In a city of roughly 3 million people, more than half of those who live there live in situations where they do not have access to a nutritious diet. This is a public health problem. People living in food deserts face twice as high a risk of cardiovascular disease. Many of Chicago's food deserts are located on the South Side. Even those who can afford to go to grocery stores often do not have time to do so and must jump through hoops just to access them. Sometimes, grocery stores are even in gang territory. Our goal is to provide a visualization mechanism to see what the problem is like on an everyday level. We often get caught up in the statistics, and the notion of going to bed hungry is so foreign to us that it has prevented us from engaging the issue. This is an attempt to help people visualize the problems of living in a food desert on the South Side of Chicago.
What it does
While there are many static one factor maps produced, there are not many maps which display the various challenges that people in food deserts may face, such as gang activity, a higher proportion of crime, etc. We scraped a whole lot of data from the U.S census, the City of Chicago, NHGIS, etc. to produce an accurate representation of how resources are allocated throughout the city, and what demographics have access to those resources.
Users can type in their address or potential residential area and see what kind of environment they are living in. Currently they can visualize gang activity, access to restaurant vs. stores, and can be shown how long it takes to get to the nearest grocery store.
Furthermore, we have integrated a postmates api into the site which will allow those who can't get access to fresh food, to attain a perhaps more diverse meal through a relatively cheap delivery service.
How I built it
We used a Django framework along with d3 for data visualization and pandas to clean the data.
Challenges I ran into
We ran into many problems acquiring datasets, unpacking and cleaning esoteric file formats such as sf1.
Overlaying an interactive map using d3 over Google Maps.
Accomplishments that I'm proud of
Got a pretty nice looking impact-full website.
What I learned
What's next for Food Desert
Building out a few more data implementations, as well as applying some neural networks towards predicting health within Chicago.