Microheart

Proposal

Poster

Application

Research Paper

PROBLEM & MOTIVATION

Problem Description: By 2030, the elderly and retired population in Singapore is expected to be approximately 90,000 -- almost twice the current figure. Despite the government’s efforts to continuously beef up the availability and accessibility of healthcare for the elderly and retired, there needs to be a means for the government to predict the optimal number of healthcare facilities that best meets the need of our target audience.

Motivation: This project is motivated by the varying ratios of (number of elderly and retired) to number of healthcare facilities in each planning area. This brings a need to detect which planning areas require better access to healthcare facilities.

OBJECTIVES

In this project, we aim to create visualisations that helps users perform and analyse the following:

Descriptive

We would like to provide users with up-to-date demographic information, specifically pertaining to the aged population and the distribution across the different areas of Singapore (as per URA 2014). In addition, we would like to include other information, such as the presence of medical facilities and its distribution mapped out on the same planning area chart.

Prescriptive

Based on the suggested design above, we aim to be able to provide users with insights on which certain planning zones or subzones require development and emphasis on medical facilities based on the volume of ageing population staying there.

Predictive

We would like to provide users with a visibility on the trend of ageing population. Based on the dependency ratio formulated from the proportion of ageing population out of the working and economically active population, we aim to be able to predict the timeline on which the ageing situation for the country reaches a critical threshold (where ageing population forms a significant proportion out of the population).

SELECTED DATASET

The list of selected dataset that we have sourced for to input into our visualizations can be found below. Necessary data pre-processing steps will be taken to clean and transform the data into a format that is suitable to upload into a visualization.

Dataset/Source	Data Attributes	Rationale Of Usage
SingStat Population by Geographic Distribution Data The dataset "Singapore Residents by Planning Area/Subzone, Age Group and Sex, June 2000 - 2018" under population trends was selected.	Planning Area: Character Text, planning areas segmented as per URA Master Plan 2014 Subzones: Character Text, sub-zones of the respective planning areas to show detail area information Age Group (bins): Numerical Integer, shows the total count of individuals under the respective age groups in the respective subzones	Required dataset to show the volume of population according to the different age groups. Segmented by planning areas, sub-zones, sex and years (across the different sheets)
The dataset "Distribution of Working Population by Planning Area" was selected.	Planning Area: Character Text, planning areas segmented as per URA Master Plan 2014 Total: Numerical integer, displays the count of individuals living in that particular planning area Industry: Character Text, displays the category of industry these working individuals fall under	This dataset helps ascertain the age-dependency ratio in every planning area.
Data for the locations of medical centres and hospitals, based on the planning areas, was selected. Locations of Clinics and Polyclinics Locations of Hospitals	Name of Clinic/Hospital: Character Text, displays the title of the facility Address: Character Text and Numerical, provides the location address of the facility Postal Code: Numerical Integer, provides the 6-digit postal code of the facility in Singapore. Provides information for the Planning Area that the facility is located in One challenge for processing this dataset is the lack of a csv/excel file for the dataset. Records have to be manually copied out from the website into a data table. However, due to the small amount of clinics and hospitals, it is a manageable task.	This dataset provides a distribution of medical facilities based on the Master Plan 2014 by URA. By mapping these locations out against the distribution of ageing population across the country, we will be able to determine the planning areas which have a greater need for medical facilities based on the volume of ageing population in that area.

BACKGROUND SURVEY OF RELATED WORKS

Our project draws inspiration from the following past works and studies:

Related Works	What We Can Learn
Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data source: World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects.	This visualisation is supposed to depict the trend of the age dependency ratio in Singapore from 1960 to 2017. The axes are not labelled, making it hard for observers to understand the data that is being captured, at a glance.
Resident population aged 65 and above by planning area Data source	This visualisation helps users quickly identify which areas in Singapore are relatively more densely populated with the elder generation and thus are more exposed to the effects of an increased ageing population.
Reference Dataset Title 3 Source: Source URL 3	WIP

PROPOSED STORYBOARD

WIP

Proposed Layout and Methodology	How Analyst Can Conduct Analysis (Rationale)
Main population distribution chart based on Master Plan 2014: Proposed Approach Map out the distribution of elderly and retirees by planning area Map out the distribution of hospitals and clinics by planning area Map out the trend of growth in elderly/retired population based on planning areas and the change in ratio of elderly to number of healthcare facilities Draft Designs:	To identify how the elderly and retired population are spread across the island This steps serves to identify planning areas in Singapore that house a relatively higher number of elderly/retired This information is to be layered on the distribution of elderly and retirees Allows analysts to view: for a certain planning area with X number of elderly/retirees, how well equipped it is in terms of fulfilling their immediate healthcare needs Allows analysts to predict which planning areas are likely to face a strain in terms of supporting the elderly/retirees Enables analysts to forward plan the setting up of new healthcare facilities
WIP	WIP
WIP	WIP

ADDRESSING KEY TECHNICAL CHALLENGES

The following are some of the key technical challenges that we may face throughout the course of the project:

Key Technical Challenges	How We Propose To Resolve
Using D3.js to create interactive visualizations	Explore more on D3.js and understand how it works through available resources such as GitHub etc
Data preprocessing	Research on the data required, understand which data set is most important and clean up the data
Designing insightful Dashboard	Explore the possible ways of visualizing data by researching on the existing charts, graphs etc

PROJECT TIMELINE

The following shows our project timeline for the completion of this project:

TOOLS/TECHNOLOGIES

The following are some of the tools/technologies that we will be utilizing during the project:

• Excel
• Tableau
• D3.js
• JMP Pro

REFERENCES

WIP

COMMENTS

Feel free to provide us with comments, suggestions and feedback to help us improve our project! (: