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Traffic accident data for August 2021.............

The ‘Narr’ variable represents the number of narratives (statements) that Officers responding to a call for service complete for a specific incident. Narratives may include an initial statement, statements of fact, and/or supplemental statements. Date fields: Date fields are displayed in the table with data type string. The string data type is typically used to represent text. All date information is accurate but will sort as text in the online table. Use the download feature if you would like to sort by date.More information: Visit the Worcester Police Department webpage to learn more about their services, programs, and initiatives.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

In July 2019, the Metropolitan Police Department (MPD) implemented new data collection methods that enabled officers to collect more comprehensive information about each police stop in an aggregated manner. More specifically, these changes have allowed for more detailed data collection on stops, protective pat down (PPDs), searches, and arrests. (For a complete list of terms, see the glossary on page 2.) These changes support data collection requirements in the Neighborhood Engagement Achieves Results Amendment Act of 2016 (NEAR Act).The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from July 22, 2019 to December 31, 2022. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest.If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.” Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date ofbirth and the date of the stop.There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors. On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS.Due to this transition, the data collection and structures for the period between August 1, 2021 – December 31, 2021 were changed. The list below provides explanatory notes to consider when using this dataset.New fields for data collection resulted in an increase of outliers in stop duration (affecting 0.98% of stops). In order to mitigate the disruption of outliers on any analysis, these values have been set to null as consistent with past practices.Due to changes to the data structure that occurred after August 1, 2021, six attributes pertaining to reasons for searches of property and person are only available for the first seven months of 2021. These attributes are: Individual’s Actions, Information Obtained from Law Enforcement Sources, Information Obtained from Witnesses or Informants, Characteristics of an Armed Individual, Nature of the Alleged Crime, Prior Knowledge. These data structure changes have been updated to include these attributes going forward (as of April 23, 2022).Out of the four attributes for types of property search, warrant property search is only available for the first seven months of 2021. Data structure changes were made to include this type of property search in future datasets.The following chart shows how certain property search fields were aligned prior to and after August 1, 2021. A glossary is also provided following the chart. As of August 2, 2022, these fields have reverted to the original alignment.https://mpdc.dc.gov/sites/default/files/dc/sites/mpdc/publication/attachments/Explanatory%20Notes%202021%20Data.pdfIn October 2022 several fields were added to the dataset to provide additional clarity differentiating NOIs issued to bicycles (including Personal Mobility Devices, aka stand-on scooters), pedestrians, and vehicles as well as stops related specifically to MPD’s Harbor Patrol Unit and stops of an investigative nature where a police report was written. Please refer to the Data Dictionary for field definitions.In March 2023 an indicator was added to the data which reflects stops related to traffic enforcement and/or traffic violations. This indicator will be 1 if a stop originated as a traffic stop (including both stops where only a ticket was issued as well as stops that ultimately resulted in police action such as a search or arrest), involved an arrest for a traffic violation, and/or if the reason for the stop was Response to Crash, Observed Moving Violation, Observed Equipment Violation, or Traffic Violation.Between November 2021 and February 2022 several fields pertaining to items seized during searches of a person were not available for officers to use, leading to the data showing that no objects were seized pursuant to person searches during this time period. Finally, MPD is conducting on-going data audits on all data for thorough and complete information. For more information regarding police stops, please see: https://mpdc.dc.gov/stopdataFigures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

This monthly performance report provides triple zero (000) and other call summary data for Northern Territory Police, Fire and Emergency Services.

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The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from January 1, 2023 – December 31, 2024. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest. If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.” Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. Please note that the term property in this context refers to a person’s belongings and not a physical building. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date of birth and the date of the stop. There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors. On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS. Beginning January 1, 2023, fields pertaining to the bureau, division, unit, and PSA (if applicable) of the officers involved in events where a stop was conducted were added to the dataset. MPD’s Records Management System (RMS) captures all members associated with the event but cannot isolate which officer (if multiple) conducted the stop itself. Assignments are captured by cross-referencing officers’ CAD ID with MPD’s Timesheet Manager Application. These fields reflect the assignment of the officer issuing the Notice of Infraction (NOIs) and/or the responding officer(s), assisting officer(s), and/or arresting officer(s) (if an investigative stop) as of the end of the two-week pay period for January 1 – June 30, 2023 and as of the date of the stop for July 1, 2023 and forward. The values are comma-separated if multiple officers were listed in the report. For Stop Type = Harbor and Stop Type = Ticket Only, the officer assignment information will be in the NOI_Officer fields. For Stop Type = Ticket and Non-Ticket the officer assignments will be in both NOI Officer (for the officer that issued the NOI) and RMS_Officer fields (for any other officer involved in the event, which may also be the officer who issued the NOI). For Stop Type = Non-Ticket, the officer assignment information will be in the RMS_Officer fields. Null values in officer assignment fields reflect either Reserve Corps members, who’s assignments are not captured in the Timesheet Manager Application, or members who separated from MPD between the time of the stop and the time of the data extraction. Finally, MPD is conducting on-going data audits on all data for thorough and complete information. Figures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

This publication can be accessed here

CSV of NIBRS crime data for 2021 Aug-Sep.The file contains data for each of the precincts within St. Louis County, and all areas that the St. Louis County Police Department and the St. Louis County Park Rangers patrol. Additionally, the 2021 file contains data for the following police departments, and includes any municipalities that these departments patrol: Ballwin, Bel Nor, Bel Ridge, Bellefontaine Neighbors, Breckenridge Hills, Brentwood, Country Club Hills, Chesterfield, Clayton, Ellisville, Eureka, Frontenac, Ladue, Lakeshire, Moline Acres, Maplewood, Normandy (starting in October 2020), Olivette, Riverview, Richmond Heights, Saint Louis County, Shrewsbury, St. John, Sunset Hills, Town & Country, Velda City, and Woodson Terrace. The file does not contain data for municipalities that the above listed departments do not patrol, so a lack of data from other municipalities does not necessarily indicate a lack of crime.Included columns:OffenseName: NIBRS description of the offenseOffenseCategory: Property, Person, or Society crime based on definitions from the FBIReportNumber: departmental complaint numberReportingJuris: The jurisdiction that reported this data to the State of MissouriForJuris: The municipality the address is in. This will either be a municipality name, MetroLink, or Saint Louis County.Address: String address of the incident, addresses are redacted for certain incident typesLatitude: If address is redacted, coordinates will also be redactedLongitude: If address is redacted, coordinates will also be redactedDateTimeCallReceived: The Date/Time the police were made aware of the crimeOccurredDate: Date the crime occurred (data is filtered on this date)OccDOW: Day of the week the crime occurredOccMonth: Month in which crime occurredPremise: The premise of the crime (eg. residential, business, etc.)Zone: Also known as a cogis, the geographical area in which the crime occurredDistrict: Also known as precinct, not available for all jurisdictions.

Analysis of ‘Stanford Open Policing Project’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/faressayah/stanford-open-policing-project on 29 August 2021.

--- Dataset description provided by original source is as follows ---

Context

On a typical day in the United States, police officers make more than 50,000 traffic stops. Our team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Our goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

Content

This dataset includes 9 Mb of stop data from Rhode Island, covering all of 2013 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration

• Do men or women speed more often?
• Does gender affect who gets searched during a stop?
• During a search, how often is the driver frisked?
• Which year had the least number of stops?
• How does drug activity change by time of day?
• Do most stops occur at night?

Those all are question waiting for you to answer them, Good Luck😃

--- Original source retains full ownership of the source dataset ---

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This series comprises of Cabinet-in-Confidence records related to a specific Ministerial Portfolio of the Andrews Government created within a Department / Agency to assist

• in the preparation or tracking of a submission to Cabinet or a Cabinet Committee;
• in the development of advice to a Minister or departmental officer on any matter that has been or is intended to be considered by Cabinet or one of its Committees; or
• in the communication of outcomes of Cabinet or Cabinet Committee deliberations.

Cabinet-in-Confidence related papers include:

• Any original, draft, extract or copy of a document which relates to or refers to a matter that has been or is intended to be considered by Cabinet or one of its Committees
• Any background, issues, discussion or consultation papers relating to a matter that has been or is intended to be considered by Cabinet or one of its Committees

• Draft Cabinet or Cabinet Committee material and any proposed or actual attachment to Cabinet or Cabinet Committee submissions
• Ministerial briefing notes, memos or speaking notes
• Departmental briefing notes, memos, comments and discussion papers on Cabinet material, such as possible legislation proposals or terms of reference for a Parliamentary Committee of Inquiry
• Relevant legal opinions
• Correspondence identifying Cabinet-in-Confidence information
• Internal distribution lists for Cabinet-in-Confidence material
• Cabinet or Cabinet Committee memberships and meeting schedules.

This dataset contains counts of offences recorded by the NT Police, categorised by offence type, time period (month), location and (for assault offences) alcohol and domestic violence involvement. Certain types of offences show strong seasonal impacts and numbers show considerable monthly variation, particularly at the regional level.

From August 2016 to July 2021, the number of monitored foreign terrorist fighters (FTFs) in Italy increased. According to data, *** foreign fighters were monitored between August 2020 and July 2021.

According to the UN Security Council, FTFs are defined as “individuals who travel to a State other than their State of residence or nationality for the purpose of the perpetration, planning or preparation of, or participation in, terrorist acts or the providing or receiving of terrorist training, including in connection with armed conflict”.

Terrorism-related charges lead to the expulsions over the same period of ** individuals. Data show that the number of people expelled for terrorism-related charges decreased.

This publication can be accessed here

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), police services in Alberta, 1998 to 2024.

Background:
The Crime Survey for England and Wales (CSEW), previously known as the British Crime Survey (BCS), has been in existence since 1981. The survey traditionally asks a sole randomly selected adult, in a random sample of households, details pertaining to any instances where they, or the household, has been a victim of a crime in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). Most of the questionnaire is completed in a face-to-face interview in the respondent's home; these variables are contained within the non-victim form (NVF) data file. Since 2009, the survey has been extended to children aged 10-15 years old; one resident of that age range has also been selected at random from the household and asked about incidents where they have been a victim of crime, and other related topics. The first set of children's data, covering January-December 2009, had experimental status, and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main dataset. Further information may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.

Self-completion data:
A series of questions on drinking behaviour, drug use and intimate personal violence (including stalking and sexual victimisation) are administered to adults via a self-completion module which the respondent completes on a laptop computer. Children aged 10-15 years also complete a separate self-completion questionnaire. The questions are contained within the main questionnaire documents, but the data are not available with the main survey; they are available only under Secure Access conditions. Lower-level geographic variables are also available under Secure Access conditions to match to the survey.

History:
Up to 2001, the survey was conducted biennially. From April 2001, interviewing was carried out continually and reported on in financial year cycles and the crime reference period was altered to accommodate this change. The core sample size has increased from around 11,000 in the earlier cycles to over 40,000. Following the National Statistician's Review of Crime Statistics in June 2011 the collation and publication of Crime Statistics moved to the Office for National Statistics (ONS) from 1st April 2012, and the survey changed its name to the Crime Survey for England and Wales (CSEW) accordingly.

Scottish data:
The 1982 and 1988 BCS waves were also conducted in Scotland. The England and Wales data for 1982 and 1988 are held at the UKDA under SNs 1869 and 2706, but the Scottish data for these studies are held separately under SNs 4368 and 4599. Since 1993, separate Scottish Crime and Justice Surveys have been conducted, see the series web page for more details.

New methodology for capping the number of incidents from 2017-18
The CSEW datasets available from 2017-18 onward are based upon a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old data sets are not, comparability has been lost with previous years. More information can be found in the 2017-18 User Guide and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. ONS intend to publish all micro data back to 1981 with incident data based on the 98th percentile cap later in 2019.
Documentation:
Please see the documentation for the main Secure Access CSEW survey held under SN 7280.

Latest edition information:
For the eighth edition (August 2021), the CSEW 2019-20 geographic data have been added to the study.

‘span style=’font-family: & Avenir Next W01 " & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘>Ce guide de l’utilisateur contient des informations sur l’ONSPD, y compris:‘span style=’font-family: "Avenir Next W01 ", ", " & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘’’ répertoire content;‘span style=’font-family: "Avenir Next W01 ", & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘’span style=’font-family: "Avenir Next W01 ", & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘’ la méthodologie pour assigner des zones aux codes postaux;‘span style=’font-family: "Avenir Next W01 " & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘’ ‘span style=’font-family:" Avenir Next W01 ", & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;'> formats de données; ‘span style=’font-family: & Avenir Next W01 " & Avenir Next W00 ", & Avenir Next "Avenir, "Helvetica Neue "Helvetica, Arial, sans-serif; police-size:17px;‘’> qualité et limitations des données et detail des changements récents qui ont eu un impact sur les données. Diverses annexes et tableaux fournissent des informations plus détaillées à l’appui. (Taille du fichier — 291 KB)