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Thousands of "traffic" related Tweets were collected during the duration of the hackathon.
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Mobile view and responsive design.
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Implementing the geocoding search allows users to find any location, even with missing or misspelled characters.
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Geo-location allows use to view heatmap points with precision accuracy.
Inspiration
Big data collection, real-time activity monitoring, exposure to new and unknown API's, and handling big data.
What it does
The user can select any location using the intelligent Google Geocoder search and choose a predetermined category that was selected by our sample databases. The Twitter data was automatically collected through their API and was stored within a MySQL database.
How we built it
We began by separating the front-end UI/UX, intermediate manipulation of data, and back-end data collection and storage to efficiently develop the application. The web front-end was developed first to outline the general function of #Tweet_Waves and established what type of data was necessary to collect. Google and Twitter API were then introduced to gather and display information picked from a specific tweet. For functionality, we implemented the Google Geocoder search tool that allows the user to input virtually any location identifier and will travel them to that point. We connected the streaming amount of information provided by Twitter into our MySQL database using Python and utilized PHP to transfer that data into the application.
Challenges we ran into
Rate limiting errors, transferring data to and from PHP to JavaScript, learning and utilizing new API's, organizing large amounts of code, and limited access to database through NKU VPN.
Accomplishments that we're proud of
Team effectiveness, ability to produce a working project, created a meaningful application that could be used in modern society/business, professional and contemporary design, overcoming the challenge of learning something new, and problem-solving.
What we learned
Multiple new programming languages and API's and how to effectively manage large amounts of data.
What's next for #Tweet_Wave
Longer development time, incorporate better visualizations, current day/yesterday/week sorting of data, potential for monetization, acquiring larger amounts of Twitter data, allowing the user to select any filter criteria, ability to return the information for research, possible use for startup companies, product and demographic tracking, migrating to a more accessible database.
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