"In 2015, The Washington Post began to log every fatal shooting by an on-duty police officer in the United States. In that time there have been more than 5,000 such shootings recorded by The Post. After Michael Brown, an unarmed Black man, was killed in 2014 by police in Ferguson, Mo., a Post investigation found that the FBI undercounted fatal police shootings by more than half. This is because reporting by police departments is voluntary and many departments fail to do so. The Washington Post’s data relies primarily on news accounts, social media postings, and police reports. Analysis of more than five years of data reveals that the number and circumstances of fatal shootings and the overall demographics of the victims have remained relatively constant..." SOURCE ==> Washington Post Article

For more information about this story

This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

2016 PoliceKillingUS DATASET
2017 PoliceKillingUS DATASET
2018 PoliceKillingUS DATASET
2019 PoliceKillingUS DATASET
2020 PoliceKillingUS DATASET

Features at the Dataset:

The file fatal-police-shootings-data.csv contains data about each fatal shooting in CSV format. The file can be downloaded at this URL. Each row has the following variables:

• id: a unique identifier for each victim
• name: the name of the victim
• date: the date of the fatal shooting in YYYY-MM-DD format
• manner_of_death: shot, shot and Tasered
• armed: indicates that the victim was armed with some sort of implement that a police officer believed could inflict harm
  • undetermined: it is not known whether or not the victim had a weapon
  • unknown: the victim was armed, but it is not known what the object was
  • unarmed: the victim was not armed
• age: the age of the victim
• gender: the gender of the victim. The Post identifies victims by the gender they identify with if reports indicate that it differs from their biological sex.
  • M: Male
  • F: Female
  • None: unknown
• race:
  • W: White, non-Hispanic
  • B: Black, non-Hispanic
  • A: Asian
  • N: Native American
  • H: Hispanic
  • O: Other
  • None: unknown
• city: the municipality where the fatal shooting took place. Note that in some cases this field may contain a county name if a more specific municipality is unavailable or unknown.
• state: two-letter postal code abbreviation
• signs of mental illness: News reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.
• threat_level: The threat_level column was used to flag incidents for the story by Amy Brittain in October 2015. http://www.washingtonpost.com/sf/investigative/2015/10/24/on-duty-under-fire/ As described in the story, the general criteria for the attack label was that there was the most direct and immediate threat to life. That would include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The other and undetermined categories represent all remaining cases. Other includes many incidents where officers or others faced significant threats.
• flee: News reports have indicated the victim was moving away from officers
  • Foot
  • Car
  • Not fleeing

The threat column and the fleeing column are not necessarily related. For example, there is an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer. Also, attacks represent a status immediately before fatal shots by police while fleeing could begin slightly earlier and involve a chase. - body_camera: News reports have indicated an officer w...

The objective of this study was to assess the role of traumatic exposures, operational and organizational stressors, and personal behaviors on law enforcement safety and wellness. The goal was to provide the necessary data to help researchers, Law Enforcement Agencies (LEAs), and policymakers design policies and programs to address risk factors for Law Enforcement Officers' (LEOs) wellness and safety outcomes. The project objectives were to identify profiles of LEAs who are using best practices in addressing officer safety and wellness (OSAW); determine the extent to which specific occupational, organizational, and personal stressors distinguish OSAW outcomes identify whether modifiable factors such as coping, social support, and healthy lifestyles moderate the relationship between stressors and OSAW outcomes; and investigate which LEA policies/programs have the potential to moderate OSAW outcomes.

Context

The Washington Post's database contains records of every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015. I did not create the data, all credits to The Washington Post. Here is the link: Washington Post link

Content

The data included all the police shooting in the US from January 1, 2015, to September 7, 2020.

Inspiration

Have fun with the data and try to find any insights!

This hosted feature layer contains five HPD NIBRS crime yearly cases from 2020 to 2024. This GIS dataset is based on NIBRS data published by Houston Police Department (HPD). The original source data can be found on HPD's Monthly Crime Data By Street And Police Beat webpage "https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm"This GIS dataset was processed and published by Houston Information Technology Services (HITS). National Incident-Based Reporting System (NIBRS) is an incident-based reporting system used by law enforcement agencies in the United States for collecting and reporting data on crimes. Local, state and federal agencies generate NIBRS data from their records management systems. Data is collected on every incident and arrest in the Group A offense category. These Group A offenses include 52 NIBRS classes in three main categories (Person, Property, and Society.) Specific facts about these offenses are gathered and reported to NIBRS. In addition to the Group A offenses, 10 Group B offenses are reported with only the arrest information. Disclaimer: This GIS dataset is prepared and made available for general reference purposes only and should not be used, or relied upon for specific applications, without independent verification. The City of Houston neither represents, nor warrants COHGIS data accuracy, or completeness, nor will the City of Houston accept liability of any kind in conjunction with its use. COHGIS information is in the public domain and may be copied without permission; citation of the source is appreciated.

This GIS dataset summarized Houston Police Department NIBRS crime reports by the Houston area 8-digit Open Location Code grid. This Houston area Open Location Code (OLC) grid system is based on latitudes and longitudes in WGS84 coordinates. Each 6-digit block has a size of 3 arc-minutes by 3 arc-minutes (approximately 3.41 miles). Each 8-digit block has a size of 3 arc-minutes by 3 arc-minutes (approximately 3.41 miles). Each 8-digit block has a size of 9 arc-seconds by 9 arc-seconds (approximately 900 feet). This grid system is used for summary statistics. This GIS dataset is based on NIBRS data published by Houston Police Department (HPD). The original source data can be found on HPD's Monthly Crime Data By Street And Police Beat webpage "https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm"This GIS dataset was processed and published by Houston Information Technology Services (HITS). National Incident-Based Reporting System (NIBRS) is an incident-based reporting system used by law enforcement agencies in the United States for collecting and reporting data on crimes. Local, state and federal agencies generate NIBRS data from their records management systems. Data is collected on every incident and arrest in the Group A offense category. These Group A offenses include 52 NIBRS classes in three main categories (Person, Property, and Society.) Specific facts about these offenses are gathered and reported to NIBRS. In addition to the Group A offenses, 10 Group B offenses are reported with only the arrest information. Disclaimer: This GIS dataset is prepared and made available for general reference purposes only and should not be used, or relied upon for specific applications, without independent verification. The City of Houston neither represents, nor warrants COHGIS data accuracy, or completeness, nor will the City of Houston accept liability of any kind in conjunction with its use. COHGIS information is in the public domain and may be copied without permission; citation of the source is appreciated.***** List of Field Names *****NIBRS_Class | Description | NIBRS_Group | Group | Field_Name09A | Murder, non-negligent | Group A - Person | AI | HPD_NIBRS_AI_09A_CNT_202009B | Negligent manslaughter | Group A - Person | AI | HPD_NIBRS_AI_09B_CNT_202009C | Justifiable homicide | Not a Crime | N | HPD_NIBRS_N_09C_CNT_2020100 | Kidnapping, abduction | Group A - Person | AI | HPD_NIBRS_AI_100_CNT_202011A | Forcible rape | Group A - Person | AI | HPD_NIBRS_AI_11A_CNT_202011B | Forcible sodomy | Group A - Person | AI | HPD_NIBRS_AI_11B_CNT_202011C | Sexual assault with an object | Group A - Person | AI | HPD_NIBRS_AI_11C_CNT_202011D | Forcible fondling | Group A - Person | AI | HPD_NIBRS_AI_11D_CNT_2020120 | Robbery | Group A - Property | AP | HPD_NIBRS_AP_120_CNT_202013A | Aggravated Assault | Group A - Person | AI | HPD_NIBRS_AI_13A_CNT_202013B | Simple assault | Group A - Person | AI | HPD_NIBRS_AI_13B_CNT_202013C | Intimidation | Group A - Person | AI | HPD_NIBRS_AI_13C_CNT_2020200 | Arson | Group A - Property | AP | HPD_NIBRS_AP_200_CNT_2020210 | Extortion, Blackmail | Group A - Property | AP | HPD_NIBRS_AP_210_CNT_2020220 | Burglary, Breaking and Entering | Group A - Property | AP | HPD_NIBRS_AP_220_CNT_202023A | Pocket-picking | Group A - Property | AP | HPD_NIBRS_AP_23A_CNT_202023B | Purse-snatching | Group A - Property | AP | HPD_NIBRS_AP_23B_CNT_202023C | Shoplifting | Group A - Property | AP | HPD_NIBRS_AP_23C_CNT_202023D | Theft from building | Group A - Property | AP | HPD_NIBRS_AP_23D_CNT_202023E | From coin-operated machine or device | Group A - Property | AP | HPD_NIBRS_AP_23E_CNT_202023F | Theft from motor vehicle | Group A - Property | AP | HPD_NIBRS_AP_23F_CNT_202023G | Theft of motor vehicle parts or accessory | Group A - Property | AP | HPD_NIBRS_AP_23G_CNT_202023H | All other larceny | Group A - Property | AP | HPD_NIBRS_AP_23H_CNT_2020240 | Motor vehicle theft | Group A - Property | AP | HPD_NIBRS_AP_240_CNT_2020250 | Counterfeiting, forgery | Group A - Property | AP | HPD_NIBRS_AP_250_CNT_202026A | False pretenses, swindle | Group A - Property | AP | HPD_NIBRS_AP_26A_CNT_202026B | Credit card, ATM fraud | Group A - Property | AP | HPD_NIBRS_AP_26B_CNT_202026C | Impersonation | Group A - Property | AP | HPD_NIBRS_AP_26C_CNT_202026D | Welfare fraud | Group A - Property | AP | HPD_NIBRS_AP_26D_CNT_202026E | Wire fraud | Group A - Property | AP | HPD_NIBRS_AP_26E_CNT_202026F | Identify theft | Group A - Property | AP | HPD_NIBRS_AP_26F_CNT_202026G | Hacking/Computer Invasion | Group A - Property | AP | HPD_NIBRS_AP_26G_CNT_2020270 | Embezzlement | Group A - Property | AP | HPD_NIBRS_AP_270_CNT_2020280 | Stolen property offenses | Group A - Property | AP | HPD_NIBRS_AP_280_CNT_2020290 | Destruction, damage, vandalism | Group A - Property | AP | HPD_NIBRS_AP_290_CNT_202035A | Drug, narcotic violations | Group A - Society | AS | HPD_NIBRS_AS_35A_CNT_202035B | Drug equipment violations | Group A - Society | AS | HPD_NIBRS_AS_35B_CNT_202036A | Incest | Group A - Person | AI | HPD_NIBRS_AI_36A_CNT_202036B | Statutory rape | Group A - Person | AI | HPD_NIBRS_AI_36B_CNT_2020370 | Pornographs, obscene material | Group A - Society | AS | HPD_NIBRS_AS_370_CNT_202039A | Betting/wagering | Group A - Society | AS | HPD_NIBRS_AS_39A_CNT_202039B | Promoting gambling | Group A - Society | AS | HPD_NIBRS_AS_39B_CNT_202039C | Gambling equipment violations | Group A - Society | AS | HPD_NIBRS_AS_39C_CNT_202040A | Prostitution | Group A - Society | AS | HPD_NIBRS_AS_40A_CNT_202040B | Assisting or promoting prostitution | Group A - Society | AS | HPD_NIBRS_AS_40B_CNT_202040C | Purchasing prostitution | Group A - Society | AS | HPD_NIBRS_AS_40C_CNT_2020510 | Bribery | Group A - Property | AP | HPD_NIBRS_AP_510_CNT_2020520 | Weapon law violations | Group A - Society | AS | HPD_NIBRS_AS_520_CNT_202064A | Human Trafficking/Commercial Sex Act | Group A - Person | AI | HPD_NIBRS_AI_64A_CNT_202064B | Human Trafficking/Involuntary Servitude | Group A - Person | AI | HPD_NIBRS_AI_64B_CNT_2020720 | Animal Cruelty | Group A - Society | AS | HPD_NIBRS_AS_720_CNT_202090A | Bad checks | Group B | B | HPD_NIBRS_B_90A_CNT_202090B | Curfew, loitering, vagrancy violations | Group B | B | HPD_NIBRS_B_90B_CNT_202090C | Disorderly conduct | Group B | B | HPD_NIBRS_B_90C_CNT_202090D | Driving under the influence | Group B | B | HPD_NIBRS_B_90D_CNT_202090E | Drunkenness | Group B | B | HPD_NIBRS_B_90E_CNT_202090F | Family offenses, no violence | Group B | B | HPD_NIBRS_B_90F_CNT_202090G | Liquor law violations | Group B | B | HPD_NIBRS_B_90G_CNT_202090H | Peeping tom | Group B | B | HPD_NIBRS_B_90H_CNT_202090I | Runaway | Group B | B | HPD_NIBRS_B_90I_CNT_202090J | Trespass of real property | Group B | B | HPD_NIBRS_B_90J_CNT_202090Z | All other offenses | Group B | B | HPD_NIBRS_B_90Z_CNT_2020

Title: Pew Research Center – Wave 69 American Trends Panel Fieldwork Dates: June 16–22, 2020 Sample Size: N = 4,708 U.S. adults Mode: Web-based survey (English and Spanish) Purpose: This wave of the ATP explores public opinion on coronavirus concerns, political traits, policing reforms, and census participation. It includes longitudinal variables from previous waves (W59 and W64) and supports multiple Pew Research reports released between June and July 2020. The dataset includes multiple weighting schemes for full sample analysis, longitudinal tracking, and census module calibration. It also features a battleground state classification variable for electoral analysis. 🏷️ Tags - Pew Research Center - American Trends Panel - COVID-19 - Public Opinion - Census Participation - Police Reform - Political Traits - Battleground States - Longitudinal Survey - Web Survey - Social Media Use - Survey Weights - Panel Study - June 2020 - U.S. Politics 📝 Notes 📊 Weighting Variables - WEIGHT_W69: Full sample weight for general analysis. - WEIGHT_W59_W69: Longitudinal weight for panelists who responded to Waves 59 and 69. - WEIGHT_W64_W69: Longitudinal weight for panelists who responded to Waves 64 and 69. - WEIGHT_W69_CENSUS: Census module weight adjusted to match estimated 2020 census response rate (targeted at 74%). 🧠 Key Variables - SNSUSE: Non-internet households coded as not using social media. - COVID_COMFORT_COMB_W69: Combines comfort levels from two COVID-related questions. - BATTLE_NARROW_W69: Classifies respondents by battleground state status using SPSS syntax. Categories include: - 1 = Solid Democrat - 2 = Lean/Likely Democrat - 3 = Toss-up/Battleground - 4 = Lean/Likely Republican - 5 = Solid Republican 🔁 Longitudinal Variables from Prior Waves - POL1DT_W59, POL1DTSTR_W59, POL1DT_W64, POL1DTSTR_W64 - DEMFIELD_W59, TRUMPDEM2020_W59, DEM1_CODE_FINAL_W59 🔐 Privacy Measures - State-level data used for classification, but actual state identifiers are excluded. - ZIP codes, counties, and phone numbers removed to protect respondent confidentiality. 📚 Related Reports - Republicans, Democrats Move Further Apart on Coronavirus - Public’s Mood Turns Grim; Trump Trails Biden - Majority Favors Power to Sue Police Officers - Census Participation and Doorstep Reluctance Let me know if you’d like help building a codebook, visualizing trends, or preparing this for statistical analysis.

The Death in Custody Reporting Act of 2013 (DICRA) requires the head of each federal law enforcement agency to submit to the U.S. attorney general, information about the death of any person who is detained, under arrest, or in the process of being arrested by a federal law enforcement officer (or by a state or local law enforcement officer while participating in a federal law enforcement operation, task force, or other capacity) being transported to, incarcerated at, or detained at any facility (including immigration or juvenile facilities) pursuant to a contract with a federal law enforcement agency, state or local government facility used by a federal law enforcement agency, or federal correctional or pre-trial detention facility located within the United States (Death in Custody Reporting Act of 2013, P.L. 113-242). The Bureau of Justice Statistics (BJS) created the Federal Deaths in Custody Reporting Program (FDCRP) to collect the data required of federal law enforcement agencies. Federal law enforcement agencies are surveyed on an annual basis about deaths that fall under the scope of DICRA. This data collection includes the 2020 Arrest-Related Death Incident Report (CJ-13A) data and the 2020 Detention/Incarceration Incident Report (CJ-13B) data.

● This dataset was provided by: Seattle Police Department ● Dataset category: Public Safety ● Dataset created date: February 14, 2020

Acknowledgement

This data has been taken from Seattle city's official website

The Police-Public Contact Survey (PPCS) provides detailed information on the nature and characteristics of face-to-face contacts between police and the public, including the reason for and outcome of the contact and the respondent's satisfaction with the contact. The data can be used to estimate the likelihood of different types of contact for residents with different demographic characteristics, including contacts involving the use of nonfatal force by police. The PPCS is used to collect data from a nationally representative sample of U.S. residents age 16 or older as a supplement to the National Crime Victimization Survey. To date, the PPCS has been conducted ten times by BJS: 1. 1996. Described in the BJS publication Police Use of Force: Collection of National Data (NCJ 165040). 2. 1999. Described in Contacts between Police and the Public: Findings from the 1999 National Survey (NCJ 184957). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 1999: UNITED STATES. 3. 2002. Described in Contacts between Police and the Public: Findings from the 2002 National Survey (NCJ 207845). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2002: UNITED STATES. 4. 2005. Described in the BJS publication Contacts between Police and the Public, 2005 (NCJ 215243). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2005: UNITED STATES. 5. 2008. Described in the BJS publication Contacts between Police and the Public, 2008 (NCJ 234599). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2008 (ICPSR 32022). 6. 2011. Split sample design due to instrument changes. New instrument findings described in two publications: Police Behavior During Traffic and Street Stops, 2011 (NCJ 242937) and Requests for Police Assistance, 2011 (NCJ 242938). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2011 (ICPSR 34276). 7. 2015. Described in the BJS publication Contacts between Police and Public, 2015 (NCJ 251145). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2015 (ICPSR 36653). 8. 2018. Described in the BJS publication Contacts between Police and Public, 2018. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2018 (ICPSR 37916). 9. 2020. Described in the BJS publication Contacts between Police and Public, 2020. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2020 (ICPSR 38320). 10. 2022. Described in the BJS publication Contacts between Police and Public, 2022. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2022 (ICPSR 38872).

This dataset is for RMS Crime Incidents for 2020. For the comprehensive dataset which includes all records please refer to the RMS Crime Incidents dataset. The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in the 2020 calendar year. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows) details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

This GIS dataset summarized Houston Police Department NIBRS crime reports by the City of Houston Super Neighborhood boundaries. This GIS dataset is based on NIBRS data published by Houston Police Department (HPD). The original source data can be found on HPD's Monthly Crime Data By Street And Police Beat webpage "https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm"This GIS dataset was processed and published by Houston Information Technology Services (HITS). National Incident-Based Reporting System (NIBRS) is an incident-based reporting system used by law enforcement agencies in the United States for collecting and reporting data on crimes. Local, state and federal agencies generate NIBRS data from their records management systems. Data is collected on every incident and arrest in the Group A offense category. These Group A offenses include 52 NIBRS classes in three main categories (Person, Property, and Society.) Specific facts about these offenses are gathered and reported to NIBRS. In addition to the Group A offenses, 10 Group B offenses are reported with only the arrest information. Disclaimer: This GIS dataset is prepared and made available for general reference purposes only and should not be used, or relied upon for specific applications, without independent verification. The City of Houston neither represents, nor warrants COHGIS data accuracy, or completeness, nor will the City of Houston accept liability of any kind in conjunction with its use. COHGIS information is in the public domain and may be copied without permission; citation of the source is appreciated.***** List of Field Names *****NIBRS_Class | Description | NIBRS_Group | Group | Field_Name09A | Murder, non-negligent | Group A - Person | AI | HPD_NIBRS_AI_09A_CNT_202009B | Negligent manslaughter | Group A - Person | AI | HPD_NIBRS_AI_09B_CNT_202009C | Justifiable homicide | Not a Crime | N | HPD_NIBRS_N_09C_CNT_2020100 | Kidnapping, abduction | Group A - Person | AI | HPD_NIBRS_AI_100_CNT_202011A | Forcible rape | Group A - Person | AI | HPD_NIBRS_AI_11A_CNT_202011B | Forcible sodomy | Group A - Person | AI | HPD_NIBRS_AI_11B_CNT_202011C | Sexual assault with an object | Group A - Person | AI | HPD_NIBRS_AI_11C_CNT_202011D | Forcible fondling | Group A - Person | AI | HPD_NIBRS_AI_11D_CNT_2020120 | Robbery | Group A - Property | AP | HPD_NIBRS_AP_120_CNT_202013A | Aggravated Assault | Group A - Person | AI | HPD_NIBRS_AI_13A_CNT_202013B | Simple assault | Group A - Person | AI | HPD_NIBRS_AI_13B_CNT_202013C | Intimidation | Group A - Person | AI | HPD_NIBRS_AI_13C_CNT_2020200 | Arson | Group A - Property | AP | HPD_NIBRS_AP_200_CNT_2020210 | Extortion, Blackmail | Group A - Property | AP | HPD_NIBRS_AP_210_CNT_2020220 | Burglary, Breaking and Entering | Group A - Property | AP | HPD_NIBRS_AP_220_CNT_202023A | Pocket-picking | Group A - Property | AP | HPD_NIBRS_AP_23A_CNT_202023B | Purse-snatching | Group A - Property | AP | HPD_NIBRS_AP_23B_CNT_202023C | Shoplifting | Group A - Property | AP | HPD_NIBRS_AP_23C_CNT_202023D | Theft from building | Group A - Property | AP | HPD_NIBRS_AP_23D_CNT_202023E | From coin-operated machine or device | Group A - Property | AP | HPD_NIBRS_AP_23E_CNT_202023F | Theft from motor vehicle | Group A - Property | AP | HPD_NIBRS_AP_23F_CNT_202023G | Theft of motor vehicle parts or accessory | Group A - Property | AP | HPD_NIBRS_AP_23G_CNT_202023H | All other larceny | Group A - Property | AP | HPD_NIBRS_AP_23H_CNT_2020240 | Motor vehicle theft | Group A - Property | AP | HPD_NIBRS_AP_240_CNT_2020250 | Counterfeiting, forgery | Group A - Property | AP | HPD_NIBRS_AP_250_CNT_202026A | False pretenses, swindle | Group A - Property | AP | HPD_NIBRS_AP_26A_CNT_202026B | Credit card, ATM fraud | Group A - Property | AP | HPD_NIBRS_AP_26B_CNT_202026C | Impersonation | Group A - Property | AP | HPD_NIBRS_AP_26C_CNT_202026D | Welfare fraud | Group A - Property | AP | HPD_NIBRS_AP_26D_CNT_202026E | Wire fraud | Group A - Property | AP | HPD_NIBRS_AP_26E_CNT_202026F | Identify theft | Group A - Property | AP | HPD_NIBRS_AP_26F_CNT_202026G | Hacking/Computer Invasion | Group A - Property | AP | HPD_NIBRS_AP_26G_CNT_2020270 | Embezzlement | Group A - Property | AP | HPD_NIBRS_AP_270_CNT_2020280 | Stolen property offenses | Group A - Property | AP | HPD_NIBRS_AP_280_CNT_2020290 | Destruction, damage, vandalism | Group A - Property | AP | HPD_NIBRS_AP_290_CNT_202035A | Drug, narcotic violations | Group A - Society | AS | HPD_NIBRS_AS_35A_CNT_202035B | Drug equipment violations | Group A - Society | AS | HPD_NIBRS_AS_35B_CNT_202036A | Incest | Group A - Person | AI | HPD_NIBRS_AI_36A_CNT_202036B | Statutory rape | Group A - Person | AI | HPD_NIBRS_AI_36B_CNT_2020370 | Pornographs, obscene material | Group A - Society | AS | HPD_NIBRS_AS_370_CNT_202039A | Betting/wagering | Group A - Society | AS | HPD_NIBRS_AS_39A_CNT_202039B | Promoting gambling | Group A - Society | AS | HPD_NIBRS_AS_39B_CNT_202039C | Gambling equipment violations | Group A - Society | AS | HPD_NIBRS_AS_39C_CNT_202040A | Prostitution | Group A - Society | AS | HPD_NIBRS_AS_40A_CNT_202040B | Assisting or promoting prostitution | Group A - Society | AS | HPD_NIBRS_AS_40B_CNT_202040C | Purchasing prostitution | Group A - Society | AS | HPD_NIBRS_AS_40C_CNT_2020510 | Bribery | Group A - Property | AP | HPD_NIBRS_AP_510_CNT_2020520 | Weapon law violations | Group A - Society | AS | HPD_NIBRS_AS_520_CNT_202064A | Human Trafficking/Commercial Sex Act | Group A - Person | AI | HPD_NIBRS_AI_64A_CNT_202064B | Human Trafficking/Involuntary Servitude | Group A - Person | AI | HPD_NIBRS_AI_64B_CNT_2020720 | Animal Cruelty | Group A - Society | AS | HPD_NIBRS_AS_720_CNT_202090A | Bad checks | Group B | B | HPD_NIBRS_B_90A_CNT_202090B | Curfew, loitering, vagrancy violations | Group B | B | HPD_NIBRS_B_90B_CNT_202090C | Disorderly conduct | Group B | B | HPD_NIBRS_B_90C_CNT_202090D | Driving under the influence | Group B | B | HPD_NIBRS_B_90D_CNT_202090E | Drunkenness | Group B | B | HPD_NIBRS_B_90E_CNT_202090F | Family offenses, no violence | Group B | B | HPD_NIBRS_B_90F_CNT_202090G | Liquor law violations | Group B | B | HPD_NIBRS_B_90G_CNT_202090H | Peeping tom | Group B | B | HPD_NIBRS_B_90H_CNT_202090I | Runaway | Group B | B | HPD_NIBRS_B_90I_CNT_202090J | Trespass of real property | Group B | B | HPD_NIBRS_B_90J_CNT_202090Z | All other offenses | Group B | B | HPD_NIBRS_B_90Z_CNT_2020

Sadly, the trend of fatal police shootings in the United States seems to only be increasing, with a total 1,173 civilians having been shot, 248 of whom were Black, as of December 2024. In 2023, there were 1,164 fatal police shootings. Additionally, the rate of fatal police shootings among Black Americans was much higher than that for any other ethnicity, standing at 6.1 fatal shootings per million of the population per year between 2015 and 2024. Police brutality in the U.S. In recent years, particularly since the fatal shooting of Michael Brown in Ferguson, Missouri in 2014, police brutality has become a hot button issue in the United States. The number of homicides committed by police in the United States is often compared to those in countries such as England, where the number is significantly lower. Black Lives Matter The Black Lives Matter Movement, formed in 2013, has been a vocal part of the movement against police brutality in the U.S. by organizing “die-ins”, marches, and demonstrations in response to the killings of black men and women by police. While Black Lives Matter has become a controversial movement within the U.S., it has brought more attention to the number and frequency of police shootings of civilians.

Each record is an administrative notice of violation (ANOV), a citation issued for violating a Municipal Code of Chicago ordinance, issued by a Chicago Police Department (CPD) member. This dataset was used by CPD analysts to create an assessment report in support of a consent decree executed between the Office of the Attorney General of the State of Illinois (OAG) and the City of Chicago. The CPD is currently engaged in a reform process, as governed by that consent decree. A consent decree is a written settlement resolving a legal dispute. The OAG/City of Chicago consent decree includes detailed requirements organized into 11 core areas (e.g., use of force, accountability and transparency), collectively designed to create organizational change within CPD. To review the consent decree and learn more about the process, see the City of Chicago’s police reform webpage: https://www.chicago.gov/city/en/sites/police-reform/home/consent-decree.html This dataset is made available to the public pursuant to consent decree requirements described in Paragraph 79 and Paragraph 80. Paragraph 79 of the consent decree states that: By April 1, 2020, and every year thereafter, CPD will conduct an assessment of the relative frequency of all misdemeanor arrests and administrative notices of violation (“ANOVs”) effectuated by CPD members of persons in specific demographic categories, including race and gender. Then, the last sentence of Paragraph 80 states that: Upon completion of the assessment, CPD will publish the underlying data, excluding personal identifying information (e.g., name, address, contact information), via a publicly-accessible, web-based data platform. This dataset was used by CPD analysts to create an assessment report pursuant to Paragraph 79. The report was designed to achieve compliance with Paragraph 79. Each record in the dataset shows information about an ANOV issued by a CPD member. An ANOV is a citation issued for violating a Municipal Code of Chicago ordinance. ANOV’s are adjudicated by the City of Chicago Department of Administrative Hearings (DOAH). ANOV data are owned and housed by the Department of Administrative Hearings. CPD receives a daily data extraction from the DOAH data system, whereupon ANOV data is ingested into the CPD data system. To request the aforementioned report contact the CPD Freedom of Information Act Section at https://home.chicagopolice.org/information/freedom-of-information-act-foia.. NOV NUMBER is the record identifier for the dataset. Each record in the dataset is a unique, unduplicated citation. Users interested in learning more about how CPD handles ANOV citations can review the current policy, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). CPD Special Order S04-22, entitled “Municipal Administrative Hearings”, provides guidelines for CPD members when issuing an ANOV.

About this dataset

The Washington Post compiled a dataset of every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

In 2015, The Post began tracking more than a dozen details about each killing by culling local news reports, law enforcement websites and social media and by monitoring independent databases such as Killed by Police and Fatal Encounters. The available features are: - Race of the deceased; - Circumstances of the shooting; - Whether the person was armed; - Whether the victim was experiencing a mental-health crisis; - Among others.

In 2016, The Post is gathering additional information about each fatal shooting that occurs this year and is filing open-records requests with departments. More than a dozen additional details are being collected about officers in each shooting.

The Post is documenting only those shootings in which a police officer, in the line of duty, shot and killed a civilian — the circumstances that most closely parallel the 2014 killing of Michael Brown in Ferguson, Mo., which began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths.

The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. In 2015, The Post documented more than two times more fatal shootings by police than had been recorded by the FBI. Last year, the FBI announced plans to overhaul how it tracks fatal police encounters.

How to use this dataset

• Understanding US Police Shootings by Curtis Thompson
• Dataset Questions and Suggestions
• More datasets

Acknowledgements

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

BibTeX

@misc{wapo-police-shootings-bot , author = {The Washington Post}, title = {data-police-shootings}, month = jan, year = 2015, publisher = {Github}, url = {https://github.com/washingtonpost/data-police-shootings} }

License

CC BY NC SA 4.0

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For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 13 release notes:Adds 2023-2024 dataVersion 12 release notes:Adds 2022 dataVersion 11 release notes:Adds 2021 data.Version 10 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last arson data they release. Changes .rda file to .rds.Version 9 release notes:Changes release notes description, does not change data.Version 8 release notes:Adds 2019 data.Note that the number of months missing variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the column_2_type variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 7 release notes:Adds a last_month_reported column which says which month was reported last. This is actually how the FBI defines number_of_months_reported so is a more accurate representation of that. Removes the number_of_months_reported variable as the name is misleading. You should use the last_month_reported or the number_of_months_missing (see below) variable instead.Adds a number_of_months_missing in the annual data which is the sum of the number of times that the agency reports "missing" data (i.e. did not report that month) that month in the card_2_type variable or reports NA in that variable. Please note that this variable is not perfect and sometimes an agency does not report data but this variable does not say it is missing. Therefore, this variable will not be perfectly accurate.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 4 release notes: Adds 1979-2000, 2006, and 2017 dataAdds agencies that reported 0 months.Adds monthly data.All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Removed data from Chattahoochee Hills (ORI = "GA06059") from 2016 data. In 2016, that agency reported about 28 times as many vehicle arsons as their population (Total mobile arsons = 77762, population = 2754.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. This Arson data set is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about arsons reported in the United States. The information is the number of arsons reported, to have actually occurred, to not have occurred ("unfounded"), cleared by arrest of at least one arsoning, cleared by arrest where all offenders are under the age of 18, and the cost of the arson. This is done for a number of different arson location categories such as community building, residence, vehicle, and industrial/manufacturing structure. The yearly data sets here combine data from the years 1979-2018 into a single file for each group of crimes. Each monthly file is only a single year as my laptop can't handle combining all the years together. These files are quite large and may take some time to load. I also added state, county, and place FIPS code from the LEAIC (crosswalk).A small number of agencies had some months with clearly incorrect data. I changed the incorrect columns to NA and left the other columns unchanged for that agency. The following are data problems that I fixed - there are still likely issues remaining in the data so make sure to check yourself before running analyses. Oneida, New York (ORI = NY03200) had multiple years that reported single arsons costing over $700 million. I deleted this agency from all years of data.In January 1989 Union, North Carolina (ORI = NC09000) reported 30,000 arsons in uninhabited single occupancy buildings and none any other months. In December 1991 Gadsden, Florida (ORI = FL02000) reported that a single arson at a community/public building caused $99,999,999 in damages (the maximum possible).In April 2017 St. Paul, Minnesota (ORI = MN06209) reported 73,400 arsons in uninhabited storage buildings and 10,000 arsons in uninhabited community/public buildings and one or fewer every other mon

This dataset was downloaded from the United States government's open data website, Data.gov. It has all the crimes reported and recorded by Los Angeles Police Department (LAPD).

Here is the link to this specific dataset Crime Data

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 10 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last arson data they release. Changes .rda file to .rds.Version 9 release notes:Changes release notes description, does not change data.Version 8 release notes:Adds 2019 data.Note that the number of months missing variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the column_2_type variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 7 release notes:Adds a last_month_reported column which says which month was reported last. This is actually how the FBI defines number_of_months_reported so is a more accurate representation of that. Removes the number_of_months_reported variable as the name is misleading. You should use the last_month_reported or the number_of_months_missing (see below) variable instead.Adds a number_of_months_missing in the annual data which is the sum of the number of times that the agency reports "missing" data (i.e. did not report that month) that month in the card_2_type variable or reports NA in that variable. Please note that this variable is not perfect and sometimes an agency does not report data but this variable does not say it is missing. Therefore, this variable will not be perfectly accurate.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 4 release notes: Adds 1979-2000, 2006, and 2017 dataAdds agencies that reported 0 months.Adds monthly data.All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Removed data from Chattahoochee Hills (ORI = "GA06059") from 2016 data. In 2016, that agency reported about 28 times as many vehicle arsons as their population (Total mobile arsons = 77762, population = 2754.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. This Arson data set is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about arsons reported in the United States. The information is the number of arsons reported, to have actually occurred, to not have occurred ("unfounded"), cleared by arrest of at least one arsoning, cleared by arrest where all offenders are under the age of 18, and the cost of the arson. This is done for a number of different arson location categories such as community building, residence, vehicle, and industrial/manufacturing structure. The yearly data sets here combine data from the years 1979-2018 into a single file for each group of crimes. Each monthly file is only a single year as my laptop can't handle combining all the years together. These files are quite large and may take some time to load. I also added state, county, and place FIPS code from the LEAIC (crosswalk).A small number of agencies had some months with clearly incorrect data. I changed the incorrect columns to NA and left the other columns unchanged for that agency. The following are data problems that I fixed - there are still likely issues remaining in the data so make sure to check yourself before running analyses. Oneida, New York (ORI = NY03200) had multiple years that reported single arsons costing over $700 million. I deleted this agency from all years of data.In January 1989 Union, North Carolina (ORI = NC09000) reported 30,000 arsons in uninhabited single occupancy buildings and none any other months. In December 1991 Gadsden, Florida (ORI = FL02000) reported that a single arson at a community/public building caused $99,999,999 in damages (the maximum possible).In April 2017 St. Paul, Minnesota (ORI = MN06209) reported 73,400 arsons in uninhabited storage buildings and 10,000 arsons in uninhabited community/public buildings and one or fewer every other month.When an arson is determined to be unfounded the estimated damage from that arson is added as negative to zero out the pr

***Starting on March 7th, 2024, the Los Angeles Police Department (LAPD) will adopt a new Records Management System for reporting crimes and arrests. This new system is being implemented to comply with the FBI's mandate to collect NIBRS-only data (NIBRS — FBI - https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs). During this transition, users will temporarily see only incidents reported in the retiring system. However, the LAPD is actively working on generating new NIBRS datasets to ensure a smoother and more efficient reporting system. *** **Update 1/18/2024 - LAPD is facing issues with posting the Crime data, but we are taking immediate action to resolve the problem. We understand the importance of providing reliable and up-to-date information and are committed to delivering it. As we work through the issues, we have temporarily reduced our updates from weekly to bi-weekly to ensure that we provide accurate information. Our team is actively working to identify and resolve these issues promptly. We apologize for any inconvenience this may cause and appreciate your understanding. Rest assured, we are doing everything we can to fix the problem and get back to providing weekly updates as soon as possible. ** This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

Export DataAccess API NSW Features of Interest Category - Emergency Service Facilities multiCRS MultiCRS service - supporting requests in multiple Coordinate Reference Systems - Information Sheet A new series of ‘multiCRS’ web services have been published to support GDA2020. These new ‘multiCRS’ services:· have a spatial reference of GDA2020· support alignment with GDA2020, GDA94 or [WGS 84-aligned-to-GDA2020] GIS environments,using built-in server-side transformations:o GDA94 < NTv2-CPD > GDA2020o GDA94 < NTv2-CPD > WGS 84 aligned to GDA2020o GDA2020 < NULL > WGS 84 aligned to GDA2020 Note: ESRI software will automatically align by transforming from the sourceSpatialReference (GDA94). Other software may need to set client-side transformations from the SpatialReference (GDA2020). Note: Client-side transformation(s) can be used to over-ride these default transformations.The original [WGS 84-aligned-to-GDA2020] is still available, without the ‘multiCRS’ suffix. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt this new multiCRS functionality. Metadata Portal Metadata InformationContent TitleNSW Features of Interest - Emergency Service Facilities multiCRSContent TypeHosted Feature LayerDescriptionThe Features of Interest – Emergency Services is a point feature dataset that represents the location of Emergency Services - related datasets such as Police, Fire and SES Stations, which is crucial to delivery of Emergency Services to NSW.The Features of Interest category Emergency Services is part of the Building Complex feature class and is represented as a community facility. Features that make up the NSW Features of interest Category - Emergency Services include:Fire Station – Urban (Fire and Rescue NSW) - The facility in which firefighting vehicles and equipment are stationed or intended to be stationed to serve urban communities. This point feature dataset is part of the Features of interest Category. Fire station (located in an urban area) data points are positioned within the cadastral parcel in which they are located. Fire Station - Bush (NSW Rural Fire Service) - The facility in which firefighting vehicles and equipment are stationed or intended to be stationed to serve rural communities. This point feature dataset is part of the Features of interest Category. Fire station (located in bushland) data points are positioned within the cadastral parcel in which they are located. Police Station - An office of the local police force, which may or may not have associated lock-up. This point feature dataset is part of the Features of interest Category. Police stations data points are positioned within the cadastral parcel in which they are located. State Emergency Service (SES) - A facility for the operations of the State Emergency Services (SES). This point feature dataset is part of the Features of interest Category. SES facility data points are positioned within the cadastral parcel in which they are located. These point feature datasets are part of the Features of Interest Category data and all the Emergency Services - related data centroids are positioned within the cadastral parcel in which they are located. These features do not fit within one of the ten foundation spatial data themes and are therefore classified as a category. They have historically been captured by Spatial Services as part of the NSW topographic mapping program and therefore warrant inclusion.Initial Publication Date05/02/2020Data Currency01/01/3000Data Update FrequencyDailyContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.au.Data Theme, Classification or Relationship to other DatasetsFeatures of Interest Category of the Foundation Spatial Data Framework (FSDF)AccuracyThis dataset was captured by utilising the best available source at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)OtherWGS84 Equivalent ToGDA2020Spatial ExtentFull stateContent LineagePlease contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityPlease contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement.Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesTRIM Number

Export Data Access API NSW Features of Interest Category - Emergency Service Facilities Please NoteWGS 84 service aligned to GDA94This dataset has a spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionality. Metadata Portal Metadata Information Content TitleNSW Features of Interest - Emergency Service FacilitiesContent TypeHosted Feature LayerDescriptionThe Features of Interest – Emergency Services is a point feature dataset that represents the location of Emergency Services - related datasets such as Police, Fire and SES Stations which is crucial to delivery of Emergency Services to NSW. The Features of Interest category Emergency Services is part of the Building Complex feature class and is represented as a community facility. Features that make up the NSW Features of interest Category - Emergency Services include: Fire Station – Urban (Fire and Rescue NSW) - The facility in which firefighting vehicles and equipment are stationed or intended to be stationed to serve urban communities. This point feature dataset is part of the Features of interest Category. Fire station (located in an urban area) data points are positioned within the cadastral parcel in which they are located. Fire Station - Bush (NSW Rural Fire Service) - The facility in which firefighting vehicles and equipment are stationed or intended to be stationed to serve rural communities. This point feature dataset is part of the Features of interest Category. Fire station (located in bushland) data points are positioned within the cadastral parcel in which they are located. Police Station - An office of the local police force, which may or may not have associated lock-up. This point feature dataset is part of the Features of interest Category. Police stations data points are positioned within the cadastral parcel in which they are located. State Emergency Service (SES) - A facility for the operations of the State Emergency Services (SES). This point feature dataset is part of the Features of interest Category. SES facility data points are positioned within the cadastral parcel in which they are located. These point feature datasets are part of the Features of Interest Category data and all the Emergency Services -related data centroids are positioned within the cadastral parcel in which they are located. These features do not fit within one of the ten foundation spatial data themes and are therefore classified as a category. They have historically been captured by Spatial Services as part of the NSW topographic mapping program and therefore warrant inclusion.Initial Publication Date25/02/2021Data Currency01/01/3000Data Update FrequencyOtherContent SourceData Provider FilesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Features of Interest Category.AccuracyThe dataset maintains a positional relationship to, and alignment with, a range of themes from the NSW FSDF including, transport, imagery, positioning, water and land cover. This dataset was captured by utilising the best available source at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG: 3857WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassified Data Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer Hub Terms and ConditionsCreative CommonsStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement.Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesTRIM Number