GIS can play an important role in various applications such as environmental monitoring, natural resource management, healthcare, land use planning and urban planning. GIS integrates common database operations such as query formation, statistical computations and overlay analysis with unique visualization and geographical functionalities.
The Objective of our study is to enhance the Quality of the student life by recommending him the places for comfortable study and ease in the daily activities. The recommendation is based on the proximity of amenities within a vicinity of the 3km radius. The essential amenities for a student which are considered herein are:

Analysis Approach: GQM


The VECTOR Quality score is calculated using the below formula:

Vector Quality Score = Number of Points in the given cluster * 1000

                                               Total Number of Points in the given Layer

The above formula with the highest value is ranked as best and the lowest values as the least preferable areas for students in Singapore.


  • Raw data points of all students (Apps: Moves, OpenPath)
  • Shape Files & External Layers (Data.gov.sg)
  • Map Exploration (OneMap.sg)



Inconsistencies in date format, duplicate data and naming conventions were sorted out before starting with data analysis. The final file had 5300 data points after data cleansing.



Step 1: Addition of Student Data Points

Visualize the student’s movement data from Moves, OpenPaths.cc on the Singapore Polygon Map. The Data points in the figure given below represent the movement of ISS students within the span of 14 days across the island.


Step2: Clustering

Grouping the data points into five clusters based on the Latitude and Longitude of the student’s data points. The figure given below represents Clustered areas across the island.


Step 3: Mean Center and Buffering

Identifying the mean center of the five clusters and creating a buffer polygon of a radius of 3km. The figure given below shows the mean center with the respective buffer areas.


Step 4: Layer Addition and Clipping

For calculating the Quality Score, we have added all the amenities layers(stepwise) and performed the clipping operation on the buffer areas as created in Step 3. The quality score for each buffer region is calculated using the Quality score formula. The image below shows one example of MRT-LRT Stops operated with Clipping tool. The same approach has been followed for all the 6 layers and quality score of the final clusters has been calculated.


Step 5: Quality score of Clusters

The quality score for all the clusters are summed and rankings are calculated accordingly. The table given below shows the ranking of the 5-clusters based on quality score.


Step 6: Dissolve and Choropleth map of Clusters

The polygons touching the 5-clusters are dissolved in a single polygon. The outcome has 6 polygons out of which 5 polygons represent 5-clusters and the 6th polygon refers to the rest of the area in Singapore. The choropleth map as shown below has been created where the dark color represents the highest-ranking polygon and light color represents the lower ranking polygon.



The Vector analysis has a disadvantage that it treats the complete polygon shape holistically and doesn’t account for distinct features of the areas within the polygon. Whereas in real life, the areas within a polygon have diverse features which are not possible to analyze using Vectors analysis.

The benefit of Raster overlay is that it considers the characteristics of neighboring areas using complex algorithms and extracts the distinct features out of data. It is easy for mathematical modeling and quantitative analysis and discrete and continuous Data are equally accommodated.

Raster Quality Score is calculated using the formula given below:

Raster Quality Score =           Number of Points in the given polygon * 1000

                                                           Area of the Polygon (From Shapefile)

Step-1: Polygon to Raster conversion

The amenities layers are converted into their raster formats using Polygon to Raster Toolbox. The coloration of the raster maps is based on Raster Quality score (density of amenities) in that area. The Raster map of Healthy Dining centres is as shown below:


The Raster map of Wifi Hotspots is as shown below:


The Raster map of Bus Stops is as shown below:


The Raster map of MRT-LRT Stops is as shown below:


The Raster map of Community Clubs is as shown below:


The Raster map of Libraries is as shown below:


Step-2: Raster Overlay Analysis

Raster overlay analysis can be done easily using Map Algebra. All the 6-raster layers are appended on each other using Add operation in Raster Calculator toolbox. The outcome map of Raster Overlay of 6 raster layers is as shown below: –


The areas marked in Green are Recommended areas with the higher quality score. The yellow areas are moderately recommended. The areas with Red shade are Non-recommended areas for students due to the low-quality score of that area. It can be inferred from the shade of an area that Cluster C5 is at Rank-1 and Cluster C2 is at Rank-5.


  • The Ranking of buffered areas around 5-clusters is the same in Vector and Raster analysis. However, the Raster analysis yields much more details in respect to locating the details within a cluster. For example, the areas within Cluster-1, Cluster-2, and Cluster-5 are having distinct color shades which signify the difference in the availability of amenities in these areas. But, Vector analysis ignores these minor details and classifies the regions in a cluster as of one shade.
  • Raffles Place has the best place for having student life experience. This cluster consists of all the essential student’s amenities for a day to day living.
  • NUS region has a comparatively lower rank. Improvement could be done on WIFI Hotspots and Healthy Dining.


Submitted By: Team Nucleus

Praveen Tiwari, Prashant Jain, Praman Shukla, Aravind Somasundaram and Sudharsan SundarRajan