XccessPoint Proposal

Proposal

Poster

Application

Research Paper

Project Description

Our team's objective is to analyse and determine how these facilities such as transportation, school and healthcare services would impact the accessibility level around HDB.

Motivation

Social inequality has been the hot topic in recent years as government start to find sustainable ways to tackle the increasing inequality and stratification in Singapore.However, with constant development and improving infrastructure around Singapore, the impact on accessibility has not really been research upon.

Data Sources

Dataset	Description	Data Type	Source(s)
Singapore Regions	To facilitate urban planning, the Urban Redevelopment Authority (URA) divides Singapore into 5 regions, namely Central, West, North, North-East and East Regions.	SHP	Data Source
Singapore Planning Area	Indicative polygon of planning area boundary. To facilitate urban planning, the Urban Redevelopment Authority (URA) divides Singapore into 55 planning areas	SHP	Data Source
Singapore Planning Subzone	Indicative polygon of subzone boundary. The Planning Regions are divided into smaller Planning Areas. Each Planning Area is further divided into smaller subzones which are usually centred around a focal point such as neighbourhood centre or activity node.	SHP	Data Source
HDB	List of HDB location via postal code	CSV	Data Source
School facilities	List of education facilities in Singapore	CSV,KML	Data Source Data Source
Government Markets Hawker Centres	Contains Address of Hawker Centres in Singapore	KML	Data Source
Heathcare Facilities	Contains Address to Healthcare Facilities in Singapore	Website Information	Data Source Data Source
LTA Mrt station	The layer contains the locations of MRT station exits.	KML	Data Source
Bus Stops	All bus stops, bus interchanges, bus terminals in Singapore.	CSV	Data Source

Application Prototype

User Interface Prototype

Technical Challenge

Tools and Data Architecture

Challenges:

1. Most of the datasets retrieved provided only addresses, not coordinates. Thus, first we had to geocode each point to get the coordinates.

2. Some datasets had CRS WGS84 while some had SVY21. Thus, we had to convert all to SVY21

3. Calculating the distance from each of the 8500 houses to each of the 5000 bus stops was computationally impossible. Thus, we had to use Raster to create a radius around each house and calculate distance from that house to the bus stops which lay within the radius to get the closest bus stop

4. Plotting 8000 points on a map was very cluttered and not insightful. Thus, we provided the user options to select regions/subzones/towns for better plots

Related Work

1. https://www.researchgate.net/publication/242450034_A_GIS-BASED_MULTI-CRITERIA_ANALYSIS_APPROACH_TO_ACCESSIBILITY_ANALYSIS_FOR_HOUSING_DEVELOPMENT_IN_SINGAPORE/download
2. https://www.researchgate.net/publication/221354375_GIS-Based_Spatial_Distribution_and_Evolvement_Analysis_of_Urban_Affordable_Housing_A_Case_Study/download

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