Food Inspection Violations Dataset

Food establishment violations and fines data

By Gove Allen [source]

About this dataset

> > The establishment.csv file contains essential information about food establishments such as their license number, name (dba_name), alternate names (aka_name), facility type (facility_type), risk level associated with each establishment (risk_level), address including street, city, state and zip code (address, city, state, zip), geographical coordinates in terms of latitude and longitude (latitude, longitude) as well as the ward where the establishment is located. > > On the other hand,the inspection_point.csv file includes details about the specific violations observed during food inspections. This includes in-depth descriptions of each violation found (Description) along with its corresponding category according to a predefined classification system(Category) and a unique code associated with it(Code). Additionally,this file also contains point levels indicating the severity of each violation(point_level) as well as any imposed fines. > > > Lastly,the violation.csv file offers further insight into violations detected during inspections by providing additional information such as fines for each violation(Fine)and inspector comments(inspector_comment). > > > By combining these datasets , it becomes possible to conduct comprehensive analysis on food establishments' compliance with safety regulations. The provided dataset will be highly useful for understanding patterns in food safety violations across different cities and states. It can assist health departments ,restaurant owners,researchers or stakeholders by identifying areas of improvement in order to enhance public health standards within this industry

How to use the dataset

> > > ## Understanding the Columns > > - license_no: The license number of the food establishment (Numeric). > - dba_name: The doing business as name of the food establishment (Text). > - Also known as aka_name. > - facility_type: The type of facility where the food establishment is located (Text). > - risk_level: The risk level associated with the food establishment (Text). > - address: The address of the food establishment (Text). > - city: The city where the food establishment is located (Text). > - state: The state where the food establishment is located (Text). > - zip :The zip code of the food establishment's location (Numeric) > 9-10: longitude: and latitude are coordinates, indicating a precise location of an establishement. > 11-12: Ward i.e administrative division in a city or town e.g New York City has five boroughs i.e Manhattan,Bronx,Queens,Brooklyn,and Staten Island(Numeric) > 13.Description: A description of the violation found during the food inspection.(Text) > 14.Category: The category to which th e violation belongs.(e.g hygiene Sanitation etc) > 15.Code: Code assigned to each violation(Text). > 16.And.Fine : amount imposed for each violation > 17.point_level:The level of severity of the violation > 18.inspector_comment: > 19.Source need to be check(need more clarification) > > ## Exploring and Analyzing Data > > > - Overall Analysis: You can analyze factors such as risk levels, facility types, and categories of violations to gain insights into the food inspection landscape. > - Fines and Penalties: You can explore the fines imposed for different violations and identify patterns or trends. > - Geographic Analysis: The dataset contains latitude and longitude coordinates for each food establishment's location. You can use these coordinates to perform geospatial analysis or visualize data on a map. > 4-

Research Ideas

> - Identifying high-risk food establishments: By analyzing the data on risk levels and violations, it is possible to identify food establishments that consistently have high-risk violations. This information can be used by regulatory authorities to prioritize inspections and ensure that necessary measures are taken to mitigate risks. > - Assessing inspection effectiveness: By analyzing the data on fines, point levels, and inspector comments, it is possible to evaluate the effectiveness of food inspections in identifying and penalizing violations. This analysis can help improve inspection protocols and ensure that they are successful in maintaining food safety standards. > - Geographical analysis of violations: By mapping the latitude and longitude coordinates of food establishments with violation data, it is possible to identify geographical areas with a higher concentration of violations. This information can be used by local authorities to target resources towards these areas for improved enforcement or educational campaigns regarding food safety practices

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.