Policy And Planning Proposal 2

Precision Policy And Planning

Team

Proposal (Iter 1)

Proposal (Iter 2)

Poster

Application

Research Paper

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PROBLEM & MOTIVATION

Problem Background:

With Singapore’s growing population and limited resources, she faces many pressing challenges for progressive development and economic growth. These challenges span across housing affordability, rising healthcare, aging population, education/income inequality, and low birth rates. For Singapore to continue progressing, it is imperative that the government continues to take proactive measures to plan and utilise its resources effectively. In this fashion, we strive to use visual analytics to help uncover some of the cracks in and opportunities in Singapore’s social demographic to assist the government in sharpening its current policies and to look into future plans. This is well in line with the government’s effort of making socially relevant data public to encourage innovation and discovery.


Motivation:

With the government's strong support and push for open source innovation, we felt that this is a key area that we could utilise our skills in bringing value to society through informing the public and assisting decision makers with planning. Furthermore, with the government's push towards a smart nation, there are increasing data sets available with reasonably high dimention that can allow us to get insights if visualised properly.

PROJECT OBJECTIVES

The key objectives we strive to achieve in this project cover two key processes of planning and policy making - Problem identification and Root cause analysis.
For each of these areas we have targeted to achieve the following:

Problem Identification:

• Provide a high level view of social demographics to allow broad understanding and awareness of data.
• Provide complimentary high level charts that show corresponding demographics of interest to allow identification of desirable/undesirable trends.
• Provide high level filters and interactive view modes to allow users to gather insights from a time based visualisation.

Root Cause Analysis (Identify Probable Correlations, not necessariyly causation):

• Provide precise interactive charts to allow further exploration of identified problems.
• Provide relevant filters and customizable views to allow users to identify key factors that might be contributing to the problem.

SELECTED DATABASE

The Data Sets we will be using for our analysis and for our application is listed below:

Dataset/Source	Data Attributes	Rationale Of Usage
Resident by planning Area, subgroup, age Group, sex and dwelling (2000 - 2019, June) https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data	Planning area SubZone Age group Type of dwelling Gender	This dataset covers a good time series from 2000-2019 and the breakdown by subzone/planning area allows it to serve as the base platform to integrating with other population data sets that are grouped by subzone/planning area as well. From here, we can also get a good view of Singapore’s residential distribution by gender and age group that might give us a few initial findings that help for further investigation with the help of complimentary data sets.
Singapore General Household Survey & Census of population (2000, 2010, 2015) https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data	Contains the following attributes by Planning Area: Gender Occupation Income Levels Travel Time to work/School Educational Qualification Language Preference Religion	This data adds a very rich level of dimensionality on top of the residential data as mentioned above. However, it only covers limited points in time and so we intend to use this data separately for more deep time static analysis. Also, as this data is very rich, we hope that it can further serve as a bridge to more abstract but complementary data.
Birth & Fertility Rate (1960 - 2018, annual) https://www.singstat.gov.sg/find-data/search-by-theme/population/births-and-fertility/latest-data	Total Fertility rate Fertility Rate by age group Fertility rate by ethinic group Gross and net reproduction rate Crude birth rate Total live-births Resident live-births Total live - birth by ethnic group and gender	This data set gives quite good coverage on the number of births by different dimentions i.e. ethnicity, age group, educational background of parents. This will be helpful in helping us bridge this data set with the other population data sets to explore Singapore’s Birthrate problems in greater detail.
Hospital Admission Rate By Sex and Age (1984 - 2018) https://www.singstat.gov.sg/find-data/search-by-theme/society/health/latest-data	Acute / Community / Psychiatric hospital By age group (0-14, 15-64, >65 ) Total Number of admissions by hospital	This data helps to give a high level view on the hospitals of singapore and how many admissions they had each year. It will be useful to integrate this with the population data set to see how taxed each hospital is in caring for different age groups through the years.

We will be using these data sets as our main inputs. However, we are still open to add complimentary data sets to further enhance the quality insights attained from our visualisation.

BACKGROUND SURVEY

To begin, we explored current charts that were used to visualise the key areas that we defined to explore (e.g. inequality, urban planning, geo-plots). This is a summary of the more interesting visualisations we found:

Reference of Other Interactive Visualization	Learning Point
Title: Income distribution by country over the years Source: https://www.gapminder.org/tools/#$chart-type=mountain	Learning Points: Highly interactive; The detailed data of the region can be displayed at corner upon selecting An overview of all the countries, income population, distribution in the same chart. Converted the Continent information into different colours. Possible Usage: It allowed the team to discover all the subzone/ planning area income distribution in the same chart with a generic reference line of average income of the Singapore or world poverty line. It would be helpful for the decision maker to redistribute the resource for the needy residents Area for Improvement: The selection has the animation of highlighted areas instead of lines. The user could get wrong information as they might be misled by the proportion of the offset area.
Title: Changing Ranks of States by Congressional Representation Source: https://www.census.gov/dataviz/visualizations/023/	Learning Points: Provide the consistency of changing rank over the sampling period. Possible Usage: Helpful for indicating the rank of the population density rank or other rank to find the changing of the hotspots between 2 or more periods. Area for Improvement: No actual data has been displayed. Only limited insights could be found from the chart The colour and intercepted points among the used could cause misunderstanding
Title: Interactive map visualization Source: https://morphocode.com/the-5-minute-walk/	Learning Points: Chart A enables users to zoom into different planning areas in the geographic distribution. Then, information of the resident population by subgroup, age group, type of dwelling will be presented by the bar chart. It allows immediate comparison of the resident population between the planning areas and draws trends among other observations. Moreover, with possible available datasets of schools, hospitals, resident household income, etc by location point, interactivity can be applied as shown in chart B. By clicking into a specific subzone, it allows users to adjust a range of radius. As the radius expands, a pie chart will show the increase in number of schools, hospitals, resident household, income, etc. Possible Usage: This can be used in our setting to do sharp analysis of specific amenities and how much of the population they cater to. E.g the user could perhaps look at common places of leisure (Parks), or Hospitals and using a radius of Planning areas, calculate the proportion of age group that the facility caters to. Area for Improvement: This chart needs very specific location data which is not available for us. Thus we will need to re-think how to use the "radius" filter function accordingly.
Title: Bricks Map Source: https://www.perceptualedge.com/blog/?p=1627	Learning Points: The bricks map provides a quick overview of an indication in a geographical chart. Furthermore, it allows immediate distribution comparison in units measurement of a variable in the planning areas. Possible Usage: Variables such as population in different age groups, qualification levels, etc can be shown in the bricks units with enhance interactivity features for users to adjust. Area for Improvement: Difficult to compare between bricks that are of the same size. As users would need to specifically count each of the units to identify the differences.
Title: Circular Network Diagram Source: https://www.census.gov/dataviz/visualizations/stem/stem-html/	Learning Points: Help to find the relationship of redistribution between 2 and more variables. Possible Usage: Display the same subzone and planning relationship and distribution between the education level and income level. Area for Improvement: No actual data has been displayed. The circle design and proportion used may not reflect the distribution accurately due to the transformation to the round shape.

BRAINSTORMING SESSIONS

From our initial survey above, we have internalised some of the models and these are some of the ideas we came up with in our Brainstorming sessions:

1. Income Distribution By Subzone Planning Areas

The above listed chart or its modified variation will be used to describe the income distribution by the planning area or subzone.
These are the respective features of the chart:

• X-Axis: % of the population group
• Y-Axis income group
• User can use the a drop down list to check the different years’ income distribution
• The data will be formed into line chart histogram
• A Singapore average income and world poverty / average lines as the reference to help the user to understand the corresponding planning zone income position among world or entire Singapore
• The small map will show the the related region of the selected planning area.

2. Horizontal Network Diagram to view relationships between different dimentions

The above listed chart or its modified variation will be used to describe the redistribution for 2 or more metrics (E.g. Income/housing type).
The User can use drop down selector to change the parameters for the chart. This would help him to find the pattern of the different parameters.
Eg Did the enough HDB flat have been prepared for low income people?

3. Rank Change Chart

This chart helps to show rank changes and progression especially within a smaller set of data, i.e. census data 2000,2010,2015.
It would help users get a quick idea of how different planning areas or subzones fared in different social demographic categories.
This would then allow users to pinpoint specific cases that are surprising/desirable/undesirable and then proceed to do the necessary precise investigation from there.

4. Interactive Radius Summary Chart

This chart follows the same concept of the "5 minute distance" radius to dynamically calculate the corresponding demographics of the population within the radius.
Using this idea, planners will be able to dynamically set a radius to check the demographics of the nearby populace with greater detail using filters and categorical charts.
This graph brings in a new dynamic way of precise exploration to see the potential usage of facilities like common spaces, hospitals, and even parks.

5. Bricks Map With Simplified Social Demographics

This will serve as the core geo-plot for our users to keep in touch with the geographic nature of analysing Singapore.
With the help of different legends and simplified demographic classes, we will be able to give the user the relevant
time series data for them to better appreciate the context and usefulness of the other charts.

PROPOSED STORYBOARD (PAPER PROTOTYPE)

Below is the proposed story board for our project:

Storyboard	Insights / Comments
Title: DASHBOARD 1 - Home View (Helicopter View Exploration)	This chart serves as the landing page where users will explore the data set at a high level. It will offer a time based playable view over key social demographics at national level on things like Age-sex pyramid, Fertility rate (with trend), Household income, Education levels. Using this page, the user will be able to identify trends that they have interest in and pick out high level insights to be explored further using the other dashboards. The time playable view will allow the user to gain deeper insights as the data can be expressed in higher dimentionality rather than condensing it to fit in a trend based line chart. We will also consider adding it different high level view options that the map can play out. (i.e. it is currently population density by planning areas, but it could also be selectively changed to income distribution/educational level...etc.)
Title: DASHBOARD 2 - Social Demographic Investigator	This view is a specific analytic view that has more specific charts and filter options to allow the user to dive in to explore social demographic issues like inequality with greater precision. We intend for this view to be able to show down to the sub-zone but understand that data is limited if we use that level of detail. As such, the view might only be able to show 2000, 2010, 2015 in greater detail as supported by the population census data. We hope to explore more potential views that help display population demographic data like education and income levels, to really help policy makers and planners get down to building more creative and targeted solutions based on sharper insights.
Title: DASHBOARD 2 - Family and Birth Rate Investigator	One of the things that made us excited about this project at first was being able to explore what are the key factors that lead to low birth rate. As such, we hope to use this visualisation to help identify areas with high/low birth rate and fit complimentary data to get down to the factors behind these low/high birth rates. Some of the factors we can imagine that are of interest are: education levels, age of marriage, religion, type of House ... etc. As such, we hope to implement charts that help to visualise some of these relationships and make the exploration more fruitful in giving insights for targeted planning and policy making. We also would like to build in charts (Interactive map visualisation) to analyse public ammenities and how they provide for different population groups for better planning.
Title: DASHBOARD 3 - Healthcare Investigator	This storyboard is heavily focused on identifying the healthcare facilities and how much strain they are getting from the population. We hope to use interactive map visualisations to allow users to selectively explore healthcare facilities and their usage and stress according to planning areas and subzones. As preventive healthcare is also the general direction of the government, we hope to build in features to help them understand where to locate certain public amenities like fitness zones or eldercare centres to optimise the land planning.

TECHNOLOGY USED

These are the current technologies we have shortlisted that might be useful for the respective steps of the project,
we will be using the most feasible of these options or adding on others if necessary:

CHALLENGES, RISK ASSESMENT AND MITIGATION

Challenges	Mitigation Plan
Inexperienced in using R and R shiny	Complete R and R shiny courses in DataCamp Refer to relevant documentation Watch tutorial videos
Difficulty to consolidate and clean the data as we are using multiple data sets with different data attributes	Utilise data cleaning tool such as tableau prep builder The team should spend extra time and effort to consolidate the data
Limited knowledge about government planning and policy making. Moreover, factors identified and insights from visualisation charts may not directly shows indication of planning and policy making findings	Research about Singapore planning and policy making Conduct extensive EDA to discover interesting insights and correlation in the visual analysis process

PROPOSED TIMELINE

This timeline shows the breakdown of tasks leading up to the project milestones. This timeline shows the progress as of 1 Mar 2020.

COMMENTS AND FEEDBACK

Feel free to leave us some comments so that we can improve!

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