Difference between revisions of "SMT201 AY2019-20T1 EX1 Heng Bing Chow"
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==Part 1: Thematic Mapping== | ==Part 1: Thematic Mapping== | ||
− | + | '''Distribution of public education institution by school types''' | |
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+ | [[File:Schools in Singapore by School Type.png|700px|center|Schools in Singapore by School Type]] | ||
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+ | First, I loaded the '''Coastal Outline''' and '''MP14 PLNG AREA NO SEA PL''' into QGIS, to serve as the base layer. | ||
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+ | Next, I used '''MMQGIS''' > Geocode CSV with Web Service > General information of schools > Set Address as Address | ||
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+ | Finally, Symbology > Categorized by mainlevel_ , Classify > then select SVG Marker Symbols. | ||
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+ | I chose P for Primary, a Baby Symbol for Secondary, a Graduation Hat for Mixed Level, a ABC board for Junior College and a Stack of Books for <br> | ||
+ | Centralised Institute. | ||
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+ | Source: https://www.data.gov.sg/dataset/school-directory-and-information | ||
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+ | '''Road Network System of Singapore''' | ||
+ | [[File:Road Section Line.png|700px|center|Road GIS in Singapore]] | ||
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+ | After Downloading the RoadSectionLine data from LTA DataMall, | ||
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+ | Open Attribute Table > Toggle Editing > Create New Field, RD_TYPE | ||
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+ | Next, Filter and Select all rows with Expressway, Update Road_Type field as 'EXPRESSWAY' | ||
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+ | Do the same steps for all other road types e.g. Road, Drive, Way, Lane, Link etc | ||
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+ | Once done, RoadSectionLine > Properties > Symbology > Categorized by RD_TYPE > Classify | ||
+ | Lines in Green are for Expressway, Orange for Major Arterial, Blue for Minor Arterial, and Purple for Local Access. | ||
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+ | Source: https://www.mytransport.sg/content/mytransport/home/dataMall/search_datasets.html?searchText=RoadSectionLine | ||
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+ | https://www.ura.gov.sg/-/media/Corporate/Resources/Publications/Streets-and-Building-Names/SBNB_handbook_streets.pdf?la=en | ||
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+ | '''2014 Master Plan Landuse''' | ||
+ | [[File:MP14 Land Use (for wiki).png|700px|center|Singapore's Land Use based on MP14]] | ||
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+ | First, load '''G_MP14 LAND_USE_PL''' into QGIS. | ||
+ | |||
+ | Next, Properties > Symbology > Categorized by LU_DESC > Classify > Apply > OK | ||
+ | |||
+ | The layer will display different colours according to the different type of use. E.g. Red for Business, Green for Open Space etc. | ||
+ | |||
+ | Source: | ||
+ | https://data.gov.sg/dataset/master-plan-2014-land-use?resource_id=0d1a6cda-7cad-4b17-b9a8-9e173afebbc1 | ||
==Part 2: Choropleth Mapping== | ==Part 2: Choropleth Mapping== |
Revision as of 15:00, 15 September 2019
Part 1: Thematic Mapping
Distribution of public education institution by school types
First, I loaded the Coastal Outline and MP14 PLNG AREA NO SEA PL into QGIS, to serve as the base layer.
Next, I used MMQGIS > Geocode CSV with Web Service > General information of schools > Set Address as Address
Finally, Symbology > Categorized by mainlevel_ , Classify > then select SVG Marker Symbols.
I chose P for Primary, a Baby Symbol for Secondary, a Graduation Hat for Mixed Level, a ABC board for Junior College and a Stack of Books for
Centralised Institute.
Source: https://www.data.gov.sg/dataset/school-directory-and-information
Road Network System of Singapore
After Downloading the RoadSectionLine data from LTA DataMall,
Open Attribute Table > Toggle Editing > Create New Field, RD_TYPE
Next, Filter and Select all rows with Expressway, Update Road_Type field as 'EXPRESSWAY'
Do the same steps for all other road types e.g. Road, Drive, Way, Lane, Link etc
Once done, RoadSectionLine > Properties > Symbology > Categorized by RD_TYPE > Classify Lines in Green are for Expressway, Orange for Major Arterial, Blue for Minor Arterial, and Purple for Local Access.
2014 Master Plan Landuse
First, load G_MP14 LAND_USE_PL into QGIS.
Next, Properties > Symbology > Categorized by LU_DESC > Classify > Apply > OK
The layer will display different colours according to the different type of use. E.g. Red for Business, Green for Open Space etc.
Part 2: Choropleth Mapping
- Aged population (+65) in 2010 and 2018.
- Proportional of aged population in 2010 and 2018.
- Percentage change of aged population between 2010 and 2018.