Urban Planning: Spatial Accessibility Visualisation

What makes one neighbourhood seem like being in the middle of nowhere while another easily accessible? It comes down to a variety of parameters such as the number of amenities in an area and the average amount of time taken to get to other places in the city.

However, property prices do not always reflect the value people put on specific combinations of these factors. With a scoring system however, buyers may be able to find a home that meets all their necessary criteria without splurging on one that they guess meets their wishlist.

Benedict Chua, Jason Yip, Kenneth Chua of Eusoff Brothers (not in photo: Shannon Kok).

A team named Eusoff Brothers made up of four members from National University of Singapore (NUS) developed a prototype visualisation tool that tackles this very problem at IDE@ DataSpark Hackathon 2018.

The solution isn’t just important to newly-wed home shoppers but also to real estate companies looking to assess the value of property in a given neighbourhood, government agencies dedicated to urban planning and restructuring and even companies hoping to tap into geolocation-based marketing.

The prototype used data from DataSpark’s OD Matrix API to determine the average time taken to work and nearly 10 other APIs and data sources, to show relative spatial accessibility scores of planning areas in Singapore according to various other parameters.

For example, if I wanted to find a planning area with the highest ratio of gyms and elderly services to population, and highest number of wireless hotspots, Bukit Merah would be the best area to live in.

If, in another example, I wanted to find a neighbourhood with the highest ratio of primary and secondary schools to population, Marine Parade would be the best choice.

You can test out the live demo here.

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