We are shaped by what we eat. But we are also shaped by where we eat. In this project, we investigate the food scene in Chicago. Are chefs ready to trade high-quality, fresh ingredients for cheaper stale ones? Are there places and times where food conservation has a subjective meaning? Are large-scale franchises such as McDonald’s and Starbucks under the same level of scrutiny as normal restaurants despite the economic interests at play?
We propose taking a structured look at Chicago’s food inspection scene and everything happening behind the curtain – starting with food violations and usual risk, progressing beyond year seasons and temperatures, reaching all the way down to location and block-level wealth and ethnicity.
In order to achieve this, we will dive into the Chicago Food Inspection as well as the Wealth Distribution Dataset from the Chicago City Data Portal, the Voting Age Census for Ethnicities from the US government website, and the Google Reviews dataset.
You can find the data story for this analysis here. In order to get the best out of this experience, we recommend you to open it on Google Chrome.
We've brought forward 3 big research questions we want to tackle on. We've also divided them into sub-questions, representing distinct chunks of analysis efforts:
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How fair are inspections?
- Which type of restaurants is being controlled the most? And which type of restaurant tend to pass the test the most?
- Do the inspection patterns change for large chain restaurants [i.e. from Starbucks and McDonald's] compared to smaller independent ones?
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How much do violations correlate with external events?
- How are violations/failed inspections sensitive to permanent external factors such as neighbourhood, type of establishments?
- How do violations/failed inspections correlate with temporary factors such as season, and time of the year? That is, for a given area, are violations more or less regular, or are they susceptible to external factors, such as temperature?
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Do violations and inspections reflect deeper biases?
- Is there any correlation between the number of violations and inspections with the (perceived) ethnicity of each neighborhood of Chicago?
- Is there a relationship between the inspections and restaurants' popularity on Google Local Review?
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Chicago food inspection: This dataset contains food inspections report from establishments located in Chicago from 2010 until now. The different columns contain information about the businesses themselves as well as about the inspections.
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Ethnicity: https://www.census.gov/data/datasets/2016/dec/rdo/2013-2017-CVAP.html. This data groups the ethnicities of all people able to legally vote in the US.
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Google reviews: https://cseweb.ucsd.edu/~jmcauley/datasets.html. The reviews put on google will allow us to see if the compliancy of a restaurant to follow health regulations correlates with the reviews received on this platform.
NB: A more detailed schedule and breakdown can be found in milestones.md.
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Sprint 1 (ends 02/12):
- Finish answering question 1
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Sprint 2 (ends 09/12):
- Finish answering question 2
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Sprint 3 (ends 16/12):
- Continue answering question 3
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Sprint 4 (ends 20/12):
- Finish answering question 3 (cont.)
- Finish writing the data story
- Alfonso
- initial data exploration,
- analysis of big chains/business size and google review's impact on inspection's result,
- poster creation and presentation preparation
- Beatriz
- initial data exploration,
- analysis of business type and other permanent external factors' (city block) impact on inspection's result,
- poster creation and presentation preparation
- Jade
- initial data cleaning,
- analysis of business' neighborhood location's and prevalent ethnicity impact on inspection's result,
- poster creation and presentation preparation
- Mahmoud
- initial data cleaning,
- analysis of temporary external factors' (temperature) impact on inspection's result,
- poster creation and presentation preparation