Sunny Singapore

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Introduction

Singapore is a leading economy in its region, but an astonishing number of its citizens fall below the first-world poverty line.

First-world poverty is a new concept to many, as it represents a group of citizens who are earning less than sufficient to cover the cost of living of their country of residence. For the fifth consecutive year, Singapore has held to its number one position as the most expensive city to live in. Although welfare is extensive in Singapore, it is definitely not exhaustive. Thus, this has become our main source of motivation for this project.

We seek to develop a tool that is easy use, analyse and to act on because we strongly believe that helping our communities should not be limited to the efforts of the government. We aim to design a platform where users can recognise the less-privileged areas and understand intuitively the type of support required. As such, any citizen, committees or even organisations can utilise this resource to lend a helping hand immediately and effectively.

Problem and Motivation

To build a dashboard that allows for:

• Profiling of neighbourhoods in Singapore by attributes: income, job specification, transportation, housing and qualifications
• Other sub-attributes to provide for analytical context: age, race/religion, expenditure, marital status, political views
• Infographic on First-World Poverty
• General guidelines on the support type for various helpgroups

Objectives

This project aims to provide insights into the following:

1. Income data by geography
2. Socioeconomic status by income
3. Support types categorised by socioeconomic situation
4. Scalable system to incorporate future data

Background Survey of Related Works

to be updated

Proposed Storyboard

#1: Title Screen

The title screen indicates the project objectives that the data visualisation tool seeks to achieve on the analysis of IFC Taiwan. As the project focuses on Taiwan branches, an image of Taipei 101 was used as a landing page.
The screens are implemented in a form of single-page website design, where each screen occupies the full screen and is navigated through scrolling action.

#2: Geographical overview

The overview will allow the user to see all respective branches in the map. There will be an option for modes of view e.g (relative sales performance), which builds a thematic map. Hovering or clicking on any branch will allow for a tooltip that displays the information corresponding to the mode.

#3: Sales Overview

This storyboard will provide visualizations for us to quickly identify top branches with high monthly sales. Upon selecting a branch, the monthly sales performance change across the years could be displayed using line graphs. It shows the overall monthly and yearly sales performance of all outlets using bar charts.

#4: Key findings and conclusion

The key findings and conclusion page display the insights that have been gathered from the visualisation tool, which aligns with the objectives of the project. The background of the page signifies the importance of tourist attractions in the selection of new outlets, which plays a big role in maximising the yield for an outlet.

Tools and Libraries

• Microsoft Excel
• R Studio
• Tableau
• Google Drive

Datasets

These are the datasets we plan to use:

Dataset	Rationale
Administrative Boundaries, Taiwan	A dataset containing SHP files of the administrative boundaries of taiwan (county, town, village) Used as a reference to digitize IFC branch trade areas
Branch location of IFC, Taiwan	A dataset containing the geographical information of each individual branch. Used as the main target of our project
Point of Interests , Taiwan	A dataset containing each individual Point-Of-Interests in Taiwan (e.g. ATMs, Amusement Parks, Banks) Used as features for analysis with regards to each branch
Outlets Monthly Sales Data	A dataset containing the monthly sales information of each individual branch Used to study the sales data along with the profile of each branch to generate yielding patterns (e.g. top and bottom performer)

Foreseen Technical Challenges

We encountered the following technical challenges throughout the course of the project. We have indicated our proposed solutions, and the outcomes of the solutions.

Key Technical Challenges	Proposed Solution	Outcome
Data is already pre-aggregated to display monthly sales	The dataset is given directly to us from IFC, and we are unable to change it. Thus, We shall utilize and do our best with the available data.	NA
Unfamiliarity in R Shiny	Watching video tutorials about R Shiny Independent learning on the design and syntax Peer learning and sharing Using Datacamp as our mentor	We managed to start using the packages quickly and suit our own project needs. Each of us work on different parts such as setting up, designing, logic and deployment. This speeds up our project progress.
Data Cleaning & Transformation Proposed Solution	Having a systematic process while working together in order to maximise efficiency e.g. taking turns to clean, transform and perform checks on the data to ensure accuracy	The adopted process was having clear instructions issued to each member in the team, along with maintaining constant communication with each other. In the event that the dataset is deemed too dirty to be usable, it was dropped along with sourcing for new data that would be a suitable replacement.
Lack of geospatial knowledge to understand the dataset initially	Attend SMT201 class to learn more, as well as reading up on resources given by Prof Kam to gain further contextual knowledge	NA
Digitising of trade areas from powerpoint slide to QGIS	The process is manual and we had to put in a lot of effort to convert the drawn polygon to data points in QGIS.	The data points can better allow us to generate insights on the profile of each outlet via its trade area.
Integrating Relevant Data from Multiple Sources Proposed Solution	Working together to decide on what data to extract or eliminate	NA
Determining the Most Effective Ways in Visualising the Data	Gain exposure to various forms of data visualisations - revisit course materials, assess existing libraries to gain inspirations.	NA

Project Timeline

Week 8: Complete detailed project proposal and gather datasets supervised by Alexia
Week 9: Clean datasets supervised by Pham
Week 10: Create data visualisation & consult on quality of work supervised by Parth
Week 11: Finalise storyboard teamwork with help of professor!
Week 12: Get ready for deadlines whoop!

References

• Tableau: https://www.tableau.com/learn/training
• R Shiny: https://shiny.rstudio.com/tutorial/

Ideation Drafts

To be updated

Comments

Feel free to leave comments / suggestions!