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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2016/12/food-inspection-forecasting.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>There are more than 15,000 restaurants in Chicago, but fewer than 40 inspectors tasked with making sure they comply with food-safety standards. To help prioritize the facilities targeted for inspection, the City of Chicago used R to create a model that <a target="_blank" rel="nofollow" href="https://chicago.github.io/food-inspections-evaluation/">predicts which restaurants are most likely to fail an inspection</a>. Using this model to deploy inspectors, the City is able to detect unsafe restaurants more than a week sooner than by using traditional selection methods, and cite 37 additional restaurants per month.</p>
<p>Chicago's Department of Public Health used the <a target="_blank" rel="nofollow" href="https://mran.microsoft.com/documents/what-is-r/">R language</a> to build and deploy the model, and made the code <a target="_blank" rel="nofollow" href="https://github.com/chicago/food-inspections-evaluation/">available as an open source project</a> on GitHub. The reasons given are twofold:</p>
<p>An open source approach helps build a foundation for other models attempting to forecast violations at food establishments. The analytic code is written in R, an open source, widely-known programming language for statisticians. There is no need for expensive software licenses to view and run this code.</p>
<p>Releasing the model as open source has had benefits for beyond Chicago as well: Montogomery County, MD adopted the process and also saw <a target="_blank" rel="nofollow" href="http://www.mayorsinnovation.org/images/uploads/pdf/1_-_Montgomery_MD.pdf">improvements in its food safety inpection process</a>.</p>
<p>You can see how the model is used in practice in the <a target="_blank" rel="nofollow" href="http://www.pbs.org/newshour/bb/chicago-revamps-restaurant-inspections-by-tapping-into-social-media/">video below from PBS NewsHour</a>. Fast forward to the 3:00 mark to see the <a target="_blank" rel="nofollow" href="http://tomschenkjr.net/">Tom Schenk</a>, Chief Data Officer for the City of Chicago, describe how the data science team there used R to develop the model. (There's also a close-up of R code using the <a target="_blank" rel="nofollow" href="https://github.com/Rdatatable/data.table/wiki">data.table package</a> around the 6:45 mark.)</p>
<p><iframe height="370" allowfullscreen="" width="501" src="http://player.pbs.org/viralplayer/2365667294/"></iframe></p>
<p>The video also describes the <a target="_blank" rel="nofollow" href="http://blog.revolutionanalytics.com/2013/08/foodborne-chicago.html">Foodborne Chicago Twitter detection</a> system for flagging tweets describing food poisoning in Chicago (also implemented with R).</p>
<p>PBS NewsHour: <a target="_blank" rel="nofollow" href="http://www.pbs.org/newshour/bb/chicago-revamps-restaurant-inspections-by-tapping-into-social-media/">Up to code? An algorithm is helping Chicago health officials predict restaurant safety violations</a> (via reader MD)</p>
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