FHI 360, 1825 Connecticut Ave NW #2, Washington, DC 20009
Your organizers are Josh Tauberer, Eric Mill, and Julia Bezgacheva. Please find us if you need anything.
Election-Year APIs, by Lindsay Young (Sat 10:15)A brief overview of API basics, followed by an in-depth look at the FEC API. Then, we’ll work with some resources from Pro Publica and Open Secrets.slideslidess, json chrome extension,
Intro to Command Line & Coding, by Jessica Garson (Sat 1:30) In this class you will learn the basics of the command line, write your first two Python programs, and discuss resources for further learning. Slides and Scripts: https://github.com/JessicaGarson/Open-Data-Day-Intro-to-Coding My twitter profile is @jessicagarson
Intro to D3.js, by Chris Given (Sat 3:15): D3 is an expressive and powerful tool used to create many of the interactive data visualizations we see on the web today. If you’re trying to do more with your data than is possible in Excel or Tableau, or if you’re making websites that require interactive, data-driven graphics, this workshop will lead you through the build of a complete D3 project
Please add your project by adding a link here to a new page on this hackpad for your project.
Best practices: Clearly state the Problem, resources(e.g. datasets) available, suggested solution, skillset requested of volunteers, how solutions will be sustained post-hacking, and contact for point person.
Problem: we want more voters to use available early voting centers in order to reduce the possibility of long lines at polling stations on Election Day. By analyzing voter demographics and past voting patterns, can we determine where and to whom we should be promoting early voting?
Bullets for the Mayor (AKA what we accomplished at OpenDataDay DC on Saturday March 5, 2016):
Today with the work of a dozen volunteers, we developed a custom app interface, built a prototype app, and wrote code to parse UPC databases to help build an app for volunteers at the Capital Area Food Bank (CAFB) to more quickly classify bulk donated salvage packaged food into "wellness" or "not wellness". We also made progress in using Census and CDC data on were there are higher concentrations of diet related health problems in the DC Maryland Virginia (DMV) area so CAFB initiatives can better target those areas. Github link: https://github.com/cafbdk. We even had some progress on another mapping project where the American Red Cross may want to consider installing Smoke Alarms across the US.
The next step is to tie all 3 aspects of the app all together into an alpha version during our event on April 2 and build a first version of the diet-related health map of the DMV. After that, we will work with the Capital Area Food Bank to further refine this prototype with their feedback. Here's the link to the Meetup placeholder to RSVP for the next event: http://www.meetup.com/DataKind-DC/events/229365671/
Take-away: Unemployed workers may need to travel long distances to obtain work in high opportunity areas. This project pairs workers with jobs in order to maximize income after commuting expense. The results should inform transit decisions for DC.
Used this info to identify high underutilization tracts (unemployment >= 10%) and high opportunity tracts (areas where there are more jobs than people living in the area).
We passed this info along to our Operations Research team to run their model built in Gurobi to determine the optimal job paths. My understanding is that they were solving a network optimization model (transportation model) but this might be very far from correct. They used number of people unemployed in a tract as the source number and number of available jobs (computed using 6% (our estimate of the national unemployment rate) times the number of jobs, which is a really rough estimate).
Another project is to find information about the number of people with given skills (by location) and the number of people working in an area with given skills (by location). This is relevant and important but not doable in the time frame.