SMT201 AY2019-20T1 EX2 Lim Jia Khee

Part 1: Data visualisation of vector layers

Views (from top left to bottom left, clockwise direction)	Description (within 100 words)
Study area and target roads layer	After filtering our initial road layer for two types of roads, namely Service and Track roads, there are a total of 188 different road osm_id. Of the 188 different road types, there are only two track roads and the remaining 186 roads being of service type.
Study area and buildings layer	There was no filtering done for this building layer. Of 527 different building osm_id there were only 22 rows where the building types were listed. The remaining 505 rows did not specify their building type and are categorised under 'Others'.
Study area and digital elevation layer	The lowest ground in Gombak Planning subzone is 9 metres above sea level, while the highest ground in Gombak Planning subzone is 145 metres above sea level. From the Digital Elevation Map (DEM), the elevation of Gombak appears to follow that of a small hill.
Study area and target natural features layer	After filtering for only parks, water and forest nature types, there are eight such features present in Gombak Planning subzone. Forest features are denoted with a shade dark green as compared to the park features.

Part 2: Proximity Analysis, Raster Layers

Views (from top left to bottom left, clockwise direction)	Description (within 150 words)
Study area and proximity to target roads layer	The proximity for road layer ranges from between 0m (minimum) and 772.01m (maximum). To better illustrate the road proximity distance of this raster layer, I decided to create five different proximity bands. Majority of Gombak planning subzone falls within 193m from any track/service roads, making this planning subzone area highly accessible by foot. Within the planning subzone, the furthest distance one has to travel is within 579m (demarked by turquoise-coloured areas). This also means that road accessibility is likely to not be a significant problem when selecting a site to build our Quarantine centre.
Study area and proximity to buildings layer	Similar to the target roads layer above, the proximity for building layer ranges from 0m (minimum) to 866.3m (maximum). To better illustrate the building proximity distance of this raster layer, I used Singleband pseudocolor to create five different proximity bands. Majority of Gombak planning subzone are between 0m and 433.1m apart. Given this densely populated area, it might be difficult to find an appropriate location that is far enough from all buildings in Gombak planning subzone. Hence, a premium will be placed on sites that are far away from all buildings within the Gombak planning subzone to prevent the spread of infectious diseases once the quarantine center is operational.
Study area and slope layer	From the raster image above, most of the land in Gombak have a steepness that is between 0 and 9.2 degrees. Only a small handful of land in Gombak Planning subzone have steepness of above 18.4 degrees, with the steepest being 36.9 degrees. It appears that the land in Gombak is relatively flat, with small patches of relatively steep grounds. When selecting our quarantine centre site, it is best to avoid the spots that are turquoise or blue in colour (between 18.4 and 36.9 degrees in steepness). This will help manage and keep construction cost low.
Study area and proximity to target natural features layer	As there are comparative less natural features in Gombak Planning subzone, a significant amount of land is at least 215.9m away from any target natural feature. There appears to be a plot of land to the South of Gombak Planning Subzone that is between 431.8m and 647.8m away from any target natural feature. This is certainly a good location that deserves more attention. Also, of the three target natural features - forest, park and water, I will keep an eye out for any potential quarantine site that is near a waterbody. This is so as some diseases are water-borne diseases and given Singapore's water scarcity problem, I will want to avoid that entirely.

Part 3: Standardisation of Proximity Analysis, Min-Max method

Across all four views, the min-max standardisation technique was used. This standardisation is done to ensure that the ranges across all layers become identical. This ensures fairness when we apply our AHP weights in the future sections.

Views (from top left to bottom left, clockwise direction)	Description (within 150 words)
Study area with criterion scores of target roads layer	Instead of directly applying the min-max formula over the target roads layer, the inverse of Xij (1 / Xij) was used instead. In turn, this changes the direction of our standardised road score. If we were to directly apply the min-max formula over this road layer, higher scores will be awarded to locations that are away from the roads, while lower scores will be given to sites that are closer to the roads. However, given that we want the inverse of that (higher scores allocated to locations closer to the road features), the inverse of Xij was used for this calculation.
Study area with criterion scores of buildings layer	City of Seattle Open Data Portal
Study area with criterion scores of slope layer	Seattle.gov
Study area with criterion scores of target natural features layer	Seattle GIS Open Data

Part 4: Analytical Hierarchical Process (AHP) input matrix and result report

Data	Source	Data Description	Source URL	Data Type
Seattle Open Airbnb Data	Inside Airbnb	Information on all Airbnb listings found within Downtown Seattle, last scrapped on 15 November 2018	http://insideairbnb.com/get-the-data.html	CSV File
Common Place Name (CPN)	City of Seattle Open Data Portal	A point feature class showing common place names and corresponding locations in Seattle.	https://data.seattle.gov/Land-Base/Common-Place-Names-CPN-/599c-9ddc	CSV File
City Clerk Neighbourhoods	Seattle.gov	Displays the 20 Large City Clerk neighborhood boundaries, along with their smaller neighborhood boundaries.	https://data.seattle.gov/dataset/City-Clerk-Neighborhoods/926y-cwh9	SHP File
Zoning (Generalized)	Seattle GIS Open Data	A polygon feature class showing zoning areas. It also provides information on the type of zoning such as Downtown, Major Institutions, Manufacturing/Industrial, Multifamily, Neighbourhood/Commercial, Residential/Commercial and Single Family.	https://data-seattlecitygis.opendata.arcgis.com/datasets/a85e74dac41d43cab5a8b840558c4d77_3?page=15	SHP File

Part 5: Suitable landlot(s) identified

Data	Source	Data Description	Source URL	Data Type
Seattle Open Airbnb Data	Inside Airbnb	Information on all Airbnb listings found within Downtown Seattle, last scrapped on 15 November 2018	http://insideairbnb.com/get-the-data.html	CSV File
Common Place Name (CPN)	City of Seattle Open Data Portal	A point feature class showing common place names and corresponding locations in Seattle.	https://data.seattle.gov/Land-Base/Common-Place-Names-CPN-/599c-9ddc	CSV File
City Clerk Neighbourhoods	Seattle.gov	Displays the 20 Large City Clerk neighborhood boundaries, along with their smaller neighborhood boundaries.	https://data.seattle.gov/dataset/City-Clerk-Neighborhoods/926y-cwh9	SHP File
Zoning (Generalized)	Seattle GIS Open Data	A polygon feature class showing zoning areas. It also provides information on the type of zoning such as Downtown, Major Institutions, Manufacturing/Industrial, Multifamily, Neighbourhood/Commercial, Residential/Commercial and Single Family.	https://data-seattlecitygis.opendata.arcgis.com/datasets/a85e74dac41d43cab5a8b840558c4d77_3?page=15	SHP File