WhereYouGeo Proposal

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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 (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

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

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

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