Due to occasional train faults and poor knowledge of the best routes, students often spend longer times travelling to school. Another issue is that students who are new to Singapore may not be aware of amenities which are available in their immediate vicinity. The objectives of this project are to help optimise travel times for students and provide helpful information about facilities near their home or travel routes.
We obtained our data from mainly these few sources:
- Moves & OpenPaths (Consolidated Students’ Coordinate Data)
- Twitter API (Tweets on Reports of Train Disruptions, from SMRT’s Official Account)
- Data.gov.sg (Shopping Mall & Hawker Centre Data)
It helps to use certain tools to understand how students are travelling around Singapore. The tools which we will be using are heatmaps, cluster maps and Anselin’s Local Moran’s I for Local Spatial Autocorrelation.
From the heatmap, we are able to tell which parts of the island are heavily distributed with movement activity. One very visible hotspot is the area around NUS, at the southwest part of Singapore.
This cluster map provides us with the number of data points which are present on the map and cluster them together. As a result, we can clearly compare one hotspot with another easily. It is also possible to tell how many outliers we have on the map. To explore further, we can click on any cluster to zoom into individual points which are contributing to any given cluster.
Anselin’s Local Moran’s I for Outlier Analysis
Using Anselin’s Local Moran’s I Index to help us distinguish outliers, we can tell how long students typical spend at each location, as compared to their neighbours. Red points are high-high points, which tell us that these students spend a longer duration, as compared to their neighbours and that there is a high concentration of these students in this particular location. Blue points are low-low points and signify the contrary. We consider these points to be outliers on the map.
We now attempt to address our objectives in this project, by examining the impact of train faults on students’ travelling plans and also to propose useful information on nearby amenities.
By plotting the lines where the disruptions have occurred before, we can see which line is the most prone to train faults (at least in the past 2 years). The area around Bishan station on the North-South line is also quite prone to such incidents. We have also discovered that train disruptions usually occur on Mondays and Thursdays. Although it is not practical to avoid travelling on those days, we advise that students be prepared for an increased probability of incidents.
Using medoid clustering to determine the centres of the clusters of students’ movements, we created a 3km geofence to highlight which shopping malls are closest to these clusters. As shown in the above diagram, we identified 3 main clusters in Singapore – NUS, CBD and in the east-side. There are not many shopping malls shown in the clusters – this is understandable as these clusters are residential districts. There are customer ratings available for each shopping mall available in Singapore as well.
Hawker centres, on the other hand, tell a different story. There are an abundance and relatively equal distribution of hawker centres within the clusters. The government has made a conscientious effort to ensure that affordable food eateries are available within a walking distance, to all residing in these residential districts. Customer ratings for these hawker centres are available too.
Proposal of Alternative Travelling Route
We have attempted to propose an alternative travelling route in the event of a train disruption, from Raffles Place to NUS ISS. As this route planning algorithm is in its infancy, we have only proposed this one alternative, which is only possible via car/taxi.
In this project, we achieved our objectives of proposing alternative routes and helping students plan their travel routes better in the event of train disruptions, as well as suggesting helpful information on nearby amenities.
We hope you enjoy exploring these maps and discovering further helpful geospatial insights about Singapore.
The published map app may be viewed at this link.
Published by Team TRUMP [EBAC 04]
Sindhu Rumesh Kumar