Difference between revisions of "GeoEstate"
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− | <div align="center" style="font-size:75px">GeoEstate</div> | + | <div align="center" style="font-size:75px">[[File:GeoEstate_logo.png|center|link=WhereYouGeo|250px]]GeoEstate</div> |
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{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
− | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | | + | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | The Team |
|} | |} | ||
− | < | + | <br> |
− | + | [[File:GeoEstate_team.png|800px]] | |
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− | + | Landed Property has always been seen as something for the wealthy, with only the top 5% of Singaporean earners being able to afford it. For Singaporeans who aspire to own such Property, there are currently many available options – Terrace Houses, Semi-Detached Houses, Corner Terrace Houses, Detached Houses and many more. Due to fluctuating property prices, it may be difficult for an aspiring landed property owner to properly plan and budget to get their dream house. Furthermore, existing owners may be stressed about when the best time to sell is. | |
− | + | Our project aims to shine light on this by providing an accurate geographically weighted regression model using factors such as location, tenure and type of sale to predict future landed property prices. Now, aspiring owners can filter by location and type of house, and easily see what their dream house would be worth in several years. Alternatively, owners of landed property who plan to sell can see if they should do so sooner or later. | |
+ | |||
+ | Landed Property purchase and sale is a huge financial commitment and we at GeoEstate are committed to ensure that you make the best financial decision for you and your family. | ||
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
− | |||
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Motivation | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Motivation | ||
|} | |} | ||
<br/> | <br/> | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Data sources | ||
+ | |} | ||
+ | |||
+ | <table class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;"> | ||
+ | <tr> | ||
+ | <th> Data </th> | ||
+ | <th> Source </th> | ||
+ | <th> Data Type/Method | ||
+ | </th> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> 2014 Master Plan Planning Subzone (Web) </td> | ||
+ | <td> [https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-web Data.gov.sg] </td> | ||
+ | <td> SHP </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> URA Private Residential Property Transactions </td> | ||
+ | <td> [https://www.ura.gov.sg/realEstateIIWeb/transaction/search.action Ura.gov.sg] </td> | ||
+ | <td> | ||
+ | CSV <br/>Data was geocoded using [https://developers.google.com/maps/documentation/geocoding/start Google Geocoding API] | ||
+ | <br/>Postal code was geocoded using [https://docs.onemap.sg/#search OneMap API] | ||
+ | </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Pre-School Locations </td> | ||
+ | <td> [https://data.gov.sg/dataset/pre-schools-location Data.gov.sg] </td> | ||
+ | <td> KML <br/> Converted to Shapefile</td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Primary/Secondary School Locations </td> | ||
+ | <td> [https://data.gov.sg/dataset/school-directory-and-information Data.gov.sg] </td> | ||
+ | <td> CSV<br/>Data was geocoded using | ||
+ | [https://docs.onemap.sg/#search OneMap API] </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> MRT/LRT Station Locations </td> | ||
+ | <td> [https://www.mytransport.sg/content/dam/datamall/datasets/Geospatial/TrainStation.zip LTA Datamall] <br/>(Direct Download) </td> | ||
+ | <td> SHP </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Supermarket Locations </td> | ||
+ | <td> [https://data.gov.sg/dataset/supermarkets Data.gov.sg] </td> | ||
+ | <td> KML <br/> Converted to Shapefile </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Shopping Mall Locations </td> | ||
+ | <td> [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia] </td> | ||
+ | <td> Text <br/> Data was converted to Shapefile after geocoding using | ||
+ | [https://docs.onemap.sg/#search OneMap API] </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Park Locations </td> | ||
+ | <td> [https://data.gov.sg/dataset/nparks-parks Data.gov.sg] </td> | ||
+ | <td> KML <br/> Converted to Shapefile </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Sports Facilities Locations </td> | ||
+ | <td> [https://data.gov.sg/dataset/sportsg-sport-facilities Data.gov.sg] </td> | ||
+ | <td> KML <br/> Converted to Shapefile </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> Hawker Centre Locations </td> | ||
+ | <td> | ||
+ | Public Food Centres: <br> | ||
+ | 1. [https://data.gov.sg/dataset/hawker-centres Data.gov.sg]<br><br> | ||
+ | Private Food Centres: <br> | ||
+ | 2. [http://www.kopitiam.biz/search-results/?keywords&zone=allzone&FC=yes&search=Search Kopitam]<br> | ||
+ | 3. [https://www.koufu.com.sg/our-brands/food-halls/koufu/ Koufu]<br> | ||
+ | 4. [https://www.foodjunction.com/outlets/ Food Junction]<br> | ||
+ | 5. [https://foodrepublic.com.sg/food-republic-outlets/ Food Republic] | ||
+ | </td> | ||
+ | <td> | ||
+ | 1: KML - Converted to Shapefile <br> | ||
+ | 2 - 5: Text - Data scraped from sites and geocoded using [https://docs.onemap.sg/#search OneMap API] | ||
+ | </td> | ||
+ | </tr> | ||
+ | </table> | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Approach | ||
+ | |} | ||
+ | |||
+ | <br> | ||
+ | <br> | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Timeline | ||
+ | |} | ||
+ | |||
+ | <br> | ||
+ | <br> | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Prototype | ||
+ | |} | ||
+ | |||
+ | <br> | ||
+ | <br> | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Tools & Technology | ||
+ | |} | ||
+ | |||
+ | [[File:GeoEstate_tech_stack.png|1000px]] | ||
+ | |||
+ | {| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"| | ||
+ | | style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Challenges | ||
+ | |} | ||
+ | |||
+ | |||
+ | <table class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;"> | ||
+ | <tr> | ||
+ | <th> No. </th> | ||
+ | <th> Key Challenges </th> | ||
+ | <th> Mitigation </th> | ||
+ | </tr> | ||
+ | |||
+ | <tr> | ||
+ | <td> 1. </td> | ||
+ | <td> Unfamiliarity with R, its packages and R Shiny </td> | ||
+ | <td> | ||
+ | # Self-directed learning with online resources such as Datacamp, | ||
+ | # Browsing community forum (Stackoverflow / discuss.onemap) for help | ||
+ | # Looking at official documentation for various packages | ||
+ | </td> | ||
+ | </tr> | ||
+ | |||
+ | <tr> | ||
+ | <td> 2. </td> | ||
+ | <td> Limited oneMap API call for standard account </td> | ||
+ | <td> | ||
+ | # Creation of R script to catch timeout & wait | ||
+ | # Filtering out distinct records to query oneMap to reduce the quantity of duplicated request | ||
+ | </td> | ||
+ | </tr> | ||
+ | </table> |
Revision as of 17:52, 19 March 2019
The Team |
Project Description |
Landed Property has always been seen as something for the wealthy, with only the top 5% of Singaporean earners being able to afford it. For Singaporeans who aspire to own such Property, there are currently many available options – Terrace Houses, Semi-Detached Houses, Corner Terrace Houses, Detached Houses and many more. Due to fluctuating property prices, it may be difficult for an aspiring landed property owner to properly plan and budget to get their dream house. Furthermore, existing owners may be stressed about when the best time to sell is.
Our project aims to shine light on this by providing an accurate geographically weighted regression model using factors such as location, tenure and type of sale to predict future landed property prices. Now, aspiring owners can filter by location and type of house, and easily see what their dream house would be worth in several years. Alternatively, owners of landed property who plan to sell can see if they should do so sooner or later.
Landed Property purchase and sale is a huge financial commitment and we at GeoEstate are committed to ensure that you make the best financial decision for you and your family.
Project Motivation |
Data sources |
Data | Source | Data Type/Method |
---|---|---|
2014 Master Plan Planning Subzone (Web) | Data.gov.sg | SHP |
URA Private Residential Property Transactions | Ura.gov.sg |
CSV |
Pre-School Locations | Data.gov.sg | KML Converted to Shapefile |
Primary/Secondary School Locations | Data.gov.sg | CSV Data was geocoded using OneMap API |
MRT/LRT Station Locations | LTA Datamall (Direct Download) |
SHP |
Supermarket Locations | Data.gov.sg | KML Converted to Shapefile |
Shopping Mall Locations | Wikipedia | Text Data was converted to Shapefile after geocoding using OneMap API |
Park Locations | Data.gov.sg | KML Converted to Shapefile |
Sports Facilities Locations | Data.gov.sg | KML Converted to Shapefile |
Hawker Centre Locations |
Public Food Centres: |
1: KML - Converted to Shapefile |
Approach |
Project Timeline |
Project Prototype |
Tools & Technology |
Challenges |
No. | Key Challenges | Mitigation |
---|---|---|
1. | Unfamiliarity with R, its packages and R Shiny |
|
2. | Limited oneMap API call for standard account |
|