Inspiration
We saw that many students at UMass and college campuses across the nation are cramming themselves into studying at a handful of extremely busy places, when that location may not even fit their needs.
What it does
Study Spot enables students to filter out locations based on the type of study environment they desire and sorts the output by distance, making the nearest suitable study spot first on their list. It then offers them directions to get there, in case they've truly never heard of the place.
How we built it
Before we started programming, we decided the most reliable information would come from the students. So we spent an hour or two going to different late night dining locations and surveyed students' favorite spots to go to. Then we had to make the app. We first created an outline of our design on a whiteboard; what APIs we could use, the filters we'd screen for(as given in the survey), and anything we could do to make the user's experience as easy as possible. These features included filters based on various attributes of the study spot, showing a page of all a given location's traits once they select a spot, and auto-loading of Google Maps navigation at the tap of a button to any of the study spots. This is, of course, why we implemented the Google Maps API. But for searching and filtering our results, we benefited from the Algolia API. We looked at several demos and used those concepts within the context of our project. Figuring out how to implement the filters was a different story. To filter distance we used the Google Maps API to calculate our distance from each study spot and filtered out numbers less than or equal to the input, making sure we cover all study spots within the given distance. We would then take the other filters such as Volume level and append them to a long filter string connected by ANDs, meaning all filters must be satisfied for a given result to be returned. We displayed all information on a selected study spot and connected to the Google Maps application in order to offer directions to the given Study Spot.
Challenges we ran into
We ran into challenges in developing the Android application as we worked with the Algolia API, which was fairly new territory for all of us. We were able to use demos initially to provide a searchable list interface, but we soon realized our app could only achieve its true potential if we embedded a filter selection menu within the application. Because the demo did not include this, I initially struggled to create an effective filter for our highly nuanced process. Additionally, I took time to realize that the asynchronously gathered list could still be sorted in a cost-effective way.
Accomplishments that we're proud of
We created exactly what we set out to do, and we did it so effectively that we were able to get started on the next step forward, a website.
What we learned
-How to use Algolia API on Android -How to connect an Android app to Google Maps -The difficulties of creating a website -How to effectively debug an Android app throughout the development process -How to take the logic we would use in a computer science course and implement it into the field
What's next for Study Spot
Since we had time towards the end of the night, we were actually able to begin our work on the next step for StudySpot, a website which would also use the Algolia and Google Maps API's. We have linked the work for the website we were able to do so far here: https://github.com/sheier1/HackUMassV . We did not put this link with the Android app link because, unlike our Android app, it isn't complete.
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