SMT201 AY2019-20T1 EX2 Wang Youjin

Part 1: Base Map Views

In order to identify a location suitable for building a National Communicable Disease Quarantine Centre given in the task question, we have to first look at the important aspects for consideration in targeted Gombak planning subzone by building the base map views 1) Gombak Road 2) Gombak Building 3) Gombak Natural Features 4) Digital Elevation together with targeted study area - Bukit Gombak:

Map with Buildings, Roads, Natural Feature and Elevation in 1 view Gombak Target Road Data Source: 1. Master Plan 2014 Subzone Boundary (No Sea) (SHP) from URA [1]. 2. Roads(SHP) from osm [2]. 3. Buildings(SHP) from osm [3]. 4. Natural(SHP) from osm [4]. 5. Raster ASTER Elevation(TIFF) from NASA [5].

Map with 4 Views on Buildings, Roads, Natural Feature and Elevation in 1 Map Description Digital Elevation Layer Data Source: 1. Master Plan 2014 Subzone Boundary (No Sea) (SHP) from URA [6]. 2. Roads(SHP) from osm [7]. 3. Buildings(SHP) from osm [8]. 4. Natural(SHP) from osm [9]. 5. Raster ASTER Elevation(TIFF) from NASA [10].

Write-up Roads(only service & tracks) In view of the Accessibility Factor that we need to consider that requires the selected site to be close to local roads to ensure easy transportation of building materials during the construction stage, we are using roads layer as one of the base map. The data has been cleaned up as required to show only categorized service roads using thicker strokes and tracks using thinner strokes. There are 199 service roads and only 2 tracks. From observation,the service roads spread across of the target area and are more concentrated in the north east didtrict. Buildings In view of the Health Risk factor that we need to consider that requires the selected site to be away from population to prevent the diseases from spreading to the public. By using categorize visualization of the building types, we can see there are 7 types including clinic, construction site, garage, place of worship, public space, residential area, and train station. However, 95%(505) of the building data are not known of its type which make it hard to present a clear understanding of the building type by categorizing it. Natural Features In view of the Natural Conservation factor that we need to consider that requires the selected site should be also be away from natural features, such as forests, parks and water bodies. From the OSM data, we can see there are 1 forest, 4 parks and 3 water bodies in the target area. Since these features are special regions and are reserved - we will use special color scheme to identify them 1)forest is in grey green, 2)parks is in light green 3)water bodies is in light blue. Digital Elevation In view of the Economic factor that we need to consider that requires the selected site should avoid steep slopes to reduce the development costs for cut-and-fill construction techniques. The lowest elevation is 8 meters above sea level, depicted in grey/white.The highest elevation with 145 meters above sea level, depicted in dark black.

Part 2: Factor Layers:Proximity and Slope Analysis Views

Next, we did a proximity analysis to show the closeness of all features in target area to the identified features of interest. As shown in the screenshot below, the darker parts the maps below depict higher closeness to the features of interest.

Four views showing the proximity map to 1)target roads layer, 2) buildings layer,3) target natural features layer and 4) slope layer that are created in Part 1.

Map with 4 Decision Factors Views

Proximity to Building Data Source: 1. Master Plan 2014 Subzone Boundary (No Sea) (SHP) from URA [11]. 2. Roads(SHP) from osm [12]. 3. Buildings(SHP) from osm [13]. 4. Natural(SHP) from osm [14]. 5. Raster ASTER Elevation(TIFF) from NASA [15].

WRITE UP Proximity to Roads Darker values means the roads network are closer to the feature in the targeted are, which means it's more suitable of developing a building. According to the number generated - the distance from a 5 meters by 5 meters land lot to the nearest road within Gombak is 0 meters, and the furthest distance is 452.60 meters. Proximity to Buildings Land lots with lighter values have higher suitability to be chosen to build the quarantine center because these locations are further away from the population. The distance from a 5 meters by 5 meters land lot to the nearest building that is located within Gombak is 0 meters, and the furthest distance is 561.81 meters. Proximity to Nature Land lots that are further away from natural features have lighter values in the map, and they have higher suitability to be chosen to build the quarantine center. The distance from a 5 meters by 5 meters land lot to the nearest forest, park or water body is 0 meters, and the furthest distance is 517.93 meters. All land lots with distance to the nearest natural feature of at least 200 meters are shown in white in the map above. Slope Analysis Slopes are calculated using proximity to elevation. Land lots with darker values have flatter surfaces, and thus they have higher suitability to build the quarantine center. The minimum steepness of a 30 meters by 30 meters land lot is 0 degrees, which indicates flat land; while the maximum steepness is approximately 37.45 degrees. As seen from the map, most of the land lots in Gombak are flat, despite the rather uneven elevation in the region.

Part 3: Factor Layers with Criterion Scores

With the proximity analysis done on the 4 selected features of interest, next we need to quantify the suitability by giving them a score that we can comprehend. Hence, applying the standardisation technique from Statistics, we can use the Max- Min method to standardise the above analysis results:

Standardised Results = [Proximity to Features - Min(Proximity to Features)] / [Max(Proximity to Features) - Min(Proximity to Features)]

This calculation can be achieved by using Raster Calculator in the processing tool. The x value will be the attribute available in the selection, then we will manually extract the max/min obtained from the proximity step.

The results will range from 0-1 and we categorize them into 10 classes to give us a clearer understanding of the score distribution performance.

Map with 4 Criterion Score Views of the 4 Decision Factor

Proximity to Building Data Source: 1. Master Plan 2014 Subzone Boundary (No Sea) (SHP) from URA [16]. 2. Roads(SHP) from osm [17]. 3. Buildings(SHP) from osm [18]. 4. Natural(SHP) from osm [19]. 5. Raster ASTER Elevation(TIFF) from NASA [20].

WRITE UP

Accessibility Factor The selected site should be close to existing local roads hence the standardizaiton score indicates the land lots that are further away from the local roads in lower score toward 0 represented by lighter green colors, while the ones nearer to local roads get a higher score toward 1, represented in darker green color. Health Risk Factor For the quarantine center to achieve its objective, we need to ensure it is built further away from the concentrated population. Hence for land lot near to the building has a lower score, represented in lighter grey color; while the ones that are further away have a higher score, represented in darker grey color scheme. Economic Factor Slope Analysis are calculated using proximity to elevation to understand the surface height changes. Land lots with flatter surfaces are considered to be more suitable as it's less costly to conduct construciton work, hence we assign those flatter area with higher score, represented in green&blue color blocks. The steeper land lot is represented in orange/red color block as less suitable lots with lower scores. As seen from the map, most area are flat. Natural Conservation Factor We want to ensure the construction is further away from the natural features for the purpose of natural conservation course. Land lots that are further away from natural features have higher score, represented by darker blue color. The land lots that are nearer to the natural feature are represented in lighter blue - lower score is assigned.

Part 4: Analytical Hierarchical Process (AHP) input matrix

With the standardised criterion score on 4 decision factors, we can now us Analytical Hierarchical Process input matrix to assign them with different importance level.

AHP Results Report

we first, according to the score standards for APH , assign different importance score to the 4 decision factors in the yellow cells in the matrix table. The template auto calculated using the scores in pairwise comparison matrix. Then, the scores are normalised by dividing the scores for each cell with the sum of its entire column. Finally, the average of the rows of the normalised matrix is taken, and that is the AHP score. we give health risk factor a high importance level, followed by accessibility, economic factor and lastly, natural conservatio factor. Their important score are 0.542, 0.233, 0.14 and 0.085 respectively. The consistency is at 3%, which is a good indicator of the score assigned. Additionally, the CI we have is 0.024. This indicates that the inconsistency generated by the pairwise comparison matrix is very low and is acceptable & meaningful. Data Source: AHP Template provided by SCB Associates [21]. 250px School types Description

Part 5: Suitability land lot

A map layout with the suitability land lot(s) and a short description of not more than 200 words commenting on each of the suitable land lot identified.

Conclusion	Map Layout
Land Lot	Description 250px School types Description