Difference between revisions of "WhereYouGeo Proposal"

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* TOTAL_TRIP (Total amount of passengers who tap in from origin train station and tap out from destination train station)
 
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1.
 
1.
 
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Fill me in
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GIS Lounge
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[https://www.gislounge.com/overview-flow-mapping/ Overview of Flow Mapping]
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2.
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YouTube (This project is part of "Open Data Challenge for Public Transport in Tokyo 2017".)
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[https://www.youtube.com/watch?v=qAxHFK64kbM Heavy 4D Tokyo] <br>
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[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)
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3.
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Anita Graser
 
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Fill me in
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[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]
 
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Revision as of 20:07, 17 February 2019

WhereYouGeoLogo.png

HOME

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER


PROJECT DESCRIPTION

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.

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.


PROJECT OBJECTIVE

To build an application that does flow analysis using the data generated by trips made. The team also hopes to include more analytics features to bring more use cases for this application.


PROJECT MOTIVATION


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

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

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

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

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

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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