Difference between revisions of "AY1516 T2 Team WalkThere Methodology"

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[[AY1516_T2_Team_WalkThere_Analysis_Findings|<font color="#3c3c3c"><strong>ANALYSIS & FINDINGS</strong></font>]]
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[[AY1516_T2_Team_WalkThere_Project_Interim_Progress|<font color="#3c3c3c"><strong>ANALYSIS & FINDINGS</strong></font>]]
 
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[[AY1516_T2_Team_WalkThere_ReviewofPreviousWork|<font color="#3c3c3c"><strong>Review of Previous Work</strong></font>]]
 
[[AY1516_T2_Team_WalkThere_ReviewofPreviousWork|<font color="#3c3c3c"><strong>Review of Previous Work</strong></font>]]
 
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[[AY1516_T2_Team_WalkThere_Sponsor|<font color="#3c3c3c"><strong>About Stakeholders</strong></font>]]
 
  
 
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With the support from LARC, we were able to obtain ez-link transactions data from 20 to 26 January 2014. We have selected just a week of data in January 2014 because the travelling patterns for each week in a month are similar and there are neither no public holidays nor school holidays in the selected week for analysis. However, regardless of scaling down the data into just a week’s period, there are still millions of transactions presented. As such, analysis of the data will be further scaled down to grouping the transactions based on demographic profiles, followed by aggregating the timings of transactions to every 15 minutes given that the timings presented come in seconds.
 
With the support from LARC, we were able to obtain ez-link transactions data from 20 to 26 January 2014. We have selected just a week of data in January 2014 because the travelling patterns for each week in a month are similar and there are neither no public holidays nor school holidays in the selected week for analysis. However, regardless of scaling down the data into just a week’s period, there are still millions of transactions presented. As such, analysis of the data will be further scaled down to grouping the transactions based on demographic profiles, followed by aggregating the timings of transactions to every 15 minutes given that the timings presented come in seconds.
  
<u>2.Bus routes</u><br>
+
<u>2.Points of interests</u><br>
Busrouters.sg is an online portal where bus routes in Singapore are displayed in a map version. Data for bus routes is public available by the developer. The bus routes are updated to the latest bus profiles provided by the Land Transport Authority (LTA). However, the bus routes are published in json format. In order for us to conduct geospatial analysis using QGIS, a conversion of json to csv format is required. Besides having the bus routes plotted out in lines using QGIS, we realized that it is also important to have the bus stops included in the bus routes, where points of the bus stops and lines of the bus routes are snapped as a whole. Busrouters.sg has provided data of bus stops for each bus services. With that information, our team will be working on incorporating bus stops with the routes using PostGIS and QGIS.
 
 
 
<u>3.Points of interests</u><br>
 
 
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. As such, POI include:<br>
 
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. As such, POI include:<br>
  
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• Shapefiles for the identified POI can be retrieved from data.gov.sg, Openstreetmap, Onemap and LTA Data Mall<br>
 
• Shapefiles for the identified POI can be retrieved from data.gov.sg, Openstreetmap, Onemap and LTA Data Mall<br>
  
===Part 2: Site Visit - Identify gaps in infrastructure===
+
===Part 2: Identify Gaps in Infrastructure===
After conducting the first analysis where we identify areas with high volume of commuters, and commuters who travel short distances. The ez-link data will show us places that attract more elderly than students, for example, asking questions such as: “do those places serve the elderly well enough?
+
After conducting the analysis in part 1, various POIs for the various age groups are identified. Certain places will attract a particular age group more and this leads us to question whether these places serve that particular age group well enough?
  
The second part of analysis involves identifying gaps in the infrastructure within Tampines planning area. Why are people commuting by bus instead of walking? Will safety be compromised if people choose to walk? Or are there roads hindering the connectivity between the point of start with the destination? Singstat had the statistics of population for June 2015 published publicly. This information will aid in the understanding of how well-served are the living areas to the community. With that, we will conduct site visits to understand the situation better on ground level.<br>
+
The second part of analysis involves identifying gaps in the infrastructure within Tampines planning area. Why are people commuting by bus instead of walking? Will safety be compromised if people choose to walk? Or are there roads hindering the connectivity between the point of start with the destination? Thus, the second part of this analysis aims to highlight areas where the time taken to travel by bus is longer than walking and recommend improvements to the current infrastructure to encourage people to walk.
  
Data for this analysis includes:<br>
+
Our data for this analysis consists of the following:
  
1. Statistics on demographic profile<br>
 
(insert photos)x3<br>
 
Based on the charts above, the top 3 age ranges in all subzones lie in the older range, which is above 50 years old. This shows that Tampines is more of a mature estate and as such, it is important to have facilities and footpaths catered to the elderly. <br>
 
  
2. Linking of pedestrian walkways<br>
+
<u>1. Mapping Out Pedestrian Network</u><br>
As a tropical country located near the equator, Singapore receives her fair share of sunlight and often discourages people from staying outdoors for long due to the high level of humidity. With that, people may choose to commute by bus even for a short distance just to avoid the sun. Having covered linked ways and planting more trees, may help alleviate the situation through introducing more shades to pedestrians during daytime; and lamp posts to provide sufficient lighting at night for safer walking experience. Areas are obstructed with varied reasons, such as not enough lightings or shades and more, will be identified when we conduct site visit. LTA data mall has provided the following data:<br>
+
We will map out the pedestrian network around the various POIs identified. This data allows us to understand whether pedestrians can arrive at their destinations via walking. This data is available to us on OSM. Furthermore, we will also make use of raster analysis where we will draw a 5m x 5x square over residential buildings. The combination of raster layer and pedestrian network from OSM will allow us to find out the shortest walking path from the origin to the destination.
• footpath<br>
 
• covered linkway<br>
 
• lamp post<br>
 
• road crossing<br>
 
• pedestrian overhead bridge and underpass<br>
 
  
3. Pedestrian network<br>
+
Besides using raster analysis, we will also make use of other geospatial tools such as pgrouting to allow us to map out the shortest walking path. We will then compare the various walking distance to the distance travelled by the bus and find out the difference. An average walking speed for the different age groups will also be used to find out the time taken if one choose to walk. We will then compare the time taken if one choose to walk to the time taken if one travel by bus.  
This data allows us to understand whether pedestrians can arrive at their destinations via walking. However, as the data is not available to us publicly, we have to formulate this network at our own means. By connecting the road network and plotting the pedestrian connectivity, even walking through void decks, will be done after conducting site visits.<br>
 
  
 +
<u>2.Bus routes</u><br>
 +
Busrouters.sg is an online portal where bus routes in Singapore are displayed in a map version. Data for bus routes is public available by the developer. The bus routes are updated to the latest bus profiles provided by the Land Transport Authority (LTA). However, the bus routes are published in json format. In order for us to conduct geospatial analysis using QGIS, a conversion of json to csv format is required. Besides having the bus routes plotted out in lines using QGIS, we realized that it is also important to have the bus stops included in the bus routes, where points of the bus stops and lines of the bus routes are snapped as a whole. Busrouters.sg has provided data of bus stops for each bus services. With that information, our team will be working on incorporating bus stops with the routes using PostGIS and QGIS.
  
 
</font>
 
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=== Literature Study ===
 
=== Literature Study ===
To understand previous studies on walkability in Singapore and in other countries and to understand the types of infrastructures that can be introduced so as to be able to make recommendations to improve the connectivity between residential estates and points of interest.  
+
To understand previous studies on walkability in Singapore and in other countries, and the types of infrastructures that can be introduced so as to be able to make recommendations to improve the connectivity between residential estates and points of interest.  
  
 
=== Software Learning ===  
 
=== Software Learning ===  
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=== Data Collection ===  
 
=== Data Collection ===  
We will be collecting data through 2 methods:<br>
+
Ez-link data will be provided by LARC while points of interests data sets are publicly available on Openstreetmap, Data.gov.sg, LTA data mall and Onemap. Pedestrian network will be manually mapped out through conducting site visits and with the integration of road network.
1) Going to Tampines to manually collate data with regards to:
 
{| class="wikitable"
 
|-
 
! Physical infrastructure
 
||
 
* Walkways (Both covered and not covered)
 
* Traffic lights
 
* Pedestrian Crossings
 
* Overhead Bridges
 
* School Zones
 
* Street Lights
 
* Staircases
 
* Wheelchair-friendly slopes
 
|-
 
! Other types of data
 
||
 
Undeveloped footpaths
 
|}
 
<br>
 
2) Collecting Data from URA with regards to the demographics of each residential block to help us better understand the distribution of people in the area of Tampines that we are analysing on. <br><br>
 
3) Onemap:<br>
 
{| class="wikitable"
 
|-
 
! Points of Interests
 
||
 
* Elderly care
 
* Tampines North Primary School
 
* Dunman Secondary School
 
* Market and Grocery Stores
 
* Community Centres
 
* Parks
 
* Bus Stops
 
* Fitness Corner and Playground
 
* Sport facilities
 
|-
 
! Residential Blocks
 
||
 
To be identified
 
|}
 
  
 
=== Data Exploration ===
 
=== Data Exploration ===
Gaining insight from the data collected to understand how walkable residential areas are from the nearby facilities, cleaning and sieving out outliers, anomalies or wrong inputs.
+
Ez-link data of one week will be segmented into 3 sections for analysis: student, adult and elderly. Each team members has to identify trends and patterns for each profile groups with the use of analytics tools such as JMP and QGIS.
  
 
=== Geospatial Analysis ===
 
=== Geospatial Analysis ===
Using QGIS, for the following:
+
Using QGIS, for the following:<br>
Map out paths that residents may take from their houses to identified points of interest
+
• Commuters behaviours throughout the entire one week.<br>
Identify points of interest within 400 metres buffer radius from each residential block
+
Map out paths that residents may take from their houses to identified points of interest<br>
Understand the coverage of street lamps to analyse the safety of walking paths at night. Through measuring the radius of coverage and the height of the lamp post, we can understand how the distribution of the lamp post should be placed.
+
Understand the coverage of street lamps to analyse the safety of walking paths at night. Through measuring the radius of coverage and the height of the lamp post, we can understand how the distribution of the lamp post should be placed.<br>
  
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[[File:Team_WalkThere_Timeline.png|1200px|center]]
 
 
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Latest revision as of 22:12, 6 March 2016

TeamWalkThereLogo.jpg

HOME

PROJECT OVERVIEW

PROJECT MANAGEMENT

DOCUMENTATION

ANALYSIS & FINDINGS

Overview

Review of Previous Work

Data

Methodology

Methodology

Part 1: Identify 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. These patterns are recognized by areas with high volumes of people commuting by bus using the ez-link data. We aim to identify any commonalities where people travel for short distances, with only a few bus stops, within journeys, as well as recognize what are the common places of interests that the various demographic groups frequent at and at what periods of the week.


Our data for this analysis consists of the following:

1.Ez-link transactions
With the support from LARC, we were able to obtain ez-link transactions data from 20 to 26 January 2014. We have selected just a week of data in January 2014 because the travelling patterns for each week in a month are similar and there are neither no public holidays nor school holidays in the selected week for analysis. However, regardless of scaling down the data into just a week’s period, there are still millions of transactions presented. As such, analysis of the data will be further scaled down to grouping the transactions based on demographic profiles, followed by aggregating the timings of transactions to every 15 minutes given that the timings presented come in seconds.

2.Points of interests
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. As such, POI include:

• MRT stations
• Schools (primary, secondary, pre-tertiary and tertiary education)
• Shopping malls
• Sports complex
• Parks
• Childcare
• Community centers
• Shapefiles for the identified POI can be retrieved from data.gov.sg, Openstreetmap, Onemap and LTA Data Mall

Part 2: Identify Gaps in Infrastructure

After conducting the analysis in part 1, various POIs for the various age groups are identified. Certain places will attract a particular age group more and this leads us to question whether these places serve that particular age group well enough?

The second part of analysis involves identifying gaps in the infrastructure within Tampines planning area. Why are people commuting by bus instead of walking? Will safety be compromised if people choose to walk? Or are there roads hindering the connectivity between the point of start with the destination? Thus, the second part of this analysis aims to highlight areas where the time taken to travel by bus is longer than walking and recommend improvements to the current infrastructure to encourage people to walk.

Our data for this analysis consists of the following:


1. Mapping Out Pedestrian Network
We will map out the pedestrian network around the various POIs identified. This data allows us to understand whether pedestrians can arrive at their destinations via walking. This data is available to us on OSM. Furthermore, we will also make use of raster analysis where we will draw a 5m x 5x square over residential buildings. The combination of raster layer and pedestrian network from OSM will allow us to find out the shortest walking path from the origin to the destination.

Besides using raster analysis, we will also make use of other geospatial tools such as pgrouting to allow us to map out the shortest walking path. We will then compare the various walking distance to the distance travelled by the bus and find out the difference. An average walking speed for the different age groups will also be used to find out the time taken if one choose to walk. We will then compare the time taken if one choose to walk to the time taken if one travel by bus.

2.Bus routes
Busrouters.sg is an online portal where bus routes in Singapore are displayed in a map version. Data for bus routes is public available by the developer. The bus routes are updated to the latest bus profiles provided by the Land Transport Authority (LTA). However, the bus routes are published in json format. In order for us to conduct geospatial analysis using QGIS, a conversion of json to csv format is required. Besides having the bus routes plotted out in lines using QGIS, we realized that it is also important to have the bus stops included in the bus routes, where points of the bus stops and lines of the bus routes are snapped as a whole. Busrouters.sg has provided data of bus stops for each bus services. With that information, our team will be working on incorporating bus stops with the routes using PostGIS and QGIS.

Work Scope

Literature Study

To understand previous studies on walkability in Singapore and in other countries, and the types of infrastructures that can be introduced so as to be able to make recommendations to improve the connectivity between residential estates and points of interest.

Software Learning

Learn how to use the QGis software, both on the laptop as well as on the mobile phone (to aid data collection)

Data Collection

Ez-link data will be provided by LARC while points of interests data sets are publicly available on Openstreetmap, Data.gov.sg, LTA data mall and Onemap. Pedestrian network will be manually mapped out through conducting site visits and with the integration of road network.

Data Exploration

Ez-link data of one week will be segmented into 3 sections for analysis: student, adult and elderly. Each team members has to identify trends and patterns for each profile groups with the use of analytics tools such as JMP and QGIS.

Geospatial Analysis

Using QGIS, for the following:
• Commuters behaviours throughout the entire one week.
• Map out paths that residents may take from their houses to identified points of interest
• Understand the coverage of street lamps to analyse the safety of walking paths at night. Through measuring the radius of coverage and the height of the lamp post, we can understand how the distribution of the lamp post should be placed.