Food Inspection Violations Data

Food inspection violations data with establishment and violation details

By Gove Allen [source]

About this dataset

> > Additionally, geographical coordinates provide precise location information through latitude and longitude values. The ward in which each establishment is situated within their respective city also appears for classification purposes. > > Further insights are captured in descriptions of detected violations that provide contextual details about specific incidents. Each violation corresponds to a category identified by a distinct code. Point levels associated with these violations indicate severity or significance. In addition to identifying issues discovered during inspections, inspectors may leave comments or notes reflecting observations made during visits. > > Fines imposed for each violation are also documented alongside their respective inspection points for reference. > > This rich collection of data serves researchers and interested stakeholders seeking to analyze trends related to food inspection violations across different establishments and locations more effectively

How to use the dataset

> > > - Understanding the Columns: > - license_no: The license number of the establishment. > - dba_name: The doing business as name of the establishment. > - aka_name: The also known as name of the establishment. > - facility_type: The type of facility (e.g., restaurant, grocery store). > - risk_level: The risk level associated with the establishment. > - address: The address of the establishment. > - city: The city where the establishment is located. > - state: The state where the establishment is located. > - zip : The zip code of the establishment. > - latitude : The latitude coordinate of the establishment's location > - longitude : The longitude coordinate of the establishment's location > ‍‍‍ > - Key Information: > > - Description: It gives you a description or details about each violation recorded during inspections. > > - Category: It provides information about which category each violation belongs to. > > - Code: Each violation has its unique code assigned for identification purposes. > > - Fine: It shows you how much fine was imposed for each violation if applicable. > > -point_level: Indicates the level of violation points assigned to each violation > > -inspector_comment: It contains comments or notes made by inspectors during inspections. > > > - Exploratory Analysis: > > Now that we have an understanding of what each column represents, let's explore some ways in which we can analyze this data: > > a) Violation Categories Analysis: Analyzing violations based on their categories can give us insights into the types of violations that are most common and prevalent in food establishments. You can plot a bar chart to visualize the distribution of violation categories. > > b) Risk Level Analysis: Analyzing violations based on risk levels assigned to establishments can help identify high-risk areas or establishments that may require further attention from inspectors. You can create a pie chart or bar chart to visualize the distribution of risk levels. > > c) Fine Analysis: By analyzing fines imposed for each violation, you can identify establishments that consistently violate regulations and incur higher fines. You can calculate statistics like average, maximum, and minimum fines imposed. > > d

Research Ideas

> - Identifying patterns and trends in food inspection violations: This dataset can be used to analyze and identify common violations, their categories, and codes. By examining the inspector comments and risk levels associated with each violation, patterns can be identified to help improve overall food safety measures. > - Comparing violation rates across different types of facilities: The dataset provides information on the type of facility (e.g., restaurant, grocery store). By analyzing the violation rates for different types of facilities, one can identify which types of establishments are more likely to have violations and address any underlying issues. > - Geospatial analysis of food inspection violations: With latitude and longitude coordinates available for each establishment's location, this dataset can be used for geospatial analysis. It could help identify specific areas or neighborhoods where violations are more common, enabling targeted interventions and improvements in those areas

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > See the dataset description for more information.

Columns

File: establishment.csv

File: inspection_point.csv

File: violation.csv

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Gove Allen.