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Research Paper

PROBLEM & MOTIVATION

In the year 1960, Singapore was facing a huge crisis. Many people were living in unhygienic slums and crowded squatters with only a meager 9% of Singaporeans lived in government flats, while everyone else yearned for a place to call home sweet home.To counter this crisis,, the Housing & Development Board (HDB) was incorporated on 1 February, 1960 and tasked with the critical mission of solving the crisis ar hand. In a mere span of 10 years, HDB had attained its goal and solved the housing crisis.

However, in 1993, HDB stopped deciding the prices of new apartments based on construction costs, instead they decided based on market prices. Prices of resale flats and new flats entered in a vicious circle, rising 50% in just 6 months of 1993 and tripled to 1996. This move closed the price gap between small and large flat types and hub pricing have never been he same again.

Thus, as graduates to be who will most likely enter the job market soon and start looking for a place to call home, we felt that it would be interesting to look into the historical flat data so that we can see which flats in Singapore would be the most value for money so that we can actually get a home which is worth its investment. We also felt that it would be fun to explore trends in the resale flat prices and see what factors really affect the prices of HDBs and see how much of a premium people attach to amenities such as proximity to public transport, schools and etc...

OBJECTIVES

In this project, we are interested to create a visualisation that helps users perform the following:

1. View the trend in the resale prices over time with respect to major events that happened in the year (Example: 1993 Change in Pricing Model,1997 Recession
2. Identify which areas are more expensive and possible reasons for the high value (Proximity to public transport, Schools, Shopping Malls, Park)
3. To find out if getting a specific HDB is a good investment based on the number of year left on the lease and which locations may potentially be more profitable based on the age of the HDB.

By using our visualisation, we will be able to give users a better idea of the pricing situation of the resale HDBs so that people can make better decisions in the HDB which they want to choose to call their home. Such as when is the best time to buy as HDB; where are the most profitable / cheapest locations; whether a HDB is expensive

SELECTED DATASET

In our analysis, we will only be using data within the year of 1990 - 2017. The rationale for the range of data selected is as follows:

The dataset for analysis will be retrieved from multiple databases, as elaborated below:

Dataset/Source	Data Attributes	Rationale Of Usage
Resales flat prices from Mar 2012 onwards (https://data.gov.sg/dataset/resale-flat-prices?resource_id=83b2fc37-ce8c-4df4-968b-370fd818138b ) Resales flat prices from 2002 - Feb 2012 (https://data.gov.sg/dataset/resale-flat-prices?resource_id=8c00bf08-9124-479e-aeca-7cc411d884c4 ) Resales flat prices from 1990 - 1999 (https://data.gov.sg/dataset/resale-flat-prices?resource_id=adbbddd3-30e2-445f-a123-29bee150a6fe )	Month Town Flat Type Block Street Name Storey Range Floor Area (Sqm) Flat Model Lease Commence Date Resale Price (S$)	This dataset will be used as a main source of information in our analysis to understand the number of HDB around Singapore from 1990 to 1999, 2002 to Feb 2012 and Mar 2012 onwards respectively.
Bus Stop Names and Locations (https://www.mytransport.sg/content/mytransport/home/dataMall.html# )	Bus Stop Number Bus Stop Roof Number Bus Stop Name X Y Latitude Longitude	This dataset aims to complement the main dataset by providing detailed information about the latitude and longitude of the bus stops located around HDB. We use a javascript script to convert all the X and Y coordinates to EPSG:4326 latitude and longitude coordinates.
Mrt Stations Names and Locations (https://www.mytransport.sg/content/mytransport/home/dataMall.html#)	MRT Station Number MRT Station Name X Y Latitude Longitude	This dataset aims to complement the main dataset by providing detailed information about the latitude and longitude of the MRT stations located around HDB. We use a javascript script to convert all the X and Y coordinates to EPSG:4326 latitude and longitude coordinates
Schools (Primary, Secondary, Junior College) Names and Locations (https://data.gov.sg/dataset/school-directory-and-information)	School Name Address Postal Code Planning Area	This dataset aims to complement the main dataset by providing detailed information about the address of schools located around HDB.

BACKGROUND SURVEY OF RELATED WORKS

There are many charts and visualisations available which illustrates the various trends of house prices and index. We have selected a few of these to study and learn before we begin developing our own visualizations.

Related Works	What We Can Learn
An Analysis of the trend and correlation between resale prices and flat production Source: http://www.teoalida.com/singapore/hdbprices/	The use of 2 different chart types with a secondary axis is effective in illustrating the correlation between resale prices and flat production. The colours used are striking and contrast well with each other. There are dips in both variables which are not explained in the infographic itself (E.g. 1997 Asian crisis, 2003 SARs outbreak). This events could be incorporated into the charts to make it more informative.
An interactive heatmap of Singapore’s house prices in various districts Source: https://www.srx.com.sg/heat-map	This heatmap uses colours appropriately so that the house prices of each district can be identified intuitively (Red means expensive, blue means cheap, orange means mid-range) The use of filters allows user to find out more about the price distribution of each house type easily. When user mouseover a district on the heatmap, the corresponding district on the legend is highlighted. This improves usability as users do not have to match district numbers manually.
An interactive visualization of house prices along MRT stations Source: https://www.srx.com.sg/mrt-home-prices/property-listings-near-east-west-line	This visualization makes use of unique ways to illustrate the relation between nearby facilities and house prices. The separating of the various MRT lines using filters at the top prevent too much information from being shown in one page

PROPOSED STORYBOARD

Proposed Layout	How Analyst Can Conduct Analysis
600px	Introduce analysts to the topic of terrorism and the objectives of the visualization project Upon clicking "Explore", analysts will then begin their process of exploration
600px	When a user enters our app, we will shoe them a brief history of HDB followed by the problem that most young people are facing with regards to understanding the HDB situation. The 3 insights we are trying to show will be displayed as 3 clickable buttons so that it is easy for a user to know what exactly he wants to look for at a glance.
600px	In the next phase of the exploration, data will be displayed based on the countries that analysts have selected previously. The radar chart shows 6 different governance indicators that defines how well a country is governed. The closer the area is to the center of the chart, the less well governed the country is. Upon mouse-over of each area, one can also retrieve the exact values of each governance indicator. The bubble plot groups attacks based on 3 main categories - country, target victim and political terror scale. Firstly, by grouping based on country, one can better visualize the number of attacks that took place in each country and contrast it with the data presented in the radar chart. Secondly, by grouping based on target victims, one can also establish the most common targets of these terrorist attacks that took place. This will bring analysts further in the data exploration, especially if there are high numbers of attacks targeting at certain groups of people. Lastly, grouping by political terror scale allow the analysts to contrast information with the radar chart. In addition, by looking at the count of attacks in each scale, one can also identify interesting patterns. In the bubble plot, each bubble represents an attack. When the analyst mouse-over each bubble, they can see more information about each attack and this helps to bring context to the analyst in their data exploration phase. Due to technical limitations and to avoid excessive clutter on the page, only attacks that have resulted in more than 15 deaths will be shown. This is in conjunction with the assumption that analysts would be more interested to look at attacks that have caused great harm to the public. By looking at both charts, the analyst will then be able to compare and establish possible linkages between how well a country is governed and the number of terrorist attacks that took place in the country. As such, these 2 charts are placed side by side to assist the analyst in their data exploration. Similarly to the previous page, the bar chart at the bottom of the page will show the number of attacks that took place for all selected countries.

ADDRESSING KEY TECHNICAL CHALLENGES

The following are some of the key technical challenges that we may face throughout the course of the project:

Key Technical Challenges	How We Propose To Resolve
Unfamiliarity of Visualization Tool Usage	Independent Learning on Visualization Tools Peer Learning
Data Cleaning & Transformation	Work together to clean, transform and analyze the data
Unfamiliarity in Programming using Javascript & D3 Libraries	Attend D3 Programming Workshop Independent Learning on D3 Libraries & Technical Tools Peer Learning
Unfamiliarity in Implementing Interactivity and Animation Tools/Techniques in Visualization App	Develop a Storyboard/Design Flow Assign members to specialize on Interactivity/Animation Techniques

PROJECT TIMELINE

The following shows our project timeline for the completion of this project:

TOOLS/TECHNOLOGIES

The following are some of the tools/technologies that we will be utilizing during the project:

• D3.js
• Proj4.js
• Google Maps Distance Matrix API
• Google Search API
• Github
• Node.js
• Angular.js

REFERENCES

• DrWealth’s infographic (https://www.drwealth.com/singapore-property-prices-along-mrt-lines/)
• Data Gov Database (https://data.gov.sg)
• D3.js (https://d3js.org/)
• Examples By Mike Bostock (https://bost.ocks.org/mike/example/)

OUR BRAINSTORMING SESSIONS

The following are some of the proposed storyboard that we designed during our brainstorming sessions:

800px

800px

800px

COMMENTS

Feel free to comment to help us improve our project! (: