Group05 Overview

Exploring Inequality’s Geographic Dimension Across Neighbourhoods in Singapore: Its Driving Forces & Touch Points

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ABSTRACT

Amidst the recent debate over growing social inequality in Singapore such as the distinct clustering of elite schools and varying access to resources, the dangers of hardening social mobility can pose as a threat for a culturally diverse nation that has upheld its values of social cohesion and racial harmony. In bridging social divide, Singapore has put in place various programmes in the community and schools to nurture shared experiences and promote inter-communities mixing. More recently however, inequality was highlighted to have a geographic dimension even for a densely populated city like Singapore. Using geospatial techniques in R, the dashboard serves to visualise whether geography is an important driver of inequality, by mapping the extent of social inequality and availability of common spaces across neighbourhoods. This will be done in three approaches. Firstly, we will analyse whether there exist clusters that could point towards social inequality and whether this is more pronounced in certain neighbourhoods, based on ethnic mix, age composition, and housing type. This will be done using spatial points pattern analysis including distance-based and density-based measures. Next, using the Hansen Accessibility Model, we will map out the available touch points within neighbourhoods that could facilitate social mixing, such as the ease of access to common spaces, amenities and opportunities for choice of education. This is because an important aspect of social inequality is having reasonably fair access to different resources. Lastly, we will move into solutioning and explore possible spaces such as vacant state land where upcoming public amenities can be best placed to optimise social class mixing and enhance the Hansen Accessibility Index.

MOVITATION

Amidst the recent debate over growing social inequality in Singapore such as the distinct clustering of elite schools and varying access to resources, the dangers of hardening social mobility can pose as a threat for a culturally diverse nation that has upheld its values of social cohesion and racial harmony. In bridging social divide, Singapore has put in place various programmes in the community and schools to nurture shared experiences and promote inter-communities mixing. More recently, inequality was highlighted to have a geographic dimension even for a densely populated city like Singapore. Using geospatial techniques in R, we hope to visualise whether geography is an important driver of inequality via a three-pronged approach.

PROJECCT OBJECTIVES

1. Identifying Demographic Clusters (Spatial Points Pattern Analysis) Firstly, we will analyse whether there exist clusters that could point towards social inequality and whether this is more pronounced in certain neighbourhoods, based on ethnic mix, age composition, and housing type. This will be done using spatial points pattern analysis including distance-based and density-based measures. We will apply kernel density estimation as a density-based point pattern measure for visualisation of our clusters, followed by distance-based measures including the L Function, Quadrat Analysis and K-Nearest Neighbour. We will then apply Complete Spatial Randomness Test using the Monti-Carlo Simulation, Quadrat Test and the Clark Evans Test to test the significance of the clusters, respectively.

2. Modelling Geographical Accessibility to Amenities & Spaces (Hansen Accessibility Model) Next, we will map out the available touch points within neighbourhoods that could facilitate social mixing. This would include the ease of access to common spaces, amenities and opportunities for choice of education. This is because an important aspect of social inequality is having reasonably fair access to different resources. We will use the Hansen Accessibility Index – REAT and SpatialAcc measures – to assess the accessibility of residents within each neighbourhood to these public spaces. For amenities that exist segregation of access, such as schools, pre-schools and childcare centres, geographical accessibility will be assessed separately based on its class, that is GEP/SAP vs Mainstream Schools, Government-Subsidised vs Privatised Pre-Schools, as well as Government-Subsidised vs Private Childcare Centres.

3. Solutioning for Common Spaces to Promote Mixing (K-Nearest Neighbour) Lastly, we will move into solutioning and explore possible spaces, using vacant state land data, where upcoming public amenities can be best placed to optimise social class mixing and enhance the Hansen Accessibility Index. This will be done by first identifying “priority areas” with high density clusters and low Hansen Accessibility Index to common spaces, and then performing K-Nearest Neighbour Analysis based on SLA’s vacant state land plots.

PROJECT DATA SOURCE

Our team's datasets are retrieved from https://data.gov.sg

Type	Format	Data	Source URL
Boundary (Polygon)	SHP	OSM Layer (Singapore)	OpenStreet Map
Boundary (Polygon)	SHP	Master Plan 2014 Subzone Boundary (No Sea)	https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea
Demographics	CSV	Estimated Singapore Resident Population in HDB Flats	https://data.gov.sg/dataset/estimated-resident-population-living-in-hdb-flats
Demographics	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
Demographics	CSV	Residents by Age Group & Type of Dwelling, Annua	https://data.gov.sg/dataset/residents-by-age-group-type-of-dwelling-annual
Demographics	CSV	Land Area and Dwelling Units by Town	https://data.gov.sg/dataset/land-area-and-dwelling-units-by-town
Demographics	SHP	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
Demographics	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
Demographics	SHP	Resident Population of Other Ethnic Groups by Age Group, Ethnic Group and Sex, 2015	https://data.gov.sg/dataset/resident-population-of-other-ethnic-groups-by-age-group-ethnic-group-and-sex-2015
Amenities	SHP	Child Care Centres	https://data.gov.sg/dataset/child-care-services?resource_id=195b6c5f-c277-4ba9-bcdc-25c264e3ee5c
Amenities	SHP	Pre-Schools	https://data.gov.sg/dataset/pre-schools-location
Amenities	CSV	Primary Schools	https://data.gov.sg/dataset/school-directory-and-information
Amenities	KML	CHAS Clinics	https://data.gov.sg/dataset/chas-clinics
Spaces	KML	Sports Facilities (SportsSG)	https://data.gov.sg/dataset/school-directory-and-information
Spaces	KML	Community Clubs (PA)	https://data.gov.sg/dataset/community-clubs
Spaces	SHP	Parks (including playgrounds)	https://data.gov.sg/dataset/parks
Spaces	KML	Community Use Sites (SLA)	https://data.gov.sg/dataset/community-use-sites
Spaces	KML	Vacant State Land (SLA)	https://data.gov.sg/dataset/sla-vacant-state-land-and-properties

PROJECT TIMELINE AND MILESTONES

This a ROUGH timeline of our entire project. Milestones indicated are according to IS415 AY1718 Project Wiki Page (Detailed timeline will be updated)

PROJECT FUNCTION COMPLETION

S/N	Task	Done by	Week	Status
1	Topic Brainstorming	ALL	2 & 3	Completed ✔
2	Drafting and refinement of Project Proposal	ALL	2 & 3	Completed ✔
3	Consultation with Prof Kam for Feedback on Proposal	ALL	4	Completed ✔
4	Finalization of Project Topic and Focus	ALL	5	Completed ✔
5	Compilation and Cleaning of Datasets 1. Categorization of Schools 2. Read in Ethnic Mix (CSV) and combine with the Age & Housing Type Data (by SZ) 3. Geocode Schools Dataset 4. Read in & St_intersect the 3 Amenities, 4 Spaces (SHP/KML files) with the Subzones & Planning Areas Convert all into SHP file, using WGS84	Yuqing Grace	6	Completed ✔
6	1st Wiki Content Update (Proposal, Methodology & Storyboard)	Yuqing	6	Completed ✔
7	Independent learning of R and R Shiny on DataCamp	ALL	6 & 7
8	Consultation with Prof Kam for Feedback on tools for Geospatial Analysis	ALL	7
9	2nd Wiki Content Update	Yuqing	7
10	Map Development PART 1: Kernel Density Estimate: Grace PART 2: Hansen Potential Analysis: Grace PART 3: Common Spaces for Solutioning: Yuanjing	Grace Yuanjing	7 & 8
11	Interface Development User Interface (Web Layout): Yuqing User Interface (Description) : Yuanjing	Yuqing Yuanjing	7 & 8
12	Consultation with Prof Kam for Feedback on progress/techniques	ALL	9
13	Debugging and Analysis of Results	ALL	9
14	Consultation with Prof Kam for Feedback on final product	ALL	10
15	Creating and Submission of Townhall Poster	Yuqing	11
16	Uploading of App on Shinyapps.io (Artefact)	Yuanjing	12
17	Finalizing Wiki Page & Research Paper (Deliverables)	ALL	13
18	Townhall Poster Presentation / Conference	ALL	14

TOOLS & TECHNOLOGIES

The following is a list of tentative tools to be adopted for the project’s scope

• Open Street Map • One Map Geocoding API • R Studio • R Shiny • Leaflet • R libraries: • shiny • leaflet • rgdal • dplyr • plyr • maptools • shinydashboard • REAT • SpatialAcc • ggmap • ggplot2 • SpatialPosition • sp • maptools • shinyBS • shinyJS

REFERENCES to RELATED WORKS

We would like to credit the following referenced visualization works adopted in the design of our dashboard.
Centroid-Amenities: An Interactive Visual Analytical Tool for Exploring and Analysing Amenities in Singapore
Authors: Siew Xue Qian Jazreel, Tay Wei Xuan, Sean Koh Jia Ming

https://jazreelsiew.shinyapps.io/AppV2/ 

We would also like to credit the referenced literature considered in the design of our problem statement.

No	Title	Link	Author
1	Commentary: Inequality has a geographic dimension - between and within neighbourhoods in Singapore	https://www.channelnewsasia.com/news/commentary/inequality-in-singapore-exists-across-within-neighbourhoods-10276898	Leong Chan-Hoong
2	Lack of social mixing is a symptom of inequality, not a cause	https://www.straitstimes.com/opinion/lack-of-social-mixing-is-a-symptom-of-inequality-not-a-cause#main-content	Teo You Yenn
3	Class divide: Singapore in danger of becoming academic aristocracy	https://www.straitstimes.com/opinion/class-divide-singapore-in-danger-of-becoming-academic-aristocracy	Chua Mui Hoong
4	The Big Read: Social stratification — a poison seeping into S’pore’s housing estates and schools	https://www.todayonline.com/singapore/big-read-social-stratification-poison-seeping-spores-housing-estates-and-schools	Kelly Ng and Toh Ee Ming
5	COMMENT: Can Singapore's elite circle turn around growing social divide?	https://sg.news.yahoo.com/comment-can-singapores-elite-circle-turn-around-growing-divide-124724650.html	Nicholas Yong
6	This Is What Inequality Looks Like	Ethos Books (Publisher)	Nicholas Yong