2015 IDCE Hackathon Winners
First Place: Wade Kodrin, Sergio Castro, and Hector Chapa
The team analyzed the current problems with using social media as a tool during disasters and boiled it down to a spectrum of issues: too few users that didn’t give enough data or too many users that gave so much data that it would take too much energy to sort it into useful information. While discussing this spectrum and what part we wanted to address, we also had a few rules we wanted to keep in mind when creating our solution:
- Keep it as simple as possible for both the citizen and the emergency manager (EM), it is easy to get sucked into trying to cram as many features as possible into a program that ultimately does a lot well but nothing great. It would be better to do one or two things great and make it open enough that other great things could be added later by smarter people.
- Not recreate a wheel and to try to mirror existing and familiar technologies to cut down on adoption and learning times
- It had to have a use outside of a disaster/emergency, otherwise it wouldn’t be adopted by either citizens or EMs, and would be a tool that sat collecting rust in the tool box.
The last point is where we first starting making progress when brainstorming. The idea was to create a simplified version of a digital 311/211 system that harnessed social media to collect very simple data sets during non-emergencies. Traditional 311/211 systems require the city to purchase the software and then adapt their current systems to that program. Our idea was to forgo the drawn out problem of governmental software purchase/implementation and make it as easy as logging into facebook or providing official government information to digital users on non-government platforms, similar to how Google Maps uses Google Transit to put public transit information into their maps.
A tool to gather information from all citizens would be very useful for almost any government. The locations of all fire hydrants, trees, pot holes, etc in a city could be passively collected from citizen users. The datasets collected could be endless, but ultimately would be a secondary purpose. The real use would be to develop a small army.
Over time repeated submissions by a user would be slowly accumulated and analyzed for accuracy. This could be done actively by the city, crowd sourced, or passively by comparing one user’s submissions with others or with known information. Over time this could identify power users and increase the reliability of the information that a user submits. During a disaster, instead of having to rely on information from anonymous internet strangers an EM would be getting information from known users with a rating of how accurate their previous submissions were. For clarity, imagine getting a few reports of flooding on a street during a tropical storm through social media. An EM would have to spend time to go through each report and see if there is truth to the reports or merely an exaggeration, time that the EM may not have. The EM would then have to determine if resources should be spent to verify the report, such as sending a police officer to verify the report. If our tool were used prior to the tropical storm, the EM could see the rating for each report’s user. If a user with a high rating or several users with moderate ratings are all reporting similar information, the EM instantly has more faith in those sources and would not have to spend time or resources to verify the reports of flooding.
The team realized that with a rewards system, the tool we developed could be applied to both ends of the spectrum for situations with too much or too little information. In locations with few social media users, the city could incentivize citizens to participate and thus grow the number of users. Some ideas were that the street/neighborhood that has the most users could get their sanitation fees refunded for that month, users that reach a certain score could get a discount at participating local businesses, or the top users could simply be recognized for the help at a press conference. Rewarding users could also help ensure accurate information is given, thus solving the problem of having to sort through too much inaccurate information from locations with many social media users.