Analytics And Intelligent Systems

NUS ISS AIS Practice Group

Spatialite – Spatially Lighting You in A Dynamic Way (Spatio-Temporal Analytics) — June 13, 2017

Spatialite – Spatially Lighting You in A Dynamic Way (Spatio-Temporal Analytics)

Street lights in Singapore is valuable but expensive assets for the city. However, according to a recent study published by Science Advances (Jun 2016) – Singapore was named the country with the worst level of light pollution in the world with a pollution level of 100 per cent. The use of artificial light here far exceeds the level of light pollution tolerable per capita.

Today’s street lights are a lot to manage, and tend to function inefficiently by wasting energy when they are on. Hence, Remote Control Monitoring System (RCMS) was designed with energy savings as the goal. Although RCMS presents opportunities for saving energy cost, street lighting can be further optimize by taking into account, the trend of people who are outdoor at night. With the advantage of geospatial analytics, we would like to introduce: Street LightTime + Weather +Flow rate of Pedestrians



Geospatial Analysis Techniques Used  :

  1. Points to Line
  2. Buffering
  3. Overlaying
  4. Clipping

Geospatial Analysis Used:

  1. Heat Map
  2. Cluster and Outlier Analysis
  3. Hot Spot Analysis
  4. Directional Distribution Analysis (for movement data)

Summary of Findings:


  • Light up in the presence of a person or car, and remain dim the rest of the time.
  • Autonomous dimming when no movement detected.
  • Predict the movement pattern and light up ahead.
  • Consider the distance between each lamppost through geo analysis


  • Improve the quality of life by reducing artificial light
  • Reduce light pollution level
  • Significant energy saving

Welcome to have a full view of the ArcGIS Map Journal from here.

The presentation slide is also available from this link .

Presented to you by: Team GEOSPIES


Promoting Healthy Lifestyle in Singapore — May 21, 2017

Promoting Healthy Lifestyle in Singapore

In line with HPB’s action plan to promote healthy lifestyle and active ageing to build a nation of healthy people and mitigate the ageing population issues, our team has made use of geospatial temporal intelligence to provide useful information and analysis to support this initiative and make it easier for us to take ownership of our own health.

We do complete view of the health related amenities on common visited places followed by the analysis such as daily behavior of people, steps analysis, ellipses, directional distribution, hotspots to get more insights.

Average working hours in Singapore:


  • Based on the statistics provided by the Ministry of Manpower*, the average working hours in Singapore for a full time is about 48 hours in a week (highlighted in red), which has exceeded the normal working hours of 40 hours in a week.
  • Although the average working hour has an improving trend over the years, people in Singapore still spend a substantial amount of time at work.
  • This leads to a challenge for working individuals to maintain a healthy lifestyle and work life balance.


Awareness of Health related Amenities:

  • To make it easier for working individuals in Singapore to maintain a healthy lifestyle amidst their busy work schedule, there is a need to raise awareness on the list of health related amenities based on their travel patterns.
  • In particular, in their course of travel between their workplace and home, it would be useful for them to be more aware of the complete list of health promoting amenities which are near and could be accessed easily to help them to overcome the time constraints and obstacles to achieve a health lifestyle.

Data Collection & Preparation


To kick start our initiative, we collected location data using both Openpath and moves phone application for our team. We then merged our dataset with available datasets from other teams. As the majority of the other team’s dataset is Openpath data, we decided to focus on Openpath data. Based on the merged dataset using Openpath, we performed the following actions:

Data Cleansing

  • For records with missing names, we tried to identify the names using the type of phone and populate these records with dummy names.
  • For records with email ids appearing under names, we populated the names as dummy names.
  • There were records where the location points are outside of Singapore (e.g. Hong Kong, Batam, etc). As the scope of our analysis is in Singapore, we decided to remove such location records.

Data Transformation

  • As the timestamp was in UTC format, we performed a data transformation to translate the time to Singapore time (UTC+8) to facilitate better analysis.

Feature Creation

  • We created a date and Day of week field (Eg Monday, Tuesday etc) from the transformed timestamp.
  • We created a list of GIS shape files e.g. gyms, healthier eateries, parks, sports fields to support our healthy life style analysis
  • We also needed data from moves to support our analysis to optimize and improve the number of walking steps achieved. As the scope of our steps analysis is on working professionals, we decided to leverage on the data collected from moves (e.g. number of steps) and attempt to derive the no of steps with the location coordinates and time. As the required data was in separate tables in moves (e.g. summary, activities, places, storyline tables) and there was no single table with all required data attributes, we use SQL to extract the data from different tables and handle records with null location coordinates.

ETL (Extract, Transform, and Load):


During the data preparation stage using data from moves apps, we extracted the activities, places and storylines from the full summary. As the data is being split across multiple tabs and is not a 1 to 1 join, the ETL process is used to merge and load back into the ArcGIS platform.

Location by Day of Week

The locations are segregated by the day of the week (e.g. Monday, Tuesday, etc.) to analyze travel patterns according the the day of the week.


Mon-Thursday : Similar patterns can be observed.


Friday : Dispersal of points can be concluded.


Saturday-Sunday: Concentration of points at NUS are visible.

Mean Centre and Directional Distribution


  • For each day of the week, the mean center and directional distribution analysis are applied on the location data to investigate how the locations are dispersed over Singapore.
  • We can see that there is a spread in the northeast/southwest direction during the weekdays and greater concentration over the southwest region during weekends.
  • The potential application based on the directional trend is that it allows us to know which area to concentrate our healthy life style efforts based on days of week, weekdays and weekends respectively. Eg we could organize exercise events at east coast park as a big team during weekday.

Easy Access to Health Promoting Facilities

Across Singapore, there are many amenities which could encourage people living in Singapore to live healthy. The amenities considered here are:

  1. Parks
  2. Gym
  3. Healthy eateries
  4. Sport Facilities


Buffering Travelling Pattern


  • A 500 meters buffer area around the travelling pattern of the class is generated as a starting point to facilitate further analysis of travelling pattern intersecting with available amenities/facilities in the subsequent slide.
  • This buffer symbolizes the deviation of an individual from his/her usual traveling pattern.
  • 500m is chosen because it is a comfortable distance for walking to the next desired location.

Intersection With Amenities/Facilities


  • The 500m buffer which was created is used to intersect the available facilities.
  • Those facilities which are out of the buffer zone will not be considered and removed from the map.
  • The remaining facilities are the ones which people can be easily accessed based on their travel patterns.

Individual Travel Patterns-Person A


  • The previous analysis made used of location data derived from many people. This analysis could also be applied to a single individual to explore the facilities which he/she can potentially use.
  • For example, the travelling patterns of person A is plotted on the map and the same workflow is applied. The results will show the health promoting amenities/facilities which person A can access with ease.

Individual Travel Pattern – Person B (by day of week)


For another example, this is the typical travelling pattern for person B on Thursday. For this individual on Thursday, the available easy accessible facilities are shown on the map which he can go to. This visualization can be extended to include any days of week.

Step Analysis

  • Usually step challenges consider only the total number of steps within a single day, without any location based analysis of the steps. Our analysis attempt to analyse steps of an individual comparing to other individuals:22
  • Based on the visualization of the number of steps (based on the size of the circle) for our team after work(ie after 6:30 pm) over a period of two weeks, it shows that our team mate TC and Yubo has a greater number of walking steps as compared with the rest of our team.
  • Upon further interview with them, we realized that Yubo tends to walk 15 minutes from Buona vista MRT to his home although there is a bus service to his home. As for TC, he has a tendency to walk from the customer’s office to his office after work. For the rest of our team, the number of steps was relatively small due to the tendency to travel by public transport /car.
  • Hence, in order to improve our walking steps to achieve a healthy number of steps amidst our busy schedule, it is recommended that we learn from the above model team members and refine our travelling patterns such that there is more walking activity or not driving to work during certain days of the week.
  • It is important to note that walking has numerous benefits. It strengthens our heart, lowers disease risk, helps to lose weight, prevents dementia, tones our legs, bums, tums, gives us more energy, and makes us happy. Hence, with a slight tweak in our travelling patterns and lifestyle, it would lead to a big change in our health condition.

Step Analysis – Gertis Ord Gi (Hotspot)

  • Applying the Gertis Ord Gi analysis on the step feature class for all the location point. We pinpoint the hotspots and coldspots in terms of number of steps recorded.
  • Cold spot would mean that an individuals is recording lesser steps at a particular location. Hot spot would mean that an individual is taking greater steps at a particular location.


For example, we can see that there are some cold spot around the Marina Bay Financial Centre while there are hotspots around the Tanjong Pagar area.


  • These two sets of hotspot and coldspots are derived from different individuals.
  • Relating this to the hotspot analysis earlier, we can see that the individual situating at Marina Bay Financial Centre is rather stationary compared to the other individual which is situated around the Tanjong Pagar area.
  • In this situation, reminder can be given to encourage the individual to move more rather than to stay stationary for long periods of time. As per advice from health experts, long periods of sitting day-in and day-out can seriously impact our health and shorten our lives.

Limitation and Possible improvements

  • Lack of dataset features/attributes
    • Age, Gender, etc. are not available in the current phase, but it will be helpful to include these attributes for analysis in future studies.
  • Limited data
    • The sample can be considered small due to the time constraints. Increase of sample size would be good for future studies.
  • Data are taken from Openpath and Moves as the only limited sources.
  • Ratings for health facilities (eg Accessibility, price, health grade, etc. ) could be added as attributes for data fields for better analysis in future studies.

Submitted by team:

  • Lee Tai Ngiap
  • Lwi Tiong Chai
  • Gello Mark Vito
  • Zheng Kaiyuan
  • Huang Yubo
Spatio-Temporal Analysis Of Real Estate Market Using Geographic Information System — May 8, 2017

Spatio-Temporal Analysis Of Real Estate Market Using Geographic Information System


           The objective is to analyse the spatial distribution of  real estate price and its variability over time. 

           Real Estate is a special type of commodity . The location of real estate plays an important role in determining the price of dwelling. The determination of prices in a given area is closely related to the spatial distribution of properties. Some of the considered important factors are MRT LINE , Amenities like Hawker centers etc 

Data Sources:

          Transaction for private dwellings was fetched from Urban Development Authority of Singapore and also the MRT Line.

Address Geocoding:

           The location of a real estate is presented in the form of street name which should be converted to Lat Long points. We have used Python Geocode api for converting the street name to Latitude and Longitude .

Exploratory analysis:

        We have analysed total number of transaction taken place in each year for the dwelling with respect to a particular range of Unit Price.


Estimation of Distribution in Unit Real Estate Price:

 Spatial interpolation method Inverse distance weighting is used to generate models of selected dwellings. 

         The first issue appeared during data loading . Transactions of various dwelling located in same building is linked to the same address point and had a same position.Thus the average of values are taken to convert to a single value. Maps of the growth of dwelling values throughout the 2014-2017 characterized by a significant price increase.


These maps shows that there is an increase in transactions around MRT lines and also significant price increase between 2016 and 2017 within unit price range of 2300 – 2600


In particular, the use of a single color scale on the “interpolation map” allowed for the sharp increase in the unit prices of dwellings in late 2016 and 2017 to be captured, as also evidenced by graphs . Simultaneously, areas where the increase was found to be the highest and places that do not respond to trends in the market were indicated.

Team Name: HackEarth

Team Members: Navneeth Goswami , Vignesh Srinivasan , Praveen Kumar

Spatial – Temporal Analytics of Students Desirability Level for Living in Singapore — May 7, 2017

Spatial – Temporal Analytics of Students Desirability Level for Living in Singapore



Singapore is a highly-developed city in the South East-Asian region and houses many excellent Universities. Hence, it attracts thousands of students from around the world annually. These students are from a diverse set of socio-economic backgrounds and hence have a varied taste in choosing an accommodation.

We were very interested in trying to understand the influential role played by the amenities or services in the selection of accommodation by Students. With this study, we would like to model the most desirable areas that our target group (ISS EBAC 04 associates) should find most suitable for their needs.


Our objective is to derive the most desirable places to live in Singapore for NUS students based on certain set of amenities

The selection of the variables was done keeping the general psyche of the students in mind. We arrived at the following Key service variables that can influence the decision made by the students:

➢ House Rental Prices

➢ Distance to:

  • MRT and LRT Stations
  • Community Clubs
  • Park Connectors
  • Healthy Dining Places


The Starting point was the cumulative data collected by our class mates. A total of “7747” data points were collected by all the students taken together. We segregated the data points to derive the following

Home Location: Using Time between 12 AM and morning 8 AM. We found a total of “233” Data Points as a good approximate representation of the entire class

Lunch Location: Using Time between 1 PM and 3 PM. “731” points were found for this category

The external data of Amenities/Facilities that we used to overlay on this base data were:

  • Community Clubs
  • Park Connectors
  • Health Dining Options
  • MRT and LRT
  • House Rental Prices
  • Singapore District

Data Sources


✓  ArcGIS online



Screen Shot 2017-05-08 at 1.14.52 AM


 House Rents

House Rent data was collected and we joined the attribute table of House rent layer with that of the district layer to give the district wise rental. We guessed approximate values for rent for locations for which the data was not available. However, note that this was for the completeness of map only and does not represent true rental of those locations.

The Most Expensive areas are among the least desirable of places for renting out a   home.   

Screen Shot 2017-05-07 at 11.19.44 PM

 Separation into Zones

We partitioned Singapore map based on the rental prices into 3 “zones” so that we can easily manage 3 mutually exclusive zones according to affordability. 3 Sub Zones were:

  • $1500 – $1850 – Low Priced Zone
  • $1900 – $2200 – Medium Priced Zone
  • $2200 – $2700 – High Priced Zone

Partitioning was done using the Lasso Tool and we Rasterised the 3 zones to be able to demarcate them individually. We could then intersect any of these zones with the buffered polygons of shortlisted Amenities/Facilities. The zones are as displayed in the map below:

Screen Shot 2017-05-07 at 11.19.58 PM

 MRT concentration

The MRT concentration Heat Map gives us an insight into the Governments’ prioritisation for residential and business areas. We can clearly see that the services are concentrated in the “Raffles Place”, “Punggol” and “Choa Chu Kang” areas.“Raffles Place” is the financial hub of the island and hence this is a highly unlikely area for residentialproperties.Screen Shot 2017-05-07 at 11.20.08 PM

 MRT Vs Home

We made a polygon buffer taking 1.5 km distance for the radius from the exact coordinates of the MRT stops. This, when over-laid with the “Home Locations of Students” gave us a good insight that MRT locations are definitely one of the major deciding factors as most students were found to be residing with in the 1.5 km raster of MRT. However, on closer Inspection some densely packed home locations near “Pasir Panjang road” were found to be far from the MRT Locations.

Screen Shot 2017-05-07 at 11.20.21 PM

The map below is a definite indication that we would have to find some other decisive services or amenities in addition to the MRT services to give us a better understanding about students residing in Pasir Panjang.

Screen Shot 2017-05-07 at 11.20.31 PM

Healthy Eating Places

To stay healthy, it is essential to eat healthy! So we wanted to get an Insight into the eating habits of the NUS Students and their decisions to take the accommodation.

The following Map gives the “Lunch locations” layer over-laid on polygon buffer of Healthy Eateries taking 1 Km distance from the exact coordinates of the eateries. This gives us the locations for lunch of the students on a college day as well as a non-college day. So, we can infer that most of the students are within 1 km radius of healthy dining options

Screen Shot 2017-05-07 at 11.20.43 PM

Community Clubs

Community Clubs (CCs) are recreational centers, having various activities related to hobby, fitness, sports and short courses on different topics. Even for CCs, we created a polygon buffer of 1 km radius which gives the ideal reach to a person in the neighborhood.

The following map gives an overlay of home locations of students over the Community Center buffer. It was found that most students resided within 1 Km distance from the nearest community clubs.

Screen Shot 2017-05-07 at 11.21.14 PM

Park Connectors

People prefer park connectors near their homes and therefore we have rated the students’ residences about 1Km within park connectors as more desirable for the people. The Following Map Does Cover most of the home locations of students with in the 1 km distance to park connectors

Screen Shot 2017-05-07 at 11.21.29 PM

Low Price Desirability Map

For deriving the following map, we took all the layers above and considered intersections of rental zones with any two or all the facilities/amenities in the vicinity.

Hence, we took multiple intersects of all the polygon buffers derived for amenities/facilities (2.2.4 – 2.2.7) in various combinations.

This helped us geographically isolate areas in the map which had all or some of the facilities contained within them.

Most Desirable Area (in Dark Green) is the intersection of:

  1. MRT+
  2. Healthy Dining Places +
  3. Community Clubs +
  4. Park Connectors +
  5. Polygon for Low Price Zone (from derivation 2.2.2)

The other colors representations are as follows:
Light Green: intersection of Low Rent Zone + MRT + Healthy Dining Places                Yellow: Intersection of Low Rent Zone + Community Clubs + Healthy Dining Places

Surprisingly, the most desirable areas with all amenities with lowest rent zones had just few students residing in them

Screen Shot 2017-05-08 at 12.18.34 AM

 Medium Price Desirability Map

We repeated the above intersections for medium rental zones. This time we found several students residing on medium rent locations with all amenities or at least 2 amenities. The colours representing different intersections are same as above (where low rent zones are now replaced with medium rent zones)

Screen Shot 2017-05-08 at 12.18.46 AM

High Price Desirability Map

We now applied intersections with “High Rent Zone”. Now, we see only a few students’ homes in the most desirable but highly expensive areas.

Screen Shot 2017-05-08 at 12.18.56 AM

Overall Desirability Map

We Overlay the Maps in 2.2.9 to 2.2.11 on each other. This gives a union of three maps as the zones are disjoint.

From this map, we can clearly see that we have captured most number of students within most desirable areas will all amenities in around 1 km radius of the amenities (ignoring rent) in dark green. This was the advantage of color coding locations as per the intersection of the number of amenities and not price.

This map shows that:

  • Majority of students reside in dark green areas, i.e. locations which have all amenities.
  • Some students reside in light green regions indicating they have facilities like MRTs andHealthy Dining places closer to their houses and


Only a few students live in brown colored locations where in Community Clubs and Healthy

Dining options are in about 1 km distance of their houses. However, these houses were spread across all (high, medium and low) rental prices

Screen Shot 2017-05-08 at 12.19.11 AM

However, it was interesting to see that the area near Pasir Panjang was highly populated despite not being the most desirable, owing to the fact that all amenities are away from this location according to our model. This brings us to the final addition to our analysis.

Screen Shot 2017-05-08 at 12.19.21 AM

Accommodation Proximity to NUS

We added a polygon to represent NUS which acts as a Geo-Fence (in brown) for the spread of NUS. We made another buffered polygon with a range of 1 Km (in Blue) extending out from this geo fenced area. This area accounts for an extremely desirable area for our target group.

The group of students residing in this area have given up most other amenities in favour of proximity to the university. This explains the query raised above of why some students stay at Pasir Panjang even when the amenities are far-offScreen Shot 2017-05-08 at 12.19.30 AM.png


The entire analysis of the geospatial data gives us the following insights into the student choice of accommodation:

  1. Most Students prefer being close to the University at the cost of lesser amenities
  2. Most students reside in locations of medium rent. Again, this is a Proximity based decision forthe students. The locations closer to the university have higher desirability irrespective of rent
  3. Very few students live in locations of low rent, however these places are at longer distance fromNUS
  4. The few student houses are found in high rent locations and these mainly because they arewithin a kilometer radius from NUS
  5. Majority of the students’ residences have proximity to amenities to at least some of theamenities from MRT station, Community center, Healthy dining facilities and Park connectors.
  6. However, those staying closest to NUS campus must travel more than a kilometer to avail all theamenities and are in general paying high rent. They are still able to utilize amenities likecommunity centers and healthy dining facilities.
  7. The Priority that we found in decreasing order of preference are:
    1. Proximity to NUS
    2. Proximity to MRT
    3. Proximity to Healthy Eateries
    4. Others


To read our complete report please refer to the link below:

1) Report: Spatial_Temporal_Analytics_of_Students_Desirability_Level


1.Abhilasha Kumari

2.Ashok Eapen

3.Pranav Agarwal

4.Rohit Pattnaik

5.Snehal Singupalli


To Recommend the Best Places in Singapore for Students Based on Vector and Raster Quality Score —

To Recommend the Best Places in Singapore for Students Based on Vector and Raster Quality Score


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 (
  • Map Exploration (



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, 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

Spatial-Temporal Analytics with Students Data to recommend optimum regions to stay —

Spatial-Temporal Analytics with Students Data to recommend optimum regions to stay

Objectives & Motivation:

Objective is to find a convenient place for students to stay based on data collected in Singapore

To any international student coming into Singapore needs robust information about convenient places to rent. To proceed with this we analyzed student’s movement data which was collected by students of NUS belonging to ISS school, exploratory spatial data analysis was done on that to find the pattern and insights. We considered that as a student basic amenities would be economical stay, cycling path, library, MRT closeness, parks, hawker center.

Data Sources:

Dataset description Data Source URL
Data points of all NUS-ISS  students IVLE (Apps used: Openpaths)
Dwelling data
Hawker Centres
Cycling Path
Park Connector Loop




OpenPath data with student’s personal location information was collected for the month of April. Data Cleaning was done to refine the data. As the data was stored on multiple mobile devices the date and time formats were inconsistent, thus, all the date and time field values were converted to a single standard format. To do more analysis, separate columns were created for date and time. The outliers were treated using R and the data points outside Singapore were ignored. Modified Name and MailID columns to obtain missing details using EXCEL.

Exploratory Analysis of Students Data (with derived fields):

Mean Centre:


From the above exploration, it is evident that most student stay at the university, travel to the residence and they use MRT most of the time. As students would prefer to stay near university, place near to MRT station and have the potential to use Cycling path, our aim is to find and suggest better zones for student life. Thus, we need to add these layers to the student data and carry out analysis.


To get more insights into a suitable dwelling, we tried to separately analyze various datasets such as HDB dwelling population data, Hawker center location, MRT station, and cycling paths.

Geographically Weighted Regression:

To find insights from student data using HDB and population data layer, geographically weighted regression is performed using variables as shown below with an assumption that data points having the timestamp of late night represent student’s home. Spatial join was done on hawker center, dwelling and open path student data layers to obtain final GWR model.

Explanatory Variables:

The count_  variable is the number of student data points per polygon, Count_1 is the number of Hawker centers per polygon, showsSHAPE_Area of the dwelling layer and HDB the total count of HDB per person.

GWR Model

The GWR results show that model can perform with moderate accuracy having adjusted R2 of 0.31.

Spatial Autocorrelation (Moran’s I) tool on the regression residuals was run to ensure that the model residuals are spatially random. Statistically significant clustering of high and/or low residuals (model under- and overprediction) indicates that our GWR model is not accurate enough to predict.

GWR Fail

The below map indicates regions with localized R square to find HDB population using students data. The Dark Red regions are the places where the model predicts with higher accuracy and the blue regions are the place where its prediction is poor.


As the model residuals are not random based on the spatial autocorrelation ( Morons I ), it cannot be used for prediction purposes. This model was just build to study the insights of students data with another layer of data.

Thus, to find convenient places for students, the places were ranked using student data points and factors like MRT, cycling path and hawker centers availability.


Ranking zones based on Hawker Centres:

To get a density of hawker centers in each we calculated a new field which was used for ranking:

Ranking of the classified field by using reclassify tool to rank the regions based on the hawker center density which shows places ranked according to economic zones for food.


Ranking zones Based on proximity to cycling paths:

The areas are ranked as per its proximity of cycling paths and data is converted to raster data and the ranked.


Ranking zones based on MRT by reclassify:

The easy access to public transport is considered one of the major consideration while choosing a place to stay and have used proximity to MRT as a factor to rank areas. MRT location data is converted to raster data to rank areas based on its proximity to MRT stations


 Final Ranking:

The final rank for each zone in Singapore was calculated based on the average of other three ranks (MRT, Hawker center, and Cycling path). Areas which are ranked good can be most favorable for staying. This final ranking can help to choose a place for staying based on individual priority.


 Final Rank




Using the final ranking, we can recommend to a new student coming to study at NUS a convenient place to stay, considering MRT, Cycling and Food places in the ranking.

As expected the better ranking zones are crowded near NUS itself and there are other places also being suggested by the ranking.

As a future scope of this story, we can add a configurable element which can replace Hawker center layer with many another layer like Libraries, Parks, HDB rental prices, Bus stop layer to form an ideal tool for the upcoming student to use it.

For detailed analysis, please read the entire report: – Spatial-Temporal Analytics with Students Data

Explored and submitted by: 





LI MEIYAO (A0163379U)

Spatio-Temporal Analysis of ISS Students and their Surroundings —

Spatio-Temporal Analysis of ISS Students and their Surroundings


  • 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



  • 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:
  • Click on video / YouTube Link below showing the movement

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:
  • YouTube Link below showing the movement

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:

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:

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:

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:

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:

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:

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.


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:



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


  • 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










To find most accessible study areas for students in NUS

Problem Space:


Analysis Strategy:

  • What? – Availability of study areas for students
  • Where? – Inside NUS campus
  • Why? – The various reasons for the preferred locations
  • How? – Finding the clusters around various study locations

Data Exploration and Feature Addition:

  • Data source is OpenPaths
  • The class data was cleansed and the records pertaining to geographical coordinates of Singapore/NUS was chosen
  • The initial analysis of the sampled data was performed using various tools such as R , carto, ArcGIS to study the geographic spread
  •  A new dataset was formulated for representing the various study centres in NUS
  • Transformation was performed to achieve the variables in the necessary format for the geo-visualization
  • Reverse geocoding was performed on the dataset using the ‘ggmap’ package in R and the corresponding locations were obtained

Preliminary Analysis:


  • Carto was used to analyse the spread of the data points during the class hours and after the class hours
  • The results of the analysis portrayed that after the class hours the population spread is more at University town owing to availability of more study areas and facilities



Base Map Creation and Polygon Generation:


Addition of Layers:


Step 3: Loaded class data (master class namely) and converted coordinates for visibility

Density Analysis:



Step 6: The high-density area was in and around National University of Singapore. We can conclude that the data points are either working in NUS or students of NUS.

Assumption and Addition of new Layer:


Hot Spot Analysis:

Step 8: Hot Spot Analysis was performed, Arc Tool Box->Spatial Statistics Tools->Mapping Clusters->Optimized Hotspot Analysis


Model Diagram – Proximity Analysis:


Step 9: Proximity Analysis was performed to find most accessible study areas for students in NUS, Arc Tool Box->Analysis Tools->Proximity->Near

Proximity Analysis Results:


Inferences/Solution Outline:

  • Comparing the inference obtained from CARTO and Model built in ARCGIS, We can find that the students only focus on University Town
  • From the model it is evident that there were other study areas that could be preferred as the data points were close to these study areas
  • The students can explore other areas like FOE,SOC etc for holding discussion sessions
  • We can propose a new study area at an optimal location based on the geographic distribution of student data in case the number of students enrolled increases over a period of time


  • It’s a student’s location data
  • Sample size is limited to ISS students only
  • Non availability of accurate shuttle bus timings data
  • Non availability of students enrolled in each and every faculty

Team Name: Incognito

Team Members: Pankaj Mitra, Deepthi Suresh, Anand Rajan, Neeraja Lalitha Muralidharan, Nandini Paila Reddy

Lightweight Electric Scooter stands in NUS —

Lightweight Electric Scooter stands in NUS


The goal of our project is to provide flexible mobility solution such as an electric scooter within the school campus. With the electric scooter, students can avoid sweating it out rushing off to classes under the hot sun, avoid waiting 10minutes for a missed bus and avoid getting stuck at a crowded bus stop during peak times. Flexible mobility also allows students to cut across narrow paths to avoid long detours that are defined by the NUS shuttle bus route.

图片 1

1 Problem Space

Compared to a bulky bicycle, the electric scooter is also lightweight, foldable and versatile, allowing navigation along narrow sheltered paths and hills, and easy lift while escalating staircases.

In this project, we want to find an optimal location for designating our electric scooter stand, so that students can rent them at a central location that is at the center of the most visited spots in campus.

图片 2

We followed an input-process-output framework for our project. The input phase consists of data collection, data cleaning, finding new data layers that complement our objective and solution.

The process consists of exploring the distribution of our data, understanding our data, exploring the patterns and generating some initial hypothesis of the activity of our students. It also involves generating heatmaps, finding hotspots and generating relevant statistics that can be used to support our objective of locating the right site for an electric scooter stand.

The output consists of identifying the optimal location for our rental stands, and generating relevant insights about distribution of our data points.

2 Data cleaning

We collected coordinates of public bus stops near NUS campus, as well as coordinates for NUS internal shuttle bus stops.

We split the date and time into two separate columns for the data from moves.

Since Openpaths time is GMT+8, we converted all times to Singapore timestamp by adding 8 hours. Missing values was deleted. Data was not imputed.

After compiling moves and Openpaths data, we removed repeated IDs or overlapping data.

Last, we converted students names to student email ID as index.

3 Data Exploration Process

Our database consists of data of 52 students from Moves and Openpath. During data exploration process, we use Tableau to explore the data. The number of records for each ID shows below.

图片 3

We also used the ‘Find Identical’ tool to filter out the 3601 different locations out of 7650 rows of data. This indicates that more than 53% of data are repeated checked-in points.

Exploring values of longitude and latitude:

  • To study how change of longitude and latitude changes with real life distance.we plotted the latitude and longitude values on google map to get a feel.
  • We discovered that 0.0001, 4th decimal place indicates a change of 10-20metres.In other words, 10 to 20 metres means the person is simply moving to different parts within the same facility.
  • For a change in 0.001, 100 – 200metres. So we can easily detect when a person is moving when we see a change in long/lat values by 0.001
  • Using this heuristic, it is easy for us to zoom in on time periods when change in movement is occurring during analysis.

4 Model Building

Use of ArcCatalog to build a default Geodatabase, for ease of managing and storing data.

To see the distribution of our data points for all 52 IDs across two weeks, we plotted all our data points on a Singapore basemap.图片 4

Next, we generated a heatmap by counting the number of points. From the heat map we can see where are the popular check-in places, or the places with high level of activity for past 2 weeks. As seen, the heatmap shows that the area with highest level of check-ins by ISS students for the two weeks is at NUS Clementi area. We can see numerous scattered spots around Singapore. These spots could be attributable to the workplaces of part-time students or residences of students staying outside campus. The high level of activity at the NUS-Clementi area aligns well with our objective to provide a flexible mobility solution for the students. We narrow our scope to the NUS campus where highest level of activity occur. Next we generated a word-cloud to have an overview of frequently checked-in places, both within and outside NUS. The word-cloud is generated from a frequency table we prepared by filtering and selecting the long/lat values that occurred frequently in our database.

图片 5

The word cloud is simply a rough guide of which are the places with high level of activity, and is no way a part of our statistical analysis yet. The size of the word indicates the frequency of check-ins. We still need to validate these places have high level of activity by running proper statistical analysis methods on ArcGISOnline.

The reason behind wanting to know the location of the most visited places is such that we can find the mean center of these popular spots, in order to designate our electric scooter rental stands.图片 6

Using Statistical Analysis to find popular check-in places

Step 1: we begin by plotting the data points that are within the NUS campus area. We generated a heatmap on ArcgisOnline to display the hot spots. The heatmap is shown below:

图片 7

Step 2: We did the hotspot analysis and generate the hexagons. Each hexagon is 23metre wide. And we have a total of 299 hexagons. There is a circle/bubble within each hexagon. The size of the bubble corresponds to its value. The smallest value or bubble has a value of 1, and the largest value is 225. Average value across all bubbles is 4. The purpose of this step is not to generate insight, but rather it is a statistical method that would be followed up in step 3.

图片 8

Step 3: Based on the result of hotspot analysis, we filter the Statistical Significance and Number of points. By setting high statistical significance setting the number of points to be >= 6, we get 17 popular spots.

Next we ranked the 17 popular spots in descending order.

图片 10On the NUS map, we placed green markers at these 17 popular spots. The map below shows a few of the markers.

图片 11

We split our area of analysis into two separate regions for the campus. The reason for this is that there is a huge separation or distance between the area at PGP/NUH and the region at central library. Using “Mean Center” tool to do spatial analysis to find out the mean center of the popular locations, we designated a scooter station at a place near to this point represented the mean center. The purple five-pointed stars represent the mean center we get.

图片 12

After generating our mean center, we realised that the mean center can be located in the middle of a road or contoured hill. To address this issue. We did a buffer zone of radius of around 10 metres that contains the neighbouring area, such that we can recommend an electric scooter spot at the perimeter of our buffer zone.
For our project we did not designate at stand at NUS U-town, which is pretty far out from the rest of the campus. The reason being that ofo services are already set up in NUS U-Town.

图片 13

We calculate the distance between two stations to confirm that these two scooter stations are quite far apart. Measuring the distance apart with the blue line drawn on the map, the two stations are approximately 1km apart.

图片 14

5 Conclusion

We believe the little bits of time saved through flexible mobility will reduce procrastination and improve productivity  for time-packed students.

Travel to the school gym you want at YIH or U-town without hesitation of time wastage or carrying loads around.

Travel to your favourite food vendors across different faculties with the increased mobility.

Travel across faculties for electives without being late for 20 minutes waiting for or switching buses in campus.

Arrive at your destination without needing to cool down your body during hot seasons.

Frees up your mind to carry your laptop around in school on a electric scooter, instead of having the burden of carrying it on your back while walking.

The public online map:

Submitted by:
Ng Wei Ping[A0056380H]
Que Qiwen[A0163391E]
Vincent [A0150347L]
Wang Ruoshi [A0163338A]
Zheng Weiyu [A0163212R]

Using Geospatial Analytics to identify Optimal Locations for Accommodation in Singapore for NUS Students —

Using Geospatial Analytics to identify Optimal Locations for Accommodation in Singapore for NUS Students

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


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).


Geo Processing:



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


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


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.



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]