Making Restaurant Inspections Smarter

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Ian Henshaw

Presentation at Triangle Open Data Day 2016, March 5, 2016. Chris Mathews - Making Restaurant Inspections Smarter

Food

Me
 Senior DBA at Wake County
 13 years
 Code for Raleigh Brigade Captain
 Business Analytics student @ Wake Tech
 Lover of craft beer and Tracy – not in that order
 ChrisTheDBA – Gmail, Github, Slack, Twitter

Chicago
https://www.foodbornechicago.org/

Chicago
http://chicago.github.io/food-inspections-evaluation/

Chicago
 Establishments that had previous critical or serious violations
 Three-day average high temperature
 Nearby garbage and sanitation complaints
 The type of facility being inspected
 Nearby burglaries
 Whether the establishment has a tobacco license or has an incidental alcohol
consumption license
 Length of time since last inspection
 The length of time the establishment has been operating
 Inspector assigned

Wake County
 The results are inconclusive that average restaurant ratings may indicate how well
a restaurant performs during a food inspection administered by Wake County
Environmental Services.
 Additionally, we see that restaurants with a higher amount of foodborne illness
flags (as derived from the text within a restaurant's reviews) tend to have lower
average restaurant ratings.
 The results of this project only suggest that restaurant ratings may be correlated
with foodborne illness outbreaks. In order to further investigate a possible
association between restaurant reviews and foodborne illness outbreaks,
additional statistical and study design methods must be considered to improve
the validity and robustness of this project.

Wake County – Next Steps
 More Data
 Additional review sources: Incorporate restaurant reviews from other sources in addition
to yelp.
 Additional Restaurants: Include more than 200 restaurants in order to have a bigger
sample size.
 Improve match rate between restaurant grade data and restaurant review data: Use
restaurant name and address.
 Storage Solution for data – Now using OpenDataSoft portal.

Wake County – Next Steps
 Refine foodborne illness flag (FBI) classification: Perform a more thorough literature
review to identify the best words to use for the FBI flag derivation.

Wake County – Next Steps
 Apply additional statistical methods and machine learning techniques:
 Apply t-test/ANOVA to identify whether the difference in average restaurant rating is
statistically different between restaurant grade groups/fbi flag groups.
 Explore the ability to prospectively predict foodborne illness outbreak or restaurant grade
given previous restaurant review ratings.

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