Difference between revisions of "SMT201 AY2019-20T1 EX1 Wang Youjin"

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== Part 2: Choropleth Mapping ==
 
== Part 2: Choropleth Mapping ==

Revision as of 18:50, 15 September 2019

Part 1: Thematic Mapping

Explain how the mapping were developed with elaboration:

Data QGIS Techniques
1.data.gov - “School Directory and Information”

New Layer created: Schools created from csv file using Geocoding tool MMQGIS plugin. 'Address' attribute is used for openstreet map projection

Distribution of school types: Under Symbology, select categoriszation by `mainlevel_` attribute.Different colors are used to indication different school types for easy illustrative reference purpose

Wyj Schoo layer.PNG

School types


Data not found CSV from geocoding:
- RAFFLES INSTITUTION
- BOWEN SECONDARY SCHOOL

2.data.gov - “Masterplan 2014 Landuse” "National Map Line"

SLA - "Road Selection Line"

Layer Imported: Land Use, Road Selection Line, National Map Line

Different color lines and width are used to represent different types of road with cateogirized technique performed on the map line layer use

WYJ Road Network.png

Road Network




3. data.gov - “MP14_SUBZONE_NO_SEA_PL” Layer: MP14_SUBZONE_NO_SEA_PL

Categorization technique is used to indicate the land used for different purpose. <brt

Wyj Landuse.PNG

Land Use


Part 2: Choropleth Mapping

[[File:|center]]

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.

[[File: |center|600px]]

FIGURE IX

FILTERING AGED POPULATION

2. .


[[File:|center|500px]]

FIGURE X

AGGREGATING DATA USING GROUPSTATS


[[File:|center|400px]]

FIGURE XI

IMPORTING GROUPSTATS GENERATED CSV USING CUSTOM DELIMITER

s: a. `is. b. ones.

[[File:|center|400px]]

FIGURE XII

DATA OVERVIEW OF IMPORTED GROUPSTATS DATA


[[File:|center|400px]]

FIGURE XIII

DATA OVERVIEW OF IMPORTED GROUPSTATS DATA

4. Led. 5. We

[[File:|center|400px]]

FIGURE XIV

PROPORTION FIELD CREATION


a. [[File:|center|300px]]

FIGURE XV

DERIVING PERCENTAGE CHANGE

6. L




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):

[[File:|center|400px]]

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):

[[File:|center|400px]]

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):

[[File:|center|400px]]

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):

[[File:|center|400px]]

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):


[[File:|center|400px]]

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):

[[File:|center|400px]]

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. [[File:|center|500px]]

FIGURE XXII

CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA

[[File:|center|500px]]

FIGURE XXIII

3 BASE COLOR PICK FOR SUBZONE PERCENTAGE CHANGE DATA


[[File:|center|400px]]

FIGURE XXIV

DATA LABELLING

For


[[File:|center|200px]]

FIGURE XXV

CATEGORISATION OF SUBZONE PERCENTAGE CHANGE DATA


Enabling each layer’s label can be done via `Layer Properties`.