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San Francisco Law Enforcement Encounters
Government & Civic Records
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About
This dataset contains details of police stops carried out by the San Francisco Police Department between 2018 and 2023. It records information such as the date, time, and duration of each stop, alongside its location. Key demographic data about the person stopped is included, covering their perceived race/ethnicity, gender, age, LGBT status, English proficiency, and any perceived or known disability. Furthermore, the dataset outlines the stated reason for the stop, actions taken by the officer, the basis for any searches or property seizures, evidence or contraband found, and the eventual results of the stop. This information provides a valuable resource for understanding policing activities and their impact on different communities.
Columns
- doj_record_id: A unique identifier for each individual record.
- person_number: A numerical identifier assigned to each person involved in a stop.
- agency_ori: The Agency Origin Identifier.
- stop_datetime: The specific date and time when the stop occurred.
- duration_of_stop: The length of the stop, measured in minutes.
- is_stop_response_to_call: A boolean indicating if the stop was in response to a call.
- location: The geographical location where the stop took place.
- district: The police district corresponding to the stop's location.
- city: The city where the stop occurred.
- perceived_race_ethnicity: The perceived race or ethnicity of the person stopped.
- perceived_gender: The perceived gender of the person stopped.
- is_lgbt: A boolean indicating whether the person stopped identifies as LGBT.
- perceived_age: The perceived age of the person stopped.
- perceived_age_group: The age group categorisation based on the perceived age.
- had_limited_or_no_english: A boolean indicating if the person stopped had limited or no English proficiency.
- perceived_or_known_disability: An indication of any perceived or known disability of the person stopped.
- reason_for_stop: The officer's stated reason for initiating the stop.
- traffic_violation_type: The specific type of traffic violation, if applicable.
- traffic_viol_cjis_off_code: The CJIS officer code related to a traffic violation.
- traffic_viol_off_code: The officer code for a traffic violation.
- traffic_viol_off_statute: The officer statute for a traffic violation.
- suspicion_cjis_off_code: The CJIS officer code related to suspicion.
- suspicion_off_code_txt: A description of the officer code for suspicion.
- suspicion_off_statute: The officer statute for suspicion.
- suspicion_sub_type: The sub-type of suspicion.
- actions_taken: The actions performed by the officer during the stop.
- basis_for_search: The stated basis or reason for any search conducted during the stop.
- basis_for_property_seizure: The stated basis or reason for any property seizure during the stop.
- type_of_property_seized: The classification of property seized during the stop.
- contraband_or_evidence: An indication of whether contraband or evidence was found.
- results_of_stop: The outcome or further actions resulting from the stop.
- longitude: The longitude coordinate of the stop location.
- latitude: The latitude coordinate of the stop location.
- supervisor_district: The supervisor district associated with the stop location.
- analysis_neighborhoods: Neighbourhood analysis codes for the stop location.
- perceived_race_ethnicity_code: A numeric code for the perceived race/ethnicity.
- perceived_gender_code: A numeric code for the perceived gender.
- perceived_or_known_disability_code: A numeric code for the perceived or known disability.
- reason_for_stop_code: A numeric code for the reason for the stop.
- actions_taken_code: A numeric code for actions taken during the stop.
- basis_for_search_code: A numeric code for the basis of the search.
- basis_for_property_seizure_code: A numeric code for the basis of property seizure.
- type_of_property_seized_code: A numeric code for the type of property seized.
- contraband_or_evidence_code: A numeric code for contraband or evidence found.
- suspicion_sub_type_code: A numeric code for the sub-type of suspicion.
- results_of_stop_code: A numeric code for the results of the stop.
Distribution
The dataset is provided in a CSV format, with a file size of 120.74 MB. It includes 46 distinct columns and approximately 250,000 records.
Usage
This dataset is suitable for a variety of analytical tasks, including:
- Analysing racial disparities in police stops and identifying potential biases or discriminatory patterns.
- Investigating connections between the stated reason for a stop and subsequent actions taken by officers.
- Exploring geographical variations in police stop patterns and their correlations with socio-economic factors.
- Determining how traffic violations influence the likelihood of a search or property seizure during a stop.
- Examining the role of perceived or known disabilities in police stops and any disparities in treatment.
- Developing predictive models to estimate the duration and outcome of police stops based on various factors.
- Investigating the use of force during police stops and its correlation with different variables.
- Analysing changes in police stop patterns over time to identify emerging trends or shifts in enforcement strategies.
Coverage
The dataset covers police stop data exclusively from San Francisco and spans a time period from 2018 to 2023. It includes demographic information such as perceived race/ethnicity, gender, age, LGBT identity, English language proficiency, and perceived or known disability. It is noted that certain fields, such as 'perceived_or_known_disability' and details related to 'suspicion' or 'traffic violations', may have a significant proportion of missing data.
License
CC0: Public Domain
Who Can Use It
- Researchers and Academics: For studies related to criminology, sociology, urban policy, and social justice.
- Policy Makers and Government Analysts: To inform public safety policies, evaluate law enforcement practices, and address community concerns.
- Journalists and Activists: For investigative reporting and advocacy regarding policing and civil liberties.
- Data Scientists and Analysts: To perform statistical analysis, develop predictive models, and uncover insights into law enforcement data.
Dataset Name Suggestions
- San Francisco Police Stop Data 2018-2023
- SFPD Stop Activity Log
- San Francisco Law Enforcement Encounters
- Urban Policing Patterns: SF 2018-2023
Attributes
Original Data Source: San Francisco Law Enforcement Encounters
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