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Reading Chicago's food inspection data

Chicago's inspection dataset has no score, packs violations into one column, and changed its violation definitions on July 1, 2018. How to read it correctly.

By Food Inspection Data editorsPublished Sources checked September 17, 2026

Chicago's Food Inspections dataset (4ijn-s7e5) covers inspections "of restaurants and other food establishments in Chicago from January 1, 2010 to the present." It has no numeric score, its violations sit in a single text column, and its definitions changed in the middle of the series. This guide walks through each. Quotes come from the city's dataset description document and portal metadata, checked September 17, 2026.

Risk 1, 2 and 3

"Each establishment is categorized as to its risk of adversely affecting the public's health, with 1 being the highest and 3 the lowest. The frequency of inspection is tied to this risk, with risk 1 establishments inspected most frequently and risk 3 least frequently."

Two practical points. Risk describes the establishment's type of operation, not its record, so a risk 1 label is not a mark against a restaurant. And because risk 1 places are inspected more often, they are over-represented among inspection rows; compare within a risk level when you can.

Results: pass, pass with conditions, fail

"An inspection can pass, pass with conditions or fail." The description defines each:

Chicago inspection results, from the city's dataset description
ResultDefinition
PassFound to have no critical or serious violations.
Pass w/ conditions"Establishments receiving a 'pass with conditions' were found to have critical or serious violations, but these were corrected during the inspection."
FailCritical or serious violations "that were not correctable during the inspection." A fail "does not necessarily mean" the establishment's license is suspended.
Out of business / not located"Establishments found to be out of business or not located are indicated as such."

Pass with conditions is the result most often misread. It is not a failure, and it is not a clean pass either: problems in the serious categories were found and fixed on the spot.

Inspection types

The description lists these types, and notes that "Re-inspections can occur for most types of these inspections and are indicated as such":

Mixing types changes results. A complaint or license inspection is not a routine sample; for a routine view, start from canvass inspections.

The July 1, 2018 break

On June 29, 2018, the city's open-data team posted a notice: "On 7/1/2018, the Chicago Department of Public Health's Food Protection unit will be changing its definition of violations." The columns stayed the same, but "the actual violations will change substantially." The notice warned that the distribution of results "may change, especially in the period immediately following 7/1/2018," and advised "considering the possibility that apparent changes in long-term trends could be artifacts of this redefinition of violation types." The city created two filtered views, one through June 30, 2018 and one from July 1, 2018 on. (The notice's original address no longer loads; the link above is an archived copy. The live dataset page still links to it.)

The dataset description document predates this change. It still says an establishment can receive "one or more of 45 distinct violations (violation numbers 1-44 and 70)," with pass defined by violation numbers 1-14 (critical) and 15-29 (serious). Those numbers describe the pre-July 2018 scheme. Do not use them to classify violations recorded after that date, and do not treat a violation number as meaning the same thing on both sides of the break.

Working with the Violations column

Per the 2018 notice, the Violations column "will still contain the violation number, description, and comments; with separate violations delimited by the pipe character with a space on each side." One row is one inspection; split the column on | to get one row per violation.

Other things to know

Chicago's results cannot be lined up against a points-based city like New York or Los Angeles County; see why scores do not compare.