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APPLICATION

RESEARCH PAPER

GROUP MEMBERS

Debbie Lee Shan Ying | Goh Chun Ming | Tan Guan Ze

PROJECT DESCRIPTION

Our project aims to make use of geospatial intelligence to explore the potential of allocating nearby doctors within estates to the residents, in particular the elderly to combat the issues of an ageing population.

PROJECT MOTIVATION

Between 1965 and this year, Singapore’s population grew from 1.9 million to 5.71 million. However, the number of citizens aged 65 and above is increasing rapidly, as population growth slows. The size of this group of citizens doubled from 220,000 in year 2000 to 886,000 today, and is expected to increase even more by 2030. As the government look towards new ways in providing better social welfare, we aim to explore the possibility of allocating neighbourhood clinic doctors to nearby HDB blocks to ensure that those with mobility issues or disabilities receives adequate healthcare.

DATA COLLECTION

To achieve our project objectives, it is necessary for us to obtain the datasets that is available online for use. The following table depicts the list of datasets we require and how we can obtain them:

No.	Dataset	Description	Source(s)
1.	Singapore Planning Subzone (MP14_SUBZONE_WEB_PL)	This dataset is necessary for us to be able to plot the Singapore map out at a planning subzone level.	Included in our In-Class and Take-Home exercises
2.	Singapore Population by Planning Area/Subzone	This dataset is necessary for us to filter out the elderly population and subsequently join it with the Subzone attributes of other datasets.	Included in our In-Class and Take-Home exercises
3.	Singapore Population by Type of Dwelling	This dataset is necessary for us to find out the total number of elderly population in a specific Subzone, so that we are able to locate the mature estates in Singapore.	Included in our In-Class and Take-Home exercises
4.	Total Number of Neighborhood Clinics in Singapore	This dataset is necessary for us to locate all the neighborhood clinics in Singapore and find out which Subzone they belong to, so as to aid in our proximity analysis with the dwellings. As this data is not readily available to us, we will need to perform web scraping to "scrape" the data off the Singapore YellowPages website. A Python script can help us achieve the following and more details can be found below.	https://www.yellowpages.com.sg/

Code Snippet 1	Code Snippet 2

The code snippets above shows how we can perform web scraping using a Python script. The script mainly does the following in a nutshell:

1. Visit through each page of the YellowPages Singapore's clinics results in a loop.
2. Make use of XPATH expressions to retrieve the content (clinic name, address, latitude and longitude) located at specific HTML attributes.
3. Check whether the retrieved clinic info is a Dental clinic, if yes we will SKIP the result, if not we will proceed to save it.
4. When all pages are visited, the retrieved clinics information will be parsed and stored into a .CSV file with the respective columns.

PROJECT TIMELINE

PROJECT CHALLENGES

No.	Key Technical Challenges	Description	Proposed Solution	Outcome
1.	Unfamiliarity with R packages and R Shiny	Our team may encounter the use of additional R resources that were not taught in class.	- Independent Learning on R packages and R Shiny - Browsing the official RDocumentation website for support and reference - Research for online tutorials that have a specific use case for certain R packages	We managed to solve the mentioned challenge with the following resources: -
2.	Data Cleaning and Transformation	As we need to collect the data from various sources, they may have different attributes such as the Coordinate Reference System (CRS), units of measurement and etc.	Adopt a standardized process of cleaning the data, focusing with what we only need. Most of the datasets used for our project can be found in our Hands-On or Take-Home exercises and we can rely on those existing data.	We managed to solve our technical challenge with the following: -
3.	Limitations & Constraints in Datasets	There are certain assumptions that we need to make based on the context and purpose of our project, such as the average number of doctors in a particular clinic, which cannot be derived from our datasets.	Working out with the team together and figuring out a reasonable and valid assumption, together with adequate online research and consultation with Prof. Kam.	We managed to solve our technical challenge with the following: -