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  is an open source, web based, geo enabled health data mapping & visualization tool to aggregate disparate public health data from multiple sources. The tool is largely inspired by applications like the Sunlight Foundation’s Sitegeist, which makes it cool for everyday folks to experience data in a locally meaningful way. For the most part public health data and health related data is currently a garbled mess of PDF’s, spreadsheets, proprietary formats, and static visualizations that are virtually impossible for the average person or elected official to find, interpret, compare or visualize. As civic hackers, the project team has seen firsthand that liberating data from the confines of spreadsheets and PDF documents by allowing people to easily aggregate, display and manipulate it based on location can truly be transformational. This project is funded by the Knight Foundation through their Prototype Fund. It is one of a handful of projects that were submitted to the Knight News Challenge: Health that were invited into the Prototype Fund. The original News Challenge application can be viewed here:

Activities: Over the course of the six month prototype the project team will: Develop a location-aware backend database system leveraging the Django Boundary Service or similar. Locate relevant local health data. Scrape and import data into database system. Design visualizations based on data and user needs. Test software with targeted user groups.

Outcomes: For the prototype the results will focus on northeast Oklahoma with a small subset of the potential datasets that could be aggregated when building full-scale version of this project. The prototype application will give some early results on data availability and desirability of this type of application from a user perspective.

Assessment: The team will be focused on answering the following question: Can we change people's ownership of health outcomes using a fun intuitive interface to health data? The team will use web analytics to track usage patterns and statistics to better understand who is using the system, what data is being used and where they are using it. The team will also query users to identify other data types that they would be interested in seeing.


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