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POSTER

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.

DATA COLLECTION

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: -