#openimpact — Food Inspection Forecasting

#openimpact — Food Inspection Forecasting

This post was created automatically via an RSS feed and was originally published at http://blog.datalook.io/openimpact-food-inspection-forecasting/

(this is one of the projects of the #openimpact shortlist)

There are over 15,000 food establishments across the City of Chicago that are subject to sanitation inspections by the Department of Public Health. Three dozen inspectors are responsible for checking these establishments, which means one inspector is responsible for nearly 470 food establishments. The Department of Public Health has systematically collected the results of nearly 100,000 sanitation inspections; meanwhile, other city departments have collected data on 311 complaints, business characteristics, and other information. With this information, the city’s advanced analytics team and Department of Public Health teamed up to forecast food establishments that are most likely to have critical violations so that they may be inspected first. The result is that food establishments with critical violations are more likely to be discovered earlier by the Department of Public Health’s inspectors.


What does it do? Optimizes food inspections with predictive analytics
Outcome Food establishments with critical violations were discovered over one week earlier during a two-month evaluation, decreasing the probability of food poisoning and serving public health.
Project page chicago.github.io
Organizations Chicago Department of Public Health, Chicago Department of Innovation and Technology, Allstate Insurance, Civic Consulting Alliance
Contact persons Tom Schenk Jr., Gene Leynes (preferred way to communicate is through the Github issues page)
Media datasmart.ash.harvard.edudatasmart.ash.harvard.educhcf.org



Source code github.com
Data used Chicago’s Open Data Portal
Data generated No
Tech stack R



Replications No (but can be applied to any inspection process)
License see here
Maintenance costs/month tbd
First steps 1. Check if similar data are available in your community
2. Reach out to your local department of public health (or whoever is responsible for organizing inspections)
3. Train your model
Interested in replicating this project? Join our DataLook #openimpact Slack channel or hit us up on Twitter.

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