Geospatial Analytics enables one to draw attention to the location aspects of the features or attributes in question for the analysis. It provides additional information that enables better decision-making. For our assignment on Geospatial story telling, we have considered the classic case of accommodation hunt for NUS students and performed the Analysis in ArcGIS.

Objective: To find a suitable location for student profiles with all the basic amenities and needs within their reach

The students have been classified into 3 profiles:

i) The Nerd – prefers an affordable living near University and does not commute much

ii) The Explorer –  loves to explore the city, prefers to participate in recreational activities  and travels a lot

iii) The Nerdy Explorer (Their combination)

Data Collection:

i) Students Commuting Data

ii) Singapore Population Data

iii) Singapore Accommodation Pricing Data

iv) Location of Basic Amenities: Library, Hawker Centre, Fire station and Police Station

v) Location of Recreational Amenities: Community Centre, Sky Greenery, Museum, Park

INPUT:

We started with the Singapore topographic map overlayed with MRT Line as our General Purpose Map.  NUS location data was added using its Geo Coordinates as we essentially want our profiles to live within close proximity to the University.

NUS LocationNUS

The residential areas in Singapore have been identified and a Thematic choropleth map was used to visualize based on population. The outskirts are more crowded and the population size decreased as we move to the center of the city

Population Map Population

Similarly the residential areas were visualized by pricing. The pattern here is reversed. The city center is more expensive and the price decrease as we move to the out skirts.

Locational Pricing MapPricingNote: The area in grey are Non- residential zones (either industrial area or office spaces).

PROCESS:

Geo Processing:

i)Buffering

ii)Intersection

The Basic amenities were represented as vector points. It includes Library , Hawker Centre, Fire Station and Police station. Library and Hawker Centre were places of daily visit for students while Fire station ad police station are general safety requirements in any neighbourhood

Basic AmenitiesBasic Amenities

1km buffer was drawn around them which will later be intersected. The intersection indicates the locations which are at close proximity to all amenities.

Library Bufferlibrary buffer

Hawker Centre Bufferhawker center buffer

Fire Station Bufferfire stations buffer

Police Station Bufferspf buffer

The 1km buffers were then intersected to identify potential locations for students stay.

Basic Amenities IntersectionBasic Amenities Intersection

Similar steps were followed for Recreational Amenities

Recreational AmenitiesRecreational Amenities

Recreational Amenities IntersectionRecreational Amenities Intersection

Student commuting data was added to the Map to understand the commuting pattern of Students and their spread around Singapore. From the map we gather that most students travel via MRT and we will take into account this information when identifying the ideal accommodation for our profiles.

Commuting DataCommuting Data

ANALYSIS:

ArcGIS Tools Used:

i) Spatial Statistics Tools -> Mapping Clusters -> Optimised Hot Spot Analysis

ii) Spatial Statistics Tools -> Measuring Geographic Distance -> Mean Centre

iii) Spatial Statistics Tools -> Measuring Geographic Distance -> Standard Distance

iv) Directional Distribution -> Measuring Geographic Distance -> Directional Distribution

Optimized Hot spot analysis was done to identify hot spots were students are concentrated.  Red colored regions indicate regions of hot spot. We note that most students are concentrated around Kent Ridge (which we assume is because students frequent NUS often)

Optimized Hot Spot AnalysisHot Spot Analysis

Hot Spot Analysis – Removing Data PointsHot Spot Analysis Removing Data points

The geographic Mean of the commuting data was taken to identify the optimal location for stay in terms of distance from all the commuted points. With the mean as centre and one standard deviation as radius the circle (dark purple) was drawn indicating the boundary or maximum limit where we want our profiles to stay. To provide some orientation to the boundary we have also created standard deviational ellipse (Light Purple).

Boundary MapBoundary

Having identified the geographical limits of our objective we overlayed the Basic Amenities intersection Map with Boundary Map to eliminate the regions that fall outside our boundary (Eg Jurong East and Hougang)

Overlay 1- Boundary Map and Basic Amenities Intersection MapOverlay 1

We then overlay the Recreational Amenities intersection Map with the Boundary Map and eliminate Yio Chu Khang and Hougang for similar reasons

Overlay 2 – Boundary Map and Recreational Amenities Intersection MapOverlay 2

OUTPUT:

We finally overlay all the intersection and pricing information into a single map. From the Map we observe that Clementi, an affordable living, is an ideal location for the Nerd Profile,  Bouna Vista (with many recreational opportunities) for the Explorer and Pasir Panjang for the Nerdy Explorer.

Conclusion

Conclusion:

Most of the students searching for accommodation would have probably intuitively considered several of the amenities mentioned in our assignment and the proximity of the amenities to their accommotdation as a major criteria in selecting their accommodation. In this assignment we have utilised an analytic approach using travelling hot spots and standard distances to identify the best location in terms of accommodation for different students based on their interest. The scope of the study can be extended further by including more amenities, student profile and considering optimal travel options for the students

Presentation Link:Click Here

Submitted by:
Abhinaya Murugesan [A0163311W]
Allen Geoffrey Raj [A0163398R]
Kavya AK [A0163250R]
Preethi Jennifer R [A0163190L]
Ram Nagarajan [A0163247E]

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