Objective:

To find most accessible study areas for students in NUS

Problem Space:

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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:

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  • 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

Modelling:

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Base Map Creation and Polygon Generation:

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Addition of Layers:

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

Density Analysis:

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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:

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Hot Spot Analysis:

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

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Model Diagram – Proximity Analysis:

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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:

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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

Limitations:

  • 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

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