layer was achieved simply by layer joining from `sum_aged_pop_sz_2010` and `sum_aged_pop_sz_2018`. Data clean up was done to replace any `NULL` values to 0. Above figure represents the expression formula used to derive the percentage change from 2010 to 2018.
Singapore Master Plan 2014 Subzone and Planning Area 2014 boundary data retrieved from data.gov	1. `SumAgedPopulation2010_PA` layer joined with `sum_aged_pop_2010_pa` by matching attribute `PLN_AREA_N` and `Zone_ID_PA`. a. Symbology (Natural Jenks): FIGURE XVI CATEGORISATION OF PLANNING AREA SUM AGED POPULATION DATA 2. `SumAgedPopulation2018_PA` layer joined with `sum_aged_pop_2018_pa` by matching attribute `PLN_AREA_N` and `Zone_ID_PA`. a. Symbology (Natural Jenks): FIGURE XVII CATEGORISATION OF PLANNING AREA SUM AGED POPULATION DATA 3. `SumAgedPopulation2010_SZ` layer joined with `sum_aged_pop_2010_sz` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`. a. Symbology (Natural Jenks): FIGURE XVIII CATEGORISATION OF SUBZONE SUM AGED POPULATION DATA 4. `SumAgedPopulation2018_SZ`layer joined with `sum_aged_pop_2018_sz` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`. a. Symbology (Natural Jenks): FIGURE XIX CATEGORISATION OF SUBZONE SUM AGED POPULATION DATA 5. `ProportionAgedPopulation2010_SZ` layer joined with `propotion_aged_pop_2010` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`. a. Symbology (Natural Jenks): FIGURE XX CATEGORISATION OF SUBZONE PROPORTION AGED POP DATA 6. `ProportionAgedPopulation2018_SZ` layer joined with `propotion_aged_pop_2018` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`. a. Symbology (Natural Jenks): FIGURE XXI CATEGORISATION OF SUBZONE PROPORTION AGED POP DATA 7. `Percentage_Change_SZ` layer joined with `2010_2018_percentage_change` by matching attribute `SUBZONE_N` and `Zone_ID_SZ`. a. Symbology: Below is the configuration used for percentage change of aged population. The legend classification intervals were split into 2 ways, negative changes which represents a decrease change were categorised using an equal distribution from the minimum decrease value of -100% to 0. Next, Natural Breaks (Jenks) were used to classify the 5 next categories for the positive values to indicate. Due to its high variance value, the Jenks classification represents best for this case. Additionally, 2 distinct colours (red and blue) were used to appropriately display the nature of percentage change of the aged population from 2010 to 2018. FIGURE XXII CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA FIGURE XXIII 3 BASE COLOR PICK FOR SUBZONE PERCENTAGE CHANGE DATA FIGURE XXIV DATA LABELLING For each respective boundary map object, we created `Label` attribute for data visualisation in which was disabled for better macro level view and observation. FIGURE XXV CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA Enabling each layer’s label can be done via `Layer Properties`.