WhereYouGeo Proposal

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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

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

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

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