Difference between revisions of "Group02 proposal"

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The Data Sets we will be using for our analysis and for our application is listed below:
 
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<b>Commercial Data</b>
 
<b>Commercial Data</b>
(Jan 2012 - Dec 2019)<br/><br/>
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(Jan 2012 - Dec 2019)<br/>
(https://spring-ura-gov-sg.libproxy.smu.edu.sg/lad/ore/property_market/index.cfm)<br>
 
  
 
<br>Residential Data</b>
 
<br>Residential Data</b>
 
(Jan 2012 - Dec 2019)<br/><br/>
 
(Jan 2012 - Dec 2019)<br/><br/>
  
 +
(https://spring-ura-gov-sg.libproxy.smu.edu.sg/lad/ore/property_market/index.cfm)<br>
 
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Revision as of 17:16, 29 February 2020

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 the visualization, our group hopes to be able to better allow for users to be able to visualize a home of his dreams beyond mere price.

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)

The dataset will be used to understand the Indonesian Export and import figure from 1996 - 2019, July across multiple categories. At the end, we wish to spot the trend or pattern of Indonesia trading and economy situation.

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 dataset will be used to understand the Indonesian Export and import figure from 1996 - 2019, July across multiple categories. At the end, we wish to spot the trend or pattern of Indonesia trading and economy situation.

Commercial Data (Jan 2012 - Dec 2019)


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

go change

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
ThematicMap.png

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:
  • Description 1
  • Description 2
  • Description 3

Dashboard 2
  • Description 1
  • Description 2
  • Description 3

Dashboard 3:
  • Description 1
  • Description 2
  • Description 3

MILESTONES

COMMENTS

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