Difference between revisions of "WhereYouGeo Proposal"

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<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT DESCRIPTION</font></div>
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT DESCRIPTION</font></div>
  
−
As more Singaporeans are opting to take public transport for day to day trips, being able to understand the trip patterns of Singaporeans can help to identify interesting insights and these patterns can be used to help improve the environment of Singapore example: building more elderly friendly facilities, more buses services when school is over, etc.  
+
Singapore’s Public Transport System is the main mode of commuting around in Singapore. This project aims to examine the travelling patterns of commuters for both bus and train in February 2019. In particular, the influx and outflux from different areas (planning area/subzones); with the flexibility of choosing the type of day and timing. Furthermore, the team will employ spatial statistics to evaluate the probability of commuters travelling to a particular train station and to determine the dominant flow of commuters by bus. The application will be developed with R Shiny and Leaflet, allowing the use of interactive map and handling of large data sets. Data used in the application are gathered from sources like LTA DataMall, Singstat, Data.gov and OneMap. Insights gathered after the project has been completed were that most populated train stations during peak period were due to it being near an interchange with great transport accessibility or one area that is populated with office buildings. For bus, we inferred that most commuters congregate in bus stops reflecting dominant flows and great level of accessibility (e.g. Interchange), commuters usually also tend to travel shorter trips with bus as their mode of transport. The findings will be of great importance to government sectors, service providers and relevant geospatial industries. However, given its limitations, more research can be done to minimise generalisability by using specific transport concession data (e.g. students, senior citizens) to extract out more focused insights.
−
 
 
−
Our project aims to provide an application that will help various government sectors like HDB, URA, SLA and LTA to enable better planning and decision making where it will eventually impact Singaporeans in the future.
 
  
 
<!-- END OF PROJECT DESC--->
 
<!-- END OF PROJECT DESC--->
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<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT OBJECTIVE</font></div>
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT OBJECTIVE</font></div>
−
To build an application that does <b>flow analysis</b> using the data generated by trips made. The team also hopes to include more analytics features to bring more use cases for this application.
+
With the application, it can effectively identify the commuters’ key travelling patterns during various periods of the day, especially during the peak period. Furthermore, we will be able to identify the various hotspots and populated areas where commuters cluster in. Our primary analysis is to conduct spatial statistics to identify the probability of commuters visiting a particular MRT station and dominant flow of passenger commuting by Bus. Ultimately, it enables relevant sectors to better plan and allow for efficient decision making that will impact Singaporeans in the future. We will demonstrate the above mentioned with the usage of R Shiny application to explore and analyse large data sets involving both bus and train.
 
<!-- END OF PROJECT OBJECTIVE--->
 
<!-- END OF PROJECT OBJECTIVE--->
  
  
−
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT MOTIVATION</font></div>
+
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT STORYBOARD</font></div>
−
 
+
[[File:WMG_Storyboard1.JPG|center|950px]]
−
 
+
[[File:WMG_Storyboard2.JPG|center|950px]]
−
<!-- END OF PROJECT MOTIVATION--->
+
<!-- END OF PROJECT STORYBOARD--->
  
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">DATA SOURCES</font></div>
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">DATA SOURCES</font></div>
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|-
 
|-
 
|  
 
|  
−
<b>Title</b>
+
<b>Data Set</b>
 
||
 
||
 
<b>Format</b>
 
<b>Format</b>
 +
||
 +
<b>Data Attributes</b>
 
||
 
||
 
<b>Link</b>
 
<b>Link</b>
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||
 
||
 
CSV
 
CSV
 +
||
 +
Retrieving in Progress...
 
||
 
||
 
Data from LTA  
 
Data from LTA  
 
|-
 
|-
 
|  
 
|  
−
Bus Stop Locations
+
Bus Stops
 +
||
 +
SHP
 +
||
 +
* BUS_STOP_N (Bus Stop Number)
 +
* BUS_ROOF_N (Bus Stop Roof Number)
 +
* LOC_DESC (Location Description)
 +
* GEOMETRY (WGS84 Coordinates)
 +
||
 +
[https://www.mytransport.sg/content/mytransport/home/dataMall/search_datasets.html?searchText=bus%20stop LTA DataMall]
 +
|-
 +
|
 +
Passenger Volume by Bus Stops (Alternative Dataset)
 +
||
 +
CSV
 +
||
 +
* YEAR_MONTH (Year and Month of observation: yyyy-mm)
 +
* DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
 +
* TIME_PER_HOUR (Time of the day in hour)
 +
* PT_TYPE (Public Transport Type: BUS)
 +
* ORIGIN_PT_CODE (Origin Bus Stop Code)
 +
* DESTINATION_PT_CODE (Destination Bus Stop Code)
 +
* TOTAL_TRIP (Total amount of passengers who tap in from origin bus stop and tap out from destination bus stop)
 +
||
 +
LTA DataMall Dynamic Datasets
 +
|-
 +
|
 +
Passenger Volume by Origin Destination Bus Stops (Alternative Dataset)
 +
||
 +
CSV
 +
||
 +
* YEAR_MONTH (Year and Month of observation: yyyy-mm)
 +
* DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
 +
* TIME_PER_HOUR (Time of the day in hour)
 +
* PT_TYPE (Public Transport Type: BUS)
 +
* PT_CODE (Bus Stop Code)
 +
* TOTAL_TAP_IN_VOLUME (Number of passenger that tap in at a given bus stop)
 +
* TOTAL_TAP_OUT_VOLUME (Number of passenger that tap out at a given bus stop
 +
||
 +
LTA DataMall Dynamic Datasets
 +
|-
 +
|
 +
Train Stations
 
||
 
||
−
KML
+
SHP
 
||
 
||
−
LTA Datamall
+
* OBJECTID (Index)
 +
* STN_NAME (Station Name)
 +
* STN_NO (Station Number)
 +
* GEOMETRY (WGS84 Coordinates)
 +
||
 +
[https://www.mytransport.sg/content/mytransport/home/dataMall/search_datasets.html?searchText=mrt LTA DataMall]
 
|-
 
|-
 
|  
 
|  
−
MRT Locations
+
Passenger Volume by Origin Destination Train Stations (Alternative Dataset)
 +
||
 +
CSV
 +
||
 +
* YEAR_MONTH (Year and Month of observation: yyyy-mm)
 +
* DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
 +
* TIME_PER_HOUR (Time of the day in hour)
 +
* PT_TYPE (Public Transport Type: TRAIN)
 +
* ORIGIN_PT_CODE (Origin Train Station Code)
 +
* DESTINATION_PT_CODE (Destination Train Station Code)
 +
* TOTAL_TRIP (Total amount of passengers who tap in from origin train station and tap out from destination train station)
 +
||
 +
LTA DataMall Dynamic Datasets
 +
|-
 +
|
 +
Passenger Volume by Train Stations (Alternative Dataset)
 +
||
 +
CSV
 
||
 
||
−
KML
+
* YEAR_MONTH (Year and Month of observation: yyyy-mm)
 +
* DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
 +
* TIME_PER_HOUR (Time of the day in hour)
 +
* PT_TYPE (Public Transport Type: TRAIN)
 +
* PT_CODE (Train Station Code)
 +
* TOTAL_TAP_IN_VOLUME (Number of passenger that tap in at a given mrt station)
 +
* TOTAL_TAP_OUT_VOLUME (Number of passenger that tap out at a given mrt station)
 
||
 
||
−
LTA Datamall
+
LTA DataMall Dynamic Datasets
 
|-
 
|-
 
|}
 
|}
 +
<b> As API Key is required and constraint in time per each request of data (5 mins), our team is unable to provide the link for datasets from "LTA DataMall Dynamic Datasets". </b>
 
</div>
 
</div>
 
<!-- END OF DATA SOURCES--->
 
<!-- END OF DATA SOURCES--->
 +
 +
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT TIMELINE</font></div>
 +
[[File:WhereYouGeoSchedule.png|center|1000px]]
 +
<!-- END OF PROJECT TIMELINE--->
  
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT KEY CHALLENGES</font></div>
 
<div style="background: #14a2d1; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT KEY CHALLENGES</font></div>
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|  
 
|  
 
1.
 
1.
 +
||
 +
Not familiar with spatial analysis method and its related R packages
 +
||
 +
As the team is new to geospatial, there are certain concept that the team is not knowledgeable in. <br>
 +
In addition, the team is not well verse with R programming language. <br>
 +
||
 +
Do more self-learning via online research or datacamp <br>
 +
Look through online resources like R documentation <br>
 +
Look through hands on exercise to help us gain more knowledge in both theory and R language
 
||
 
||
 
Fill me in
 
Fill me in
 +
|-
 +
|
 +
2.
 +
||
 +
Lack of data for analysis
 
||
 
||
−
Fill me in
+
As Singapore does not collect much data on human traffic flow, this might be a potential challenge for the team to conduct spatial analysis.
 
||
 
||
−
Fill me in
+
Look for relevant agencies or find different data sets to merge and conduct spatial analysis.
 
||
 
||
 
Fill me in
 
Fill me in
Line 125: Line 216:
 
1.
 
1.
 
||
 
||
−
Fill me in
+
GIS Lounge
 +
||
 +
[https://www.gislounge.com/overview-flow-mapping/ Overview of Flow Mapping]
 +
|-
 +
|
 +
2.
 +
||
 +
YouTube (This project is part of "Open Data Challenge for Public Transport in Tokyo 2017".)
 +
||
 +
[https://www.youtube.com/watch?v=qAxHFK64kbM Heavy 4D Tokyo] <br>
 +
[https://tokyochallenge.odpt.org/2017/award/index-e.html Winning projects for the "Open Data Challenge for Public Transport in Tokyo 2017".] (Translation may be required as web-page is in Japanese)
 +
|-
 +
|
 +
3.
 +
||
 +
Anita Graser
 
||
 
||
−
Fill me in
+
[https://anitagraser.com/2017/10/08/movement-data-in-gis-8-edge-bundling-for-flow-maps/ Movement data in GIS #8: edge bundling for flow maps]
 
|-
 
|-
 
|}
 
|}

Latest revision as of 15:00, 14 April 2019

WhereYouGeoLogo.png

HOME

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER


PROJECT DESCRIPTION

Singapore’s Public Transport System is the main mode of commuting around in Singapore. This project aims to examine the travelling patterns of commuters for both bus and train in February 2019. In particular, the influx and outflux from different areas (planning area/subzones); with the flexibility of choosing the type of day and timing. Furthermore, the team will employ spatial statistics to evaluate the probability of commuters travelling to a particular train station and to determine the dominant flow of commuters by bus. The application will be developed with R Shiny and Leaflet, allowing the use of interactive map and handling of large data sets. Data used in the application are gathered from sources like LTA DataMall, Singstat, Data.gov and OneMap. Insights gathered after the project has been completed were that most populated train stations during peak period were due to it being near an interchange with great transport accessibility or one area that is populated with office buildings. For bus, we inferred that most commuters congregate in bus stops reflecting dominant flows and great level of accessibility (e.g. Interchange), commuters usually also tend to travel shorter trips with bus as their mode of transport. The findings will be of great importance to government sectors, service providers and relevant geospatial industries. However, given its limitations, more research can be done to minimise generalisability by using specific transport concession data (e.g. students, senior citizens) to extract out more focused insights.


PROJECT OBJECTIVE

With the application, it can effectively identify the commuters’ key travelling patterns during various periods of the day, especially during the peak period. Furthermore, we will be able to identify the various hotspots and populated areas where commuters cluster in. Our primary analysis is to conduct spatial statistics to identify the probability of commuters visiting a particular MRT station and dominant flow of passenger commuting by Bus. Ultimately, it enables relevant sectors to better plan and allow for efficient decision making that will impact Singaporeans in the future. We will demonstrate the above mentioned with the usage of R Shiny application to explore and analyse large data sets involving both bus and train.


PROJECT STORYBOARD
WMG Storyboard1.JPG
WMG Storyboard2.JPG
DATA SOURCES

Data Set

Format

Data Attributes

Link

LTA Concession Data

CSV

Retrieving in Progress...

Data from LTA

Bus Stops

SHP

  • BUS_STOP_N (Bus Stop Number)
  • BUS_ROOF_N (Bus Stop Roof Number)
  • LOC_DESC (Location Description)
  • GEOMETRY (WGS84 Coordinates)

LTA DataMall

Passenger Volume by Bus Stops (Alternative Dataset)

CSV

  • YEAR_MONTH (Year and Month of observation: yyyy-mm)
  • DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
  • TIME_PER_HOUR (Time of the day in hour)
  • PT_TYPE (Public Transport Type: BUS)
  • ORIGIN_PT_CODE (Origin Bus Stop Code)
  • DESTINATION_PT_CODE (Destination Bus Stop Code)
  • TOTAL_TRIP (Total amount of passengers who tap in from origin bus stop and tap out from destination bus stop)

LTA DataMall Dynamic Datasets

Passenger Volume by Origin Destination Bus Stops (Alternative Dataset)

CSV

  • YEAR_MONTH (Year and Month of observation: yyyy-mm)
  • DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
  • TIME_PER_HOUR (Time of the day in hour)
  • PT_TYPE (Public Transport Type: BUS)
  • PT_CODE (Bus Stop Code)
  • TOTAL_TAP_IN_VOLUME (Number of passenger that tap in at a given bus stop)
  • TOTAL_TAP_OUT_VOLUME (Number of passenger that tap out at a given bus stop

LTA DataMall Dynamic Datasets

Train Stations

SHP

  • OBJECTID (Index)
  • STN_NAME (Station Name)
  • STN_NO (Station Number)
  • GEOMETRY (WGS84 Coordinates)

LTA DataMall

Passenger Volume by Origin Destination Train Stations (Alternative Dataset)

CSV

  • YEAR_MONTH (Year and Month of observation: yyyy-mm)
  • DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
  • TIME_PER_HOUR (Time of the day in hour)
  • PT_TYPE (Public Transport Type: TRAIN)
  • ORIGIN_PT_CODE (Origin Train Station Code)
  • DESTINATION_PT_CODE (Destination Train Station Code)
  • TOTAL_TRIP (Total amount of passengers who tap in from origin train station and tap out from destination train station)

LTA DataMall Dynamic Datasets

Passenger Volume by Train Stations (Alternative Dataset)

CSV

  • YEAR_MONTH (Year and Month of observation: yyyy-mm)
  • DAY_TYPE (Day type of observation: WEEKDAY / WEEKENDS/HOLIDAY)
  • TIME_PER_HOUR (Time of the day in hour)
  • PT_TYPE (Public Transport Type: TRAIN)
  • PT_CODE (Train Station Code)
  • TOTAL_TAP_IN_VOLUME (Number of passenger that tap in at a given mrt station)
  • TOTAL_TAP_OUT_VOLUME (Number of passenger that tap out at a given mrt station)

LTA DataMall Dynamic Datasets

As API Key is required and constraint in time per each request of data (5 mins), our team is unable to provide the link for datasets from "LTA DataMall Dynamic Datasets".

PROJECT TIMELINE
WhereYouGeoSchedule.png
PROJECT KEY CHALLENGES

No.

Key Technical Challenges

Description

Proposed Solution

Outcome

1.

Not familiar with spatial analysis method and its related R packages

As the team is new to geospatial, there are certain concept that the team is not knowledgeable in.
In addition, the team is not well verse with R programming language.

Do more self-learning via online research or datacamp
Look through online resources like R documentation
Look through hands on exercise to help us gain more knowledge in both theory and R language

Fill me in

2.

Lack of data for analysis

As Singapore does not collect much data on human traffic flow, this might be a potential challenge for the team to conduct spatial analysis.

Look for relevant agencies or find different data sets to merge and conduct spatial analysis.

Fill me in

PROJECT TOOLS AND TECHNOLOGIES
WhereYouGeoTech.png
REFERENCES

No.

Website Name

Link

1.

GIS Lounge

Overview of Flow Mapping

2.

YouTube (This project is part of "Open Data Challenge for Public Transport in Tokyo 2017".)

Heavy 4D Tokyo
Winning projects for the "Open Data Challenge for Public Transport in Tokyo 2017". (Translation may be required as web-page is in Japanese)

3.

Anita Graser

Movement data in GIS #8: edge bundling for flow maps

COMMENTS

No.

Name

Date

Comments

1.

Insert your Name here

Insert Date here

Insert Comment here

2.

Insert your Name here

Insert Date here

Insert Comment here

3.

Insert your Name here

Insert Date here

Insert Comment here



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