Walking long distances to our favorite study spots only to find out it's full is a daily occurrence in school. NUSGotSpace offers a convenient solution to this problem. By tapping into existing infrastructure we are able to inform users of the vacancy of their study spots and suggest new alternatives.
What it does
‘NUS Got Space?’ is a friendly chat bot that informs users about the crowd density of NUS study spots. By tracking student WIFI connections to various access points around campus, Got Space? helps students find the least crowded areas and save time.
How I built it
We built it by scrapping through csv file data, generating and formulating usable data. Thereafter, we presented the information through the Python Telegram Bot API. We chose Python Telegram Bot API as Telegram is one of the most popular messaging applications used by NUS students. MatPlotLib is then used to illustrate crowd levels over the last hour.
Challenges I ran into
We were not able to acquire real time data from WIFI APs from NUS IT, thus we needed to generate our own data. This was done in accordance to the key pieces of information that NUS IT's real time data would provide. Another challenge concerned how to determine if someone was occupying a particular seat and not just passing through.
Accomplishments that I'm proud of
It was the very first hackathon for three of our group members and hence coming into this hackathon we were surprised by the rigor of the event. Without much prior experience in telegram bot development, we managed to put together a workable and scalable model that includes useful features for the client, combining key ideas such as geoprocessing, data analysis and UX design.
What I learned
We learned how to handle specific API such matplotlib, geopy, telegram and pandas. We learned the importance of writing code that is scalable and dynamic that would aid us in our aspirations to deploy a live version for the NUS student population.
What's next for NUS Got Space?
We are hoping to collaborate with NUS IT, the technological hub for NUS. This would give us access to real time WiFi data around campus. With real time data, we would be able to truly save NUS students the disappointment of not being able to find a study space.
In addition, we are hoping to generate stronger data analytics so that we can provide insights for utilized or underutilized study spaces in NUS. It would then be possible to optimize NUS' Urban Space Configuration, and allow NUS to better plan future developments.
With more data, we could even implement Artificial Intelligence (AI) to predict the trends and popular study areas.