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Making Restaurant Inspections Smarter

Date post: 22-Jan-2018
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Making Restaurant Inspections Smarter
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Page 1: Making Restaurant Inspections Smarter

Making Restaurant Inspections Smarter

Page 2: Making Restaurant Inspections Smarter

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

Page 3: Making Restaurant Inspections Smarter

Chicago

https://www.foodbornechicago.org/

Page 4: Making Restaurant Inspections Smarter

Chicago

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

Page 5: Making Restaurant Inspections Smarter

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

Page 6: Making Restaurant Inspections Smarter

Chicago

Page 7: Making Restaurant Inspections Smarter

New York City

Page 8: Making Restaurant Inspections Smarter

New York City

Page 9: Making Restaurant Inspections Smarter

Wake County

Wake County LIVES data

Page 10: Making Restaurant Inspections Smarter

Wake County

Yelp Reviews

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Wake County

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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.

Page 13: Making Restaurant Inspections Smarter

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.

Page 14: Making Restaurant Inspections Smarter

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.

Page 15: Making Restaurant Inspections Smarter

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.

Page 16: Making Restaurant Inspections Smarter

Thanks


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