We are still talking a lot of data at North Park – in particular Chicago data. So I’m going to start getting my hands dirty working with this data to build capacity for future partnerships with faculty and students. So here is the first in what I hope to be many installments of the “Working with Chicago Data” series.
Mapping Chicago’s Grocery Stores
First step: Download data from the Chicago Data Portal (https://data.cityofchicago.org/). I’m using the Grocery Store 2013 dataset for this example.
The data itself seems pretty clean and well formatted. I’m going to use Tableau for this example because that’s the tool I’m learning right now. I opened Tableau and imported the spreadsheet from the Chicago Data Portal. I ended up creating 4 different visualizations based on this data.
The first is a map of grocery store locations. It uses the latitude and longitude from the dataset to create points. Pretty standard and vanilla.
These next map is much more interesting. It takes into account the size of the store (measured in square footage) and codes that as size and color. Larger stores have larger, darker circles.
The last two maps were just variations on the second map. One version filtered out “small stores” that were less than 10,000 square feet. The other filtered out stores with the work “liquor” in the title. On a technical levels, these filters were easy to apply. However, I’m completely aware of the cultural assumptions I’m bringing to bear here. When I (white, affluent, middle class) think about a grocery store I think about a large store that doesn’t have the word “liquor” in the title.
That’s that! It was pretty easy to get this data and put it to use in the form of a map. I used Tableau here but I could also use Excel (with the power map add) or a more specialized tool like ArcGIS.
In terms of next steps or extensions:
- It would be interesting to compare results using a different tool. Might be good to showcase the basic steps for using each tool.
- It would be very interesting to add neighborhood boundaries and/or other information such as demographic information and/or economic status. I’ll have to look at ways to incorporate this data.
- It would also be very interesting to combine this data with user feedback like Yelp reviews.