DS-18 · Predicting the health inspection

Using restaurant inspection history, can you predict which establishments will fail their next inspection — and should a health department actually use such a model to target visits?

Virginia restaurant inspection records (https://www.vdh.virginia.gov/environmental-health/food-safety-in-virginia/) or the Richmond open data portal (https://data.richmondgov.com/).

Inspection frequency is itself risk-based, so past inspection count is a proxy for past suspicion — a feedback loop. Establishments that closed vanish from the data. Violation severity coding changes over time.

A next-inspection risk model with the feedback loop explicitly analyzed, a discussion of what targeted enforcement would do to small and independently-owned establishments, and a deployment recommendation.

CT: identifying the feedback loop unprompted. CR: constructing a survivorship-corrected sample. CO: peer challenge on deployment ethics. CM: a recommendation memo to a health department. CZ: enforcement targeting has distributional consequences.

An environmental-health specialist, restaurant operator, or public-sector data analyst reviews the loop analysis at Checkpoint 1 and the memo at Checkpoint 2. School-supervised.

R1: framed as a targeting decision, not just prediction. R2: closures and coding changes handled. R3: a "inspect everyone equally" baseline. R4: performance plus a feedback-loop simulation or argument. R5: a real memo. R6: distributional impact addressed with a stance.

(a) Prior inspection count is the top feature and the model has learned who was already suspected. (b) The student recommends targeting without noticing it concentrates on small independent restaurants.