Group02 proposal

Team

Proposal

Poster

Application

Research Paper

PROBLEM & MOTIVATION

Problem
When it comes to purchasing or renting a property, there are many factors that go into a buyer’s consideration before he makes the final decision. The primary concern for buyers is the pricing of the property [1]. However, our group identified that there are also secondary concerns such as the weather and the amenities available that do influence the buyer’s final decision to purchase the property. There are limited tools available to help property buyers to identify areas that suit their needs/preferences best. The current tools that are available are only optimal to suit one category of concern, but fails when we try to use more categories to make our visualization.

For example, different people have different preferences for the weather. Some may prefer sunny weather while others prefer it to rain all the time. Currently, we are able to find weather information, and property prices information, but not both at the same time.

Motivation
Through our visualization, the group hopes to assist users to be able to easily visualize a home of his dreams in different aspects beyond mere prices. Our visualisation will incorporate data from property prices, weather and amenities to identify the accessibility of the area and help our users to justify the real value of the property. With our visualisation dashboard, users will be able to identify properties that they truly desire.

OBJECTIVES

We aim to provide an interactive visualization dashboard to assist property seekers with identifying the housing area that best suits their needs with visualization information such as:

1. Insights on the weather (which consists of the Rainfall Precipitation, Temperature and Wind speed) of each postal area.
2. Insights on the available amenities within the proximity of the selected geo boundary.
3. Insights on the distribution of commercial and residential prices for each postal area over the past few years.

Target Group:

• Property buyers with weather preferences
• Commerical buyers who wants to open a shop

DATASET

The Data Sets we will be using for our analysis and for our application is listed below:

Data/Source	Variables/Description	Rationale Of Usage
Temperature and Rainfall Data (Jan 2012 - Dec 2019) (http://www.weather.gov.sg/climate-historical-daily/)	Stations Date Daily Rainfall Highest 30-min/60-min/120-min Rainfall (mm) Mean/Minimum/Maximum Temperature (°C) Mean/Max Wind Speed (km/h)	This dataset covers a good time series of Singapore's weather from 2012 to 2019 across different weather categories. Our team wish to spot the trend or pattern of Singapore's climate in every town if possible.
Amenities Location Data (https://api.data.gov.sg/v1/environment/rainfall) (https://api.data.gov.sg/v1/environment/air-temperature)	Station ID Station Name Latitude Longitude	The data set will be used to anchor the amenities available for the selected property in a specified range
Commercial Data and Residential Data (Jan 2012 - Dec 2019) (https://spring-ura-gov-sg.libproxy.smu.edu.sg/lad/ore/property_market/index.cfm)	Project Name Address No. of Units Area (sqm) Type of Area Transacted Price ($) Nett Price($) Unit Price ($ psm) Unit Price ($ psf) Sale Date Property Type Tenure Completion Date Type of Sale Purchaser Address Indicator Postal District Postal Sector Postal Code Planning Region Planning Area	This dataset covers a good time series from 2012 to 2019 and the breakdown by subzone/planning area and postal code to visualize in a map. These transaction data will be used together with the weather data to explore potential relationships. We will also use the amenities data to identify the accessibility of the property to help our users to justify if the property worth its price.

BACKGROUND SURVEY OF RELATED WORK

Below are a few visualizations and charts we considered making for our projects.

Visual Considerations	Insights / Comments
Title: Qualitative Thematic Map Source: https://mapdesign.icaci.org/2014/12/mapcarte-353365-life-in-los-angeles-by-eugene-turner-1977/	Change Pros: go change go change Cons: go change
Title:
Title:
Title:

KEY TECHNICAL CHALLENGES & MITIGATION

No.	Challenge	Description	Mitigation Plan
1.	Software Challenge	Unfamiliarity of visualisation tools such as R, R Shiny, Tableau.	Github Learning Stackoverflow research Self-directed and peer learning Watch video tutorials from YouTube Hands-on practice using the different training platforms such as Data Camps
2.	Programming Challenge	Inexperince with data cleaning and transformation using R	Trial and error Read online articles and forums for guidance Watch video tutorials on how to fully utilise packages such as lapply, tidyr and dplyr
3.	Workload Constraint	Time and Workload Constrains	Design reasonable project timeline based on everyone's ability and capacity. Set milestones and adjust the timeline accordingly based on the team's progress.
4.	Dataset Complexity	Our have different data from multiple sources in multiple different formats, hence we foresee a huge challenge in standardizing the data Note: Our current dataset is looking at 49 areas over the spread of 8 years of data, for every year there are 12 months of data. This gives a total of 4,704 CSV files to consolidate and clean for weather data alone.	Make use of data preparation tools such as tableau prep Make use of our database management skills to normalize all data tables into third normal form

STORYBOARD

Dashboards	Description
Dashboard 1: Qualitative Thematic Map of Singapore property	Our group plans to do an interactive Qualitative Thematic Map which reflects different faces based on the year, price, weather, amenities filters adjusted by the users. This chart will show the data at a high level for users to identify which area meets their needs in a glance. Users can also view the map based on the postal area or zone. Filters used includes: Sliders Year Price Amenities Single Dropdown List Weather Map level of detail Multiple Drowndown List Property Type Based on User’s adjustment for the filters, the map would reflect the user 3 different data types: Weather When the postal area/zone’s selected weather is above the median of Singapore’s data over the selected year, the chart will reflect a circle face shape. However, if the postal area/zone’s selected weather falls in the median or is lower than the median of Singapore’s data over the selected year, the chart will reflect a circle face shape with devil horns. Pricing When the postal area/zones have houses with pricing over the selected year that meets the requirements of the user based on his/her filter, the chart will reflect a smiling face. However, if the postal area/zones have houses with pricing over the selected year that is lower than the requirements of the user based on his/her filter, the chart will reflect a blank face. Lastly, if the postal area/zones have houses with pricing over the selected year that is more than the requirements of the user based on his/her filter, the chart will reflect a sad face. Amenities When the postal area/zone’s number of amenities in the selected year meets the requirements of the user based on the filter, the chart will reflect the faces to be shown in green colour. However, if the postal area/zone’s number of amenities does not meet the requirements of the user, the chart will reflect the faces in blue colour. With this chart, users will be able to identify and shortlist the area(s) that meets their requirements the best.
Dashboard 2: Property prices in Subzones{Based on Users selected areas in storyboard 1}	The purpose of this chart is to show a detailed breakdown of the properties that meets the requirements of the Users based on the filter of Storyboard 1 in the User’s shortlisted area(s) in the chart from storyboard 1. This chart shows all the properties and their prices for all the properties that meet the requirements of the Users based on the filter’s range and the shortlisted area(s). The filters in this chart include Year of the transaction, Property Type, Property’ transacted Pricing and users can also choose how they would like to sort the properties based on their preferences. The chart will be able to help users better understand the property prices based on his/her shortlisted area(s) and make a decision on which area’s property to purchase. X-Axis: Transacted Properties’ Name Y-Axis: Transacted Pricing This chart will be shown together with the chart in storyboard 3 to help the buyer make the best-informed decisions.
Dashboard 3	Description 1 Description 2 Description 3
Dashboard 4:	Description 1 Description 2 Description 3

COMMENTS

No.	Name	Date	Comments
1.	(Name)	(Date)	(Comment)
2.	(Name)	(Date)	(Comment)
3.	(Name)	(Date)	(Comment)