A map-based decision-support tool combining OneMap geocoding, SQL distance logic, and Python processing to evaluate school access around a chosen address.
Outcome: Turned school-proximity analysis into a practical map workflow that helps users evaluate residential options around a user-selected address.
For many households in Singapore, school access is not a secondary preference. It is part of the housing decision itself.
The difficulty is that school proximity is often checked manually, one address at a time, across separate tools. That makes comparison slow and easy to get wrong.
I built a lightweight geospatial calculator that starts with an address and turns it into a map-based school-access view.
The output highlighted both the selected address and nearby schools so users could compare proximity visually rather than reading a raw table of distances.
The SQL layer converted school coordinates and the input address into geographic points, then calculated distances between them. That made it possible to return:
Python then added logic for marker color and emphasis, making the results easier to scan on a map. Schools within the closer radius band were visually separated from those further away, while the user’s selected address was given its own marker state.
This project sits at the intersection of housing, public data, and decision support.
It showed how geospatial tooling can turn a common homebuying question into something clearer and more operational:
That is the kind of workflow I find most useful: not just analysis for its own sake, but tools that make location-based choices easier to evaluate with evidence.