NC DataPalooza 2017 Open Data Competition Kickoff
Here are the teams competing in the 5th Annual NCDataPalooza Kickoff for the 2017 open data competition:
Web-application (website) that suggests a list of graduate schools that future graduate students have a higher chance of being admitted into based on students’ personal information (GPA, GRE, potentially, the country they are from, financial capabilities).
Techies on Tour: lowering the barriers to inclusive, community-driven tech
(Lory Mills, Suzanne Beaumont, Stephen Larrick, Noel Isama, David Licause)
A simple coordinated engagement model to bring volunteer technologists to nonprofits and community organizations, to listen to needs and offer support. Outcomes will include best practice guidance for brigades and other tech groups to engage with community organizations, an index/registry of local non-profits and their technical needs, an outreach list of tech volunteers, and a simple routing system to direct a recurring “flash mob” style event focusing all available volunteers on one community org per month.
(Brad Broge, Zack Clark)
Have a unified park locator app that can find and display all parks, regardless of jurisdiction
(William McGuire, Sophia Newton, Zeydy Ortiz, Mark Hutchinson)
Aggregating social data to prevent or identify price gouging and shorten the time for prosecution when incidents occur
Are we represented?
(Leonard Lawson, Eric Jackson, Janelle Bailey, Will Hartye, Laura Biediger, Lorien Olive, Jason Jones)
Shed light on how representative our elected bodies are by comparing the demographics of local & state elected bodies with the demographics of the communities they represent. Goals are to visualize potential discrepancies in an easy-to-understand way and to provide a tool for analysis of potential impact on other community outcomes.
(Kristen Heibel, Jim Scarborough, Rob Montalvo)
Improve on the idea of a weather radio by using the internet to retrieve warning polygons, only alerting on actionable items for the singular installed location, and delivering site-specific sheltering instructions. This facilitates sheltering when needed, no unnecessary sheltering, and improves compliance through site-specific messaging.
(Sriharish Ranganathan, Anand Niranjan)
We are currently working on building a simple and Interactive question and answer chatbot to get restaurant information. Our chatbot understands What, When, Why, Who, Where, Which, How based questions and looks for the relevant response based on the question parameters and a set of rules that are defined for each question type and the system returns the most relevant answer to the question.
Prepared by Suzanne Beaumont