Objective

  • To explore the unexplored places within the NUS campus, based on the NUS-ISS students’ location and movement data.
  • To determine preference of their dwelling place, based on factors like Time/Distance, Facility and Cost.
  • To explore the median house rent across the pockets of habitation of the NUS-ISS Students across Singapore.
  • Justify the house rent across the Singapore region where majority of the NUS-ISS Students prefer to stay, based on factors like Healthy-Living, Tourist Spots and Wireless Hotspots.

Data and Software Used

Data:

Softwares:

  • ArcGIS Online,
  • ArcMap Desktop

Process Flow

Process Flow

 Trend of Cafeteria Visits & other Facilities within

NUS campus by Students

Based on the data, we analyzed the trend of cafeteria visits of ISS students during luch hours (12 PM to 2 PM) on weekdays and weekdays. We could see the trends as mentioned below

Cafeteria Visits

During Weekdays:

  • It is noticed that during weekdays most of the people walk to nearest cafeteria (either Terrace / Deck) for LUNCH and few catch bus to Utown since it takes lot of time to return to NUS-ISS from Utown and Students have choice of all cuisines in Terrace / Deck to select from.
  • Through this analysis, we can interpret that ISS Students considered time as main factor here.
  • Spatio-Temporal analysis of the same has been included in this slide (as an embedded video) and in the below Youtube url.
  • ArcGIS Online Map: https://arcg.is/0jyXvq
  • Click on video / YouTube Link below showing the movement https://www.youtube.com/watch?v=NqfKjSsQ08w

Cafeteria-Weekday Trend.jpg

During Weekends:

  • It is noticed that during weekend most of the people walk to nearest cafeteria (either Terrace / Deck for lunch) for LUNCH and few catch bus to Utown since it takes lot of time to return to NUS-ISS.
  • Through this analysis, interpreted that ISS Students considered features (variety) as main factor and then time here.
  • Spatio-Temporal analysis of the same has been included in this slide as an embedded video.
  • ArcGIS Online Map: https://arcg.is/enqrb
  • YouTube Link below showing the movement https://youtu.be/eg0uwenY6gc

Cafeteria-Weekend Trend

Unexplored Locations

  • This map shows the trend of NUS-ISS students spotted around the NUS Campus.
  • Predominantly, We could see the students spotted around Terrace, BIZ, UTown, Library, Deck and ISS. We analysed and found recreational and sports arena that are under-utilized by our Students.
  • Recreational facilities includes The Scholar Chinese Restaurant, Cafe on the Ridge, Aerobic room, The Ridge Bar
  • Sports arena (facility) includes NUS Archery, NUS Field, Multi-Purpose Sports Hall, Swimming Pool
  • From this analysis, we encourage NUS-ISS Students to make use of available Facilities by managing Time and make NUS Campus lively.

Simply, Explore the ‘Unexplored

ArcGIS Online Map: https://arcg.is/1uanT0

Unexplored Places.JPG

Location Preference of Students outside NUS Campus

General trend of students’ location of stay outside NUS campus

This map shows the students’ general trend of location of stay across Singapore. We could see a widespread distribution of residences across the island. The pockets of students’ concentration across the days could be seen in closer proximity in and around NUS since there are no major clusters found at the far ends of the island, a couple of stray instances apart. The southern region seems to be having the maximum concentration of students while the other regions more or less resemble each other in their concentrations across Singapore. So, the general trend indicates that students prefer to stay closer to college and want to avoid travelling far.

ArcGIS Online Map:

https://nusiss3.maps.arcgis.com/home/webmap/viewer.html?webmap=96646c314e504a8da6b8ad4399b51eed

Total Moves Data.JPG

HEAT MAP ANALYSIS – Locations WHERE most students prefer to stay

This map shows the location where students, presumably their residence, across Singapore (outside NUS campus). From the overall data, the duration of stay was considered greater than 11 hours (> ~37000 seconds) at a single location. The prominent locations where major concentrations of students were found to be

  1. Clementi
  2. Chau Chu Kang
  3. Serangoon
  4. Senkang

Clementi had the highest concentration of students. ArcGIS Online Map:

https://nusiss3.maps.arcgis.com/home/webmap/viewer.html?webmap=0b93dbe083fe44e388a086d274125b42

Heat Map.JPG

HOT SPOT ANALYSIS – Rent Of The Preferred Locations

Rental data of the 4 preferred locations were analyzed and hot spot analysis (Getis Ord Gi* value) was performed to find the locations with highest and lowest rents. From this map, we can find the hot and cold spots based on the rent values across the 4 places. Clementi had the highest concentration indicating that students preferred to travel less (time) though the cost of stay was comparatively high in areas around Clementi

ArcGIS Online Map: https://nusiss3.maps.arcgis.com/home/webmap/viewer.html?webmap=01ef0865466242038005711d664c8ce9

Hot Spot Analysis.JPG

Rental Analysis through Surface Interpolation

Rent Analysis has been performed through Surface Interpolation. Surface Interpolation is a procedure used to predict values of cells at neighboring locations based on spatial auto-correlation. In this map, we have taken the scenario of Clementi and have indicated the rent ranges. The reason for this trend has been discussed in the upcoming slides.

ArcGIS Online Map: https://nusiss3.maps.arcgis.com/home/webmap/viewer.html?webmap=6a883883f06643f7be0b999adb3ff672

Surface Interpolation - Clementi.JPG

Spatial Temporal Analysis of travel from residence to NUS:

This map has been analyzed through a video as to how the students travel from the locations discussed earlier. From the movement analysis. Comparatively we can see students from Clementi can reach soon, while students from Serangoon and Senkang travel for more duration through an MRT line while the student from CCK walks for a long distance and then comes through bus causing a long travel time.

Based on the above and the concentration of students, we could see students preferring Time and Features over Cost as many are clustered around NUS and Clementi. Next we will discuss the reasons for rents being high in areas surrounding Clementi. Link to movement video: https://www.youtube.com/watch?v=4y8-orUzE4E

Movement Data.JPG

Analysis of Facilities IN SINGAPORE: Optimized HOTSPOT Analysis

Optimized Hot Spot Analysis.JPG

Clockwise from the Top:

  • The plotted Healthier Dining Data points across Singapore regions.
  • The statistically significant spatial clusters of high values (Optimized Hot Spots) for the Healthier Dining across the regions of Singapore.
  • Both Plotted Data points and Hot Spots for Healthier Dining across the regions of Singapore.

Analysis of ALL CONSIDERED FACILITIES in SINGAPORE

On analyzing the Central Region (CBD) of Singapore, we find amenities such as Healthier Dining, Hospitals, Registered Pharmacies, Sports and Exercise Facilities and other Facilities like Wireless Hotspots were concentrated close to Clementi and the central region and hence contributing to higher rent in the locality. The above map shows the hot spot analysis of all the mentioned amenities and it is found clustered around the Central region which is close to Clementi.

ArcGIS Online Map: https://arcg.is/irvOG

Analysis of ALL CONSIDERED FACILTIES in SINGAPORE – WITH CBD BOUNDARY

 

On analyzing the Central Region (CBD) of Singapore, we find amenities such as Healthier Dining, Hospitals, Registered Pharmacies, Sports and Exercise Facilities and other Facilities like Wireless Hotspots were concentrated close to Clementi and the central region and hence contributing to higher rent in the locality. The above map shows the hot spot analysis of all the mentioned amenities and it is found clustered around the Central region which is close to Clementi. CBD region can be seen with a green boundary.

SKELETAL VIEW OF CBD AREA :  Highlighting proximity to Facilities

 

A Spatially recreated skeletal view of the CBD area through polygon and poly line data with some of the Points of Interest Data Points Plotted (Healthy Living- Healthier Dining, Hospitals, Registered Pharmacies, Sports and Exercise Facilities, Tourism and other Facilities like Wireless Hotspots). A tour of the CBD clearly proves that one is never far from these highly sought after points of Interest which also makes it costly for surrounding localities

SUMMARY

  • Spatio-Temporal Analysis of Cafeteria Visits by NUS-ISS Students : Trend analysis over weekdays and Weekend (Time and Facility concerned)
  • Explore the Unexplored NUS : Encourage NUS-ISS to make use of NUS campus and facilities to the fullest.
  • Most of the NUS-ISS students were found to be residing in and around (time to travel to NUS-ISS is less) the Clementi Area, in the central region of Singapore, and it has a relatively higher house rent compared to the other regions of Singapore.
  • A further analysis was done, on the basis of the facilities like Healthy Living (Healthy Eateries, Hospitals, Registered Pharmacies, Sports and Exercise Facilities), Tourism and other Facilities like Wireless Hotspots, to justify the higher price of the central region that includes Clementi Area.
  • What was observed from the Optimized Hot Spot Analysis, using ArcGIS Desktop, was that all the statistically significant spatial clusters of high values with auto corrected spatial dependence of the points of interest considered (Healthy Living, Tourism and Facilities like Wireless Hotspots), clustered around the Central Business District (CBD) area.
  • Not only that, a Spatially recreated skeletal view of the CBD area, within its limits and around, proved that one is never far from these highly sought after points of Interest.
  • Now with the option of so many sought after facilities to avail in and around, and of course the proximity to the NUS campus, it is but logical for the NUS-ISS students to have chosen the central region as their area of residence, despite the relatively higher house rent prevailing in the central region of Singapore.

Uploaded by Team MARS

MUTHARASAN ANBARASAN

RAGHAVAN KALYANASUNDARAM

SAMBIT KUMAR PANIGRAHI

SARAVANAN KALASTHA SEKAR

SESHAN SRIDHARAN

 

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