Name: Durham County Food Inspections
Creator: Kaggle
License: http://opendatacommons.org/licenses/odbl/1.0/

About this Dataset

Durham County Food Inspections

Scores, Violations, and Environmental Health Speicalist Observations

By City and County of Durham Data [source]

About this dataset

This dataset contains information about food health inspections in Durham County, North Carolina. The data is gathered from multiple sources and will help you gain insight into how food safety is handled and monitored within the state. This dataset includes the score given to the establishment, comments made by inspectors, violations noted during inspection, number of repeat violations found during an inspection, amount of time taken for inspection and more! With this data at your disposal, you can make informed decisions on improving food safety standards in your local area

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How to use the dataset

This dataset contains information about food health inspections in Durham County, North Carolina. The data includes inspection scores, comments and violations from multiple establishments. This dataset can be used to gain insights into the sanitation standards across food service establishments in Durham County.

To get started with this dataset, first take a look at the columns listed above and become familiar with the field names. Once you have familiarized yourself with the data fields, you can begin exploring by filtering the data according to certain criteria (such as county or establishment type). You can also use visualization software such as Tableau to create charts and visualizations of your findings. Additionally, you can run calculations on individual fields or build queries that compare different metrics across multiple fields.

Finally, after exploring the data and analyzing it further to answer more specific questions, you can share your insights publicly or even publish research papers based on your acquired knowledge gained by working with this dataset!

Research Ideas

Using geolocation data to identify local health inspection trends in the region.

Analyzing the correlation between inspection scores, violations found, and comments made by inspectors to determine what types of establishments make for a successful inspection.

Predicting the type of establishment based on certain criteria such as seating capacity, water temp sum,travel time etc and comparing those predictions with the actual type of establishment for accuracy checks

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0

You are free to:

Share - copy and redistribute the material in any medium or format.

Adapt - remix, transform, and build upon the material for any purpose, even commercially.

You must:

Give appropriate credit - Provide a link to the license, and indicate if changes were made.

ShareAlike - You must distribute your contributions under the same license as the original.

Keep intact - all notices that refer to this license, including copyright notices.

No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.

No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: food-health-inspections_3.csv

Column name	Description
score_sum	The total score received for an inspection. (Integer)
end_inspection_ampm	The time of day the inspection ended (AM or PM). (String)
comments	Comments made by the inspector during the inspection. (String)
seats	The number of seats in the establishment. (Integer)
com_num	The unique identifier for the establishment. (Integer)
premise_name	The name of the establishment. (String)
delete_mark	A flag indicating if the record has been deleted. (Boolean)
travel_time_min	The time it took for the inspector to travel to the establishment. (Integer)
action_code_desc	The action taken by the inspector. (String)
travel_time_hrs	The time it took for the inspector to travel to the establishment in hours. (Integer)
water_temp_sum	The water temperature of the establishment. (Integer)
sewage	The sewage system of the establishment. (String)
ehs_num	The environmental health specialist number. (Integer)
insp_type	The type of inspection. (String)
type_description	The description of the type of inspection. (String)
field59	Unknown field. (String)
time_of_inspection_hr	The time of day the inspection began (AM or PM). (String)
insp_date	The date of the inspection. (Date)
num_repeat_rf_inter_viol	The number of repeat violations found during the inspection. (Integer)
est_type_num	The type of establishment. (Integer)

File: food-health-inspections_2.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 City and County of Durham Data.

Tables

Food Health Inspections 2

@kaggle.thedevastator_durham_county_food_inspections.food_health_inspections_2

9.53 kB
1 row
3 columns

CREATE TABLE food_health_inspections_2 (
  "index" BIGINT,
  "links" VARCHAR,
  "dataset" VARCHAR
);

Food Health Inspections 3

@kaggle.thedevastator_durham_county_food_inspections.food_health_inspections_3

6.45 MB
112,046 rows
88 columns

CREATE TABLE food_health_inspections_3 (
  "index" BIGINT,
  "update_user_id" DOUBLE,
  "score_sum" DOUBLE,
  "end_inspection_ampm" VARCHAR,
  "comments" VARCHAR,
  "seats" DOUBLE,
  "com_num" VARCHAR,
  "premise_name" VARCHAR,
  "delete_mark" VARCHAR,
  "travel_time_min" DOUBLE,
  "biguanide_sum" DOUBLE,
  "action_code_desc" VARCHAR,
  "travel_time_hrs" DOUBLE,
  "water_temp_sum" DOUBLE,
  "epi_type_id" DOUBLE,
  "sewage" BIGINT,
  "ehs_num" VARCHAR,
  "classification_id" DOUBLE,
  "insp_type" BIGINT,
  "type_description" VARCHAR,
  "field59" VARCHAR,
  "time_of_inspection_hr" DOUBLE,
  "insp_date" TIMESTAMP,
  "num_repeat_rf_inter_viol" DOUBLE,
  "est_type_num" VARCHAR,
  "est_type" BIGINT,
  "rpt_area_num" VARCHAR,
  "action_code_id" VARCHAR,
  "permit_type_id" VARCHAR,
  "complaint_section" VARCHAR,
  "water" BIGINT,
  "rpt_area_code" BIGINT,
  "person_incharge_fname" VARCHAR,
  "est_id" BIGINT,
  "water_ph_sum" DOUBLE,
  "end_time_of_inspection_hh" DOUBLE,
  "inspection_time_min" DOUBLE,
  "num_rf_inter_viol" DOUBLE,
  "extra_credit" VARCHAR,
  "origin" BIGINT,
  "geolocation" VARCHAR,
  "grade" VARCHAR,
  "est_group_desc" VARCHAR,
  "oss_id" VARCHAR,
  "county" BIGINT,
  "verification_required_date" TIMESTAMP,
  "void_date" VARCHAR,
  "update_date" TIMESTAMP,
  "epi_type_desc" VARCHAR,
  "ehs" BIGINT,
  "district" DOUBLE,
  "person_incharge_lname" VARCHAR,
  "territory" DOUBLE,
  "end_time_of_inspection_mm" DOUBLE,
  "inspection_reason_id" DOUBLE,
  "cdp_est_num" DOUBLE,
  "est_num" DOUBLE,
  "oss_num" VARCHAR,
  "final_score_sum" DOUBLE,
  "image_file_name" VARCHAR,
  "image_file_path" VARCHAR,
  "ehs_id" BIGINT,
  "six_point_demerit" VARCHAR,
  "group_code_id" DOUBLE,
  "request_number" VARCHAR,
  "state_id" BIGINT,
  "smoking_allowed" VARCHAR,
  "classification_desc" VARCHAR,
  "permit_status" VARCHAR,
  "sample_attendance" VARCHAR,
  "sample" VARCHAR,
  "comment_sheet_id" VARCHAR,
  "sent_to_bets_yn" VARCHAR,
  "id" BIGINT,
  "violations_id" BIGINT,
  "inspection_time_hrs" DOUBLE,
  "setup_date" TIMESTAMP,
  "time_of_inspection_mm" DOUBLE,
  "mileage" VARCHAR,
  "permit_status_id" DOUBLE,
  "ampm_of_inspection" VARCHAR,
  "followup_id" VARCHAR,
  "rpt_area_desc" VARCHAR,
  "time_of_inspection" DOUBLE,
  "chlorine_sum" DOUBLE,
  "bromine_sum" DOUBLE,
  "est_group_id" DOUBLE,
  "inspection_reason_desc" VARCHAR
);

Food Health Inspections 4

@kaggle.thedevastator_durham_county_food_inspections.food_health_inspections_4

26.9 MB
112,046 rows
3 columns

CREATE TABLE food_health_inspections_4 (
  "index" BIGINT,
  "type" VARCHAR,
  "features" VARCHAR
);