Difference between revisions of "Group05 Overview"

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<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue"> ABSTRACT </font></div>
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<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue"> INTRODUCTION </font></div>
 
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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 bonding and bridging communities, 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.  
 
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 bonding and bridging communities, 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, the dashboard serves to visualise whether geography is an important driver of inequality, by mapping the extent of social inequality and access to common spaces across neighbourhoods. This will be done via three approaches. First, 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.
+
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 access to common spaces across neighbourhoods. This will be done via three approaches. First, 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 choropleth mapping, spatial segregation index, and spatial points pattern analysis at both the subzone and dwelling unit level. 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, by identifying centroids, 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.
 
<br>
 
<br>
 
|-
 
|-
 
|}
 
|}
−
<!-- END OF ABSTRACT--->
+
<!-- END OF INTRODUCTION--->
  
 
<div style="padding: 17px">
 
<div style="padding: 17px">
 
</div>
 
</div>
−
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue"> MOTIVATION </font></div>
+
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue"> MOTIVATION & EXISTING GAPS</font></div>
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
|-
 
|-
 
|  
 
|  
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
−
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.
+
Although there has been some ethnographic research conducted on the state of social inequality, as well as the popularly known Gini Coefficient to measure inequality across the years, there seems to be little work done on the possibility of social clusters formed within and across neighbourhoods. Exploration into inequality's geographic dimension in our little red dot could potentially bring about interesting insights. In doing so, the hope is for more targeted solutioning efforts to promote social mixing. Using geospatial techniques in R, we thus seek to visualise whether geography is an important driver of inequality via a three-pronged approach.  
−
 
 
−
Although there has been some ethnographic research conducted on the state of social inequality, as well as the popularly known Gini Coefficient to measure Singapore's inequality across the years, there seems to be little work done on the possibility of social clusters formed within neighbourhoods. Exploration into inequality's geographic dimension in our little red dot could potentially bring about interesting insights. In doing so, the hope is for more targeted solutioning efforts to promote social mixing. Using geospatial techniques in R, we thus seek to visualise whether geography is an important driver of inequality via a three-pronged approach.  
 
 
|-
 
|-
 
|}
 
|}
−
<!-- END OF MOTIVATION--->
+
<!-- END OF MOTIVATION & EXISTING GAPS--->
  
 
<div style="padding: 17px">
 
<div style="padding: 17px">
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<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<b>1. Identifying Demographic Clusters via Spatial Points Pattern Analysis</b><br>  
 
<b>1. Identifying Demographic Clusters via Spatial Points Pattern Analysis</b><br>  
−
First, 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.
+
First, 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 first by choropleth mapping at the subzone level, then applying the spatial segregation index at the dwelling unit level.
  
−
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.  
+
Next, we will perform spatial points pattern analysis using kernel density estimation as a density-based point pattern measure for visualisation of our clusters, followed by the L Function as a distance-based measure. To test the significance of our clusters, we will then apply Complete Spatial Randomness Test using the Monti-Carlo Simulation.
  
 
|-
 
|-
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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.  
 
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.  
+
We will use the Hansen Accessibility Index's REAT measure to assess the accessibility of residents within each neighbourhood to these public spaces. We will then plot and visualise these indices across neighbourhoods for comparison.  
  
−
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, Subsidised vs Privatised Pre-Schools, as well as Subsidised vs Private Childcare Centres.
+
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 and Subsidised vs Privatised Pre-Schools/Childcare Centres.
 
|-
 
|-
 
|
 
|
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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.  
 
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.  
+
This will be done by first identifying “priority areas” with high density clusters and low Hansen Accessibility Index to common spaces, and then identifying centroids with reference to nearby vacant state land plots.  
 
|-
 
|-
 
|}
 
|}
 
<!-- END OF KEY OBJECTIVES--->
 
<!-- END OF KEY OBJECTIVES--->
 +
  
 
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">DATA SOURCES</font></div>
 
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">DATA SOURCES</font></div>
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<!-- END OF DATA SOURCES--->
  
−
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT TIMELINE AND MILESTONES</font></div>
 
−
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
 
−
The following lists the tentative timeline of our project<br>
 
−
<br>
 
−
</div>
 
  
−
<!-- END OF TIMELINE--->
+
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">TIMELINE & MILESTONES</font></div>
−
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT FUNCTION COMPLETION</font></div>
 
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 +
The following table shows the timeline and milestones of our project. Details will be updated progressively. <br>
 +
</div>
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 +
</div>
 
|-
 
|-
 
|  
 
|  
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<!-- END OF MILSTONES--->
 
<!-- END OF MILSTONES--->
  
−
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">TOOLS & TECHNOLOGIES</font></div>
+
 
 +
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">TOOLS & PACKAGES</font></div>
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
−
The following is a list of tentative tools to be adopted for the project’s scope.<br>
+
The following is a list of tools to be adopted for the project’s scope.
−
</div>
 
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
|-
 
|-
 
|  
 
|  
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 +
*QGIS
 
*Open Street Map
 
*Open Street Map
−
*One Map Geocoding API
+
*OneMap Geocode
 
*R Studio
 
*R Studio
−
*R Shiny
 
−
*Leaflet
 
 
*R libraries
 
*R libraries
 
**shiny
 
**shiny
 
**leaflet
 
**leaflet
 
**rgdal
 
**rgdal
 +
**sf
 +
**sp
 +
**spatstat
 
**dplyr
 
**dplyr
 
**plyr
 
**plyr
 
**maptools
 
**maptools
 
**shinydashboard
 
**shinydashboard
 +
**spatialsegregation
 
**REAT
 
**REAT
 
**SpatialAcc
 
**SpatialAcc
 
**ggmap
 
**ggmap
 
**SpatialPosition
 
**SpatialPosition
−
**sp
 
 
**maptools
 
**maptools
 
**shinyBS
 
**shinyBS
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<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">REFERENCES TO RELATED WORKS</font></div>
 
<div style="background: #56A5EC; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">REFERENCES TO RELATED WORKS</font></div>
−
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Open Sans, Arial, sans-serif; border-radius: 7px; text-align:left">
+
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 +
|-
 +
|
 +
 
 
1. We would like to credit the following referenced visualization works adopted in the design of our dashboard.<br>
 
1. We would like to credit the following referenced visualization works adopted in the design of our dashboard.<br>
  
 +
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
<b>Centroid-Amenities: An Interactive Visual Analytical Tool for Exploring and Analysing Amenities in Singapore</b><br>
 
<b>Centroid-Amenities: An Interactive Visual Analytical Tool for Exploring and Analysing Amenities in Singapore</b><br>
−
Authors: Siew Xue Qian Jazreel, Tay Wei Xuan, Sean Koh Jia Ming
+
Authors: Siew Xue Qian Jazreel, Tay Wei Xuan, Sean Koh Jia Ming <br>
−
https://jazreelsiew.shinyapps.io/AppV2/ <br>
+
https://jazreelsiew.shinyapps.io/AppV2/ <br>
 +
[[File:map3.png|center|1000px]] <br>
 +
|-
 +
|}
  
−
2. We would also like to credit the referenced literature considered in the design of our problem statement.
+
 
−
<br>
+
2. The following also lists the referenced literature considered in the design of our problem statement.  
−
</div>
+
<div style="border-left: #56A5EC solid 10px;font-family: Helvetica; padding: 0px 30px 0px 18px; ">
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
{| class="wikitable" style="background-color:#FFFFFF;" width="100%"
 
|-
 
|-
 
|  
 
|  
 +
 +
</div>
 +
 
<b>No</b>
 
<b>No</b>
 
||
 
||

Latest revision as of 14:24, 11 June 2018

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

OVERVIEW

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

BACK TO HOMEPAGE

INTRODUCTION

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 bonding and bridging communities, 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, the dashboard serves to visualise whether geography is an important driver of inequality, by mapping the extent of social inequality and access to common spaces across neighbourhoods. This will be done via three approaches. First, 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 choropleth mapping, spatial segregation index, and spatial points pattern analysis at both the subzone and dwelling unit level. 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, by identifying centroids, 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.

MOTIVATION & EXISTING GAPS

Although there has been some ethnographic research conducted on the state of social inequality, as well as the popularly known Gini Coefficient to measure inequality across the years, there seems to be little work done on the possibility of social clusters formed within and across neighbourhoods. Exploration into inequality's geographic dimension in our little red dot could potentially bring about interesting insights. In doing so, the hope is for more targeted solutioning efforts to promote social mixing. Using geospatial techniques in R, we thus seek to visualise whether geography is an important driver of inequality via a three-pronged approach.

KEY OBJECTIVES

1. Identifying Demographic Clusters via Spatial Points Pattern Analysis
First, 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 first by choropleth mapping at the subzone level, then applying the spatial segregation index at the dwelling unit level.

Next, we will perform spatial points pattern analysis using kernel density estimation as a density-based point pattern measure for visualisation of our clusters, followed by the L Function as a distance-based measure. To test the significance of our clusters, we will then apply Complete Spatial Randomness Test using the Monti-Carlo Simulation.

2. Modelling Geographical Accessibility to Amenities & Spaces
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's REAT measure to assess the accessibility of residents within each neighbourhood to these public spaces. We will then plot and visualise these indices across neighbourhoods for comparison.

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 and Subsidised vs Privatised Pre-Schools/Childcare Centres.

3. Solutioning for Common Spaces to Promote Mixing

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 identifying centroids with reference to nearby vacant state land plots.


DATA SOURCES

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, Annual

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

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

https://data.gov.sg/dataset/sla-vacant-state-land-and-properties


TIMELINE & MILESTONES
The following table shows the timeline and milestones of our project. Details will be updated progressively.

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. Geocoding and Categorization of Schools
2. Combine all Demographics datasets (Housing, Age and Ethnic) for analysis by Subzone
3. Convert all spatial files to WGS84 for OSM compatibility
4. Data Clean, Manipulate and Overlay the Amenities/Spaces with Demographics datasets

Grace
Yuing

6

Completed ✔

6

1st Wiki Content Update
(Proposal, Methodology & Storyboard)

Yuqing
Yuanjing

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 Poster

Yuqing

11

16

Uploading of App on Shinyapps.io (Artefact)

Yuanjing

12

17

Finalizing Wiki Page & Research Paper (Deliverables)

ALL

13

18

Poster Presentation / Conference

ALL

14


TOOLS & PACKAGES

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

  • QGIS
  • Open Street Map
  • OneMap Geocode
  • R Studio
  • R libraries
    • shiny
    • leaflet
    • rgdal
    • sf
    • sp
    • spatstat
    • dplyr
    • plyr
    • maptools
    • shinydashboard
    • spatialsegregation
    • REAT
    • SpatialAcc
    • ggmap
    • SpatialPosition
    • maptools
    • shinyBS
    • shinyJS


REFERENCES TO RELATED WORKS

1. 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/

Map3.png


2. The following also lists 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)

Teo You Yenn

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