Centroid-Amenities Project Details

Home

Proposal

Poster

Application

Research Paper

Motivation and Problem Discovery

As the Singaporean Government looks toward more data-driven approaches to urban planning, it becomes imperative to the cause that we use intelligent ways to determine where we place our built amenities and services, we best serve our citizens' needs.

Currently, there are no ready tools available that could help planners optimise the placement of amenities and services, to make them as accessible as possible to the citizens they serve. Furthermore, we do not have ready analysis tools to help planners assess how accessible the existing amenities and services are.

Project Objectives

Our goals are :

• To build a GIS tool (an R Shiny app)
• Help administrators determine where best to put eldercare or any amenities
• Based on a geographical cluster analysis as determined through k-means clustering

Data Sources

Data	Source	Data Type/Method
OSM Layer (Singapore)	OpenStreet Map	SHP
Master Plan 2014 Subzone Boundary (No Sea)	https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea	SHP
Estimated Singapore Resident Population in HDB Flats	https://data.gov.sg/dataset/estimated-resident-population-living-in-hdb-flats	.csv
Dwelling Units under HDB's Management, by Town and Flat Type	https://data.gov.sg/dataset/number-of-residential-units-under-hdb-s-management	.csv
Residents by Age Group & Type of Dwelling, Annual	https://data.gov.sg/dataset/residents-by-age-group-type-of-dwelling-annual	.csv
Land Area and Dwelling Units by Town	https://data.gov.sg/dataset/land-area-and-dwelling-units-by-town	.csv
Singapore Residents by Subzone and Type of Dwelling, June 2016	https://data.gov.sg/dataset/singapore-residents-by-subzone-and-type-of-dwelling-june-2016	SHP
Singapore Residents by Subzone, Age Group and Sex, June 2016 (Gender)	https://data.gov.sg/dataset/singapore-residents-by-subzone-age-group-and-sex-june-2016-gender	SHP
Pre-Schools Location	https://data.gov.sg/dataset/pre-schools-location	KML
Child Care Services	https://data.gov.sg/dataset/child-care-services?resource_id=195b6c5f-c277-4ba9-bcdc-25c264e3ee5c	SHP

Project Timeline /Milestones

Related Work

OneMap SchoolQuery (https://www.onemap.sg/main/v2/schoolquery)

Using this as a reference for our user interface and functionalities of our application.

Clustering With GIS: An Attempt to Classify Turkish District Data

Using this as a reference for the algorithm and analyze.

Buffer Analysis and Modified K-Means Clustering for Geo-Spatial Amenities on Gujarat state

Using this as a reference for our k-means clustering.

Challenges

Lack of Data

• Diffcult to find insightful data needed for the project
• Data found are not complete

Data cleaning and transformation

• Data found are not in the format that could be analysed, thus data cleaning is tough
• Documentation of cleaning process

Inexperience with QGIS

• Self-learning is required
• Listen to Prof Kam in class
• Peer-to-peer learning

Tools and Techologies

• QGIS
• Shiny
• R Language
• Open Street Map
• Mircosoft Excel

Meet The Team

References

Aksoy, Ece. (2006). Clustering With GIS: An Attempt to Classify Turkish District Data. Retrieved March 3, 2018, from https://www.researchgate.net/publication/255603804_Clustering_With_GIS_An_Attempt_to_Classify_Turkish_District_Data

Brajesh, S., Katyal, K., Pandya, M., Chaudhary, B., Bodar, H., Thakkar, P., Patel, L., Patel, N., Patel, K. (2014). Buffer Analysis and Modified K-Means Clustering for Geo-Spatial Amenities on Gujarat state. International Journal of Scientific & Engineering Research,5(8), 838-841. Retrieved March 3, 2018, from https://www.ijser.org/researchpaper/Buffer-Analysis-and-Modified-K-Means-Clustering-for-Geo-Spatial-Amenities-on-Gujarat-state.pdf

Tai, J., & Toh, Y. (2016, November 10). Growing old: Should you be worried? Retrieved March 04, 2018, from http://www.straitstimes.com/singapore/growing-old-should-you-be-worried

Comments

Hello fellow mates! Feel free to add your comments under this section. :)