Difference between revisions of "AY1516 T2 Team CommuteThere Project Further Analysis"

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This analysis aims to identify commuter patterns of each demographic groups - students, adults and elderly - as each group has differing interests and preferences in the places to frequent at. Data used for this methodology involves the ez-link and points of interests (POI) data. Given that the places that each demographic groups frequent at varies due to differing interests and preferences, to include points of interests (POI) in this analysis will be helpful to understand which places attract various groups of people at various periods of the week. With that, our team conclude that POI should be places that serve the primary needs of the people.
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Analysing commuter patterns is further segregated to two sub-methods:
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====1. Identifying common destination points====
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An initial analysis will be conducted to find out the common destinations that commuters travel to given that each demographic groups will have different needs and hence different places they frequent to. A heatmap of the common points will be visualized using QGIS. Areas with a darker intensity of colour would show the areas where many commuters alight at.
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====2. Identifying travel patterns====
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Travel patterns are categorized into four different segments: Island wide, inter town, intra town and most frequently travelled trips, where commuters may travel just within Tampines planning area, or within the east region, or island wide. To do so, we will use QGIS to map out.
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Analysis of commuter patterns is split into 4 segments:
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! Segments !! Description
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|style="text-align:Center;"|Island wide
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||Overall commuting activity for each demographic groups as a whole, regardless of place of origin. This will provide an overview of the  commuters’ travelling pattern in Singapore.
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|style="text-align:Center;"|Inter town
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||Travelling patterns of the commuters whose trips originate from Tampines planning area and end in the East region i.e Bedok,Paya Lebar, Changi, Pasir Ris
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|-
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|style="text-align:Center;"|Intra town
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||Travelling patterns of the commuters whose trips originate and end in Tampines planning area i.e Tampines, Simei
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|style="text-align:Center;"|Most frequent travelled trips
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||Commuters who made the same trip for at least four times in a week can be categorised as such. The data for each demographic groups are analysed based on weekdays which has most of the activities reflected on
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Revision as of 16:05, 15 April 2016

Commutetherelogo.png

HOME

PROJECT OVERVIEW

PROJECT MANAGEMENT

DOCUMENTATION

ANALYSIS & FINDINGS

Data Preparation

Initial Analysis

Further Analysis

Analyse Commuter Patterns

This analysis aims to identify commuter patterns of each demographic groups - students, adults and elderly - as each group has differing interests and preferences in the places to frequent at. Data used for this methodology involves the ez-link and points of interests (POI) data. Given that the places that each demographic groups frequent at varies due to differing interests and preferences, to include points of interests (POI) in this analysis will be helpful to understand which places attract various groups of people at various periods of the week. With that, our team conclude that POI should be places that serve the primary needs of the people.

Analysing commuter patterns is further segregated to two sub-methods:

1. Identifying common destination points

An initial analysis will be conducted to find out the common destinations that commuters travel to given that each demographic groups will have different needs and hence different places they frequent to. A heatmap of the common points will be visualized using QGIS. Areas with a darker intensity of colour would show the areas where many commuters alight at.

2. Identifying travel patterns

Travel patterns are categorized into four different segments: Island wide, inter town, intra town and most frequently travelled trips, where commuters may travel just within Tampines planning area, or within the east region, or island wide. To do so, we will use QGIS to map out.


Analysis of commuter patterns is split into 4 segments:

Segments Description
Island wide Overall commuting activity for each demographic groups as a whole, regardless of place of origin. This will provide an overview of the commuters’ travelling pattern in Singapore.
Inter town Travelling patterns of the commuters whose trips originate from Tampines planning area and end in the East region i.e Bedok,Paya Lebar, Changi, Pasir Ris
Intra town Travelling patterns of the commuters whose trips originate and end in Tampines planning area i.e Tampines, Simei
Most frequent travelled trips Commuters who made the same trip for at least four times in a week can be categorised as such. The data for each demographic groups are analysed based on weekdays which has most of the activities reflected on

Data

Data Provider Description

Living Analytics Research Center(LARC)

  • Ez-link and MRT dataset from 9 January 2012 - 15 January 2012
  • Geographical coordinates of the bus stops
  • Bus stops ID and name
  • Actual bus service number and ID

Data.gov

  • Planning area boundary shapefile
  • Points of Interests (POIs)
    • MRT stations
    • Schools (Primary, Secondary & Junior Colleges)
    • Shopping malls
    • Sports complex
    • Parks
    • Childcare
    • Community centers

Open Street Map (OSM)

  • Buildings shapefile
  • Road shapefile

Data

Data Provider Description

Living Analytics Research Center(LARC)

  • Ez-link and MRT dataset from 9 January 2012 - 15 January 2012
  • Geographical coordinates of the bus stops
  • Bus stops ID and name
  • Actual bus service number and ID

Data.gov

  • Planning area boundary shapefile
  • Points of Interests (POIs)
    • MRT stations
    • Schools (Primary, Secondary & Junior Colleges)
    • Shopping malls
    • Sports complex
    • Parks
    • Childcare
    • Community centers

Open Street Map (OSM)

  • Buildings shapefile
  • Road shapefile

Retrieved from ‘https://wiki.smu.edu.sg/ANLY482/index.php?title=AY1516_T2_Team_CommuteThere_Project_Further_Analysis&oldid=7406’