View the dataFor best results:View the dashboard in full screen.Use Chrome or Firefox as your browser.Read the dataData viewsThere are two views with this dashboard. You can toggle between them by clicking the button on the top right of the dashboard.The views are:Crime summary viewCrime details viewViewing modesThere are ways to view with this dashboard. You can toggle between them by clicking the button.The modes to view the data are:DarkLightSearch the dataCrime summary viewThe search options allow you to select:Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Crime: Select a type of crime to display.Select Chart: Select a way to display the crime data.Crime detail viewThe search options allow you to select:Date range: Select a custom date range.Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Type: Select a type of crime.Select Categories: Select one or more categories of crime to display.Select Details: Select one or more details to filter the data displayed.Select Chart: Select a way to display the crime data.View dashboard data definitions and detailed directionsView the open data set

Context

From 1934 to 1963, San Francisco was infamous for housing some of the world's most notorious criminals on the inescapable island of Alcatraz. Today, the city is known more for its tech scene than its criminal past. But, with rising wealth inequality, housing shortages, and a proliferation of expensive digital toys riding BART to work, there is no scarcity of crime in the city by the bay. From Sunset to SOMA, and Marina to Excelsior, this dataset provides nearly 12 years of crime reports from across all of San Francisco's neighborhoods.

This dataset was featured in our completed playground competition entitled San Francisco Crime Classification. The goals of the competition were to:

• predict the category of crime that occurred, given the time and location

• visualize the city and crimes (see Mapping and Visualizing Violent Crime for inspiration)

Content

This dataset contains incidents derived from SFPD Crime Incident Reporting system. The data ranges from 1/1/2003 to 5/13/2015. The training set and test set rotate every week, meaning week 1,3,5,7... belong to test set, week 2,4,6,8 belong to training set. There are 9 variables:

• Dates - timestamp of the crime incident

• Category - category of the crime incident (only in train.csv).

• Descript - detailed description of the crime incident (only in train.csv)

• DayOfWeek - the day of the week

• PdDistrict - name of the Police Department District

• Resolution - how the crime incident was resolved (only in train.csv)

• Address - the approximate street address of the crime incident

• X - Longitude

• Y - Latitude

Acknowledgements

This dataset is part of our completed playground competition entitled San Francisco Crime Classification. Visit the competition page if you are interested in checking out past discussions, competition leaderboard, or more details regarding the competition. If you are curious to see how your results rank compared to others', you can still make a submission at the competition submission page!

The original dataset is from SF OpenData, the central clearinghouse for data published by the City and County of San Francisco.

***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.

Crime severity index (violent, non-violent, youth) and weighted clearance rates (violent, non-violent), Canada, provinces, territories and Census Metropolitan Areas, 1998 to 2024.

This study assessed the implementation and impact of the One Vision One Life (OVOL) violence-prevention strategy in Pittsburgh, Pennsylvania. In 2003, the rise in violence in Pittsburgh prompted community leaders to form the Allegheny County Violence Prevention Imitative, which became the OVOL program. The OVOL program sought to prevent violence using a problem-solving, data-driven model to inform how community organizations and outreach teams respond to homicide incidents. The research team examined the impact of the OVOL program on violence using a quasi-experimental design to compare violence trends in the program's target areas before and after implementation to (1) trends in Pittsburgh neighborhoods where One Vision was not implemented, and (2) trends in specific nontarget neighborhoods whose violence and neighborhood dynamics One Vision staff contended were most similar to those of target neighborhoods. The Pittsburgh Bureau of Police provided the violent-crime data, which the research team aggregated into monthly counts. The Pittsburgh Department of City Planning provided neighborhood characteristics data, which were extracted from the 2000 Census. Monthly data were collected on 90 neighborhoods in Pittsburgh, Pennsylvania from 1996 to 2007, resulting in 12,960 neighborhood-by-month observations.

This map shows the incidence of seven major felonies -- burglary, felony assault, grand larceny, grand larceny of a motor vehicle, murder, rape, and robbery -- in New York City over the past year. Data can be mapped in aggregate at the precinct level, as a heat map showing concentration of crimes, or as individual incident points.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

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 Ontario, 1998 to 2024.

This dataset contains detailed records of reported crimes in Mexicali, Baja California, Mexico. Each entry includes information such as the type of crime, neighborhood (colonia), date and time of the incident, and geographical coordinates.

The data were obtained from the Baja California Secretariat of Citizen Security website. https://www.seguridadbc.gob.mx/contenidos/estadisticas5.php

CRIME_CLASSIFICATION: Category in which the crime is classified, as indicated in the source
CRIME_SCENE: Location of the crime at the colony level
REGISTRATION_DATE: Date on which the crime was reported. This date is used for the statistical count by month, day and year
OCCURRED_DATE: Date on which the events occurred. In some cases, it may differ from the date of recording, since a crime may be reported on a date other than the date of its commission
CRIME_TIME: Time at which the crime was committed
MUNICIPALITY: The municipality where the crime was recorded

In addition, I added the following data through the CONAPO portal https://www.gob.mx/conapo/documentos/indices-de-marginacion-2020-284372 database on Marginalization Indexes 2020

TYPE: Subdivision or Colony
X: Longitude
Y: Latitude

Abstract (en): This data collection was designed to evaluate the effects of disorderly neighborhood conditions on community decline and residents' reactions toward crime. Data from five previously collected datasets were aggregated and merged to produce this collection: (1) REACTIONS TO CRIME PROJECT, 1977 [CHICAGO, PHILADELPHIA, SAN FRANCISCO]: SURVEY ON FEAR OF CRIME AND CITIZEN BEHAVIOR (ICPSR 8162), (2) CHARACTERISTICS OF HIGH AND LOW CRIME NEIGHBORHOODS IN ATLANTA, 1980 (ICPSR 8951), (3) CRIME FACTORS AND NEIGHBORHOOD DECLINE IN CHICAGO, 1979 (ICPSR 7952), (4) REDUCING FEAR OF CRIME PROGRAM EVALUATION SURVEYS IN NEWARK AND HOUSTON, 1983-1984 (ICPSR 8496), and (5) a survey of citizen participation in crime prevention in six Chicago neighborhoods conducted by Rosenbaum, Lewis, and Grant. Neighborhood-level data cover topics such as disorder, crime, fear, residential satisfaction, and other key factors in community decline. Variables include disorder characteristics such as loitering, drugs, vandalism, noise, and gang activity, demographic characteristics such as race, age, and unemployment rate, and neighborhood crime problems such as burglary, robbery, assault, and rape. Information is also available on crime avoidance behaviors, fear of crime on an aggregated scale, neighborhood satisfaction on an aggregated scale, and cohesion and social interaction. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.. The 40 neighborhoods are a convenience sample based on the availability of surveys with similar variables of interest. Each of the five data collections from which the sample was drawn used different procedures for selecting respondents and different definitions of community. See detailed descriptions in Lewis and Skogan (ICPSR 8162), Greenberg (ICPSR 7951), Taub and Taylor (ICPSR 7952), Pate and Annan (ICPSR 8496), and Skogan's final report to the National Institute of Justice. 1998-04-20 The data have been reformatted to logical record length, and new SPSS data definition statements have been prepared. Also, SAS data definition statements were produced for the collection, and the codebook was converted to a Portable Document Format file. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (85-IJ-CX-0074).

Abstract (en): This study investigated changes in the geographic concentration of drug crimes in Cleveland from 1990 to 2001. The study looked at both the locations of drug incidents and where drug offenders lived in order to explore factors that bring residents from one neighborhood into other neighborhoods to engage in drug-related activities. This study was based on data collected for the 224 census tracts in Cleveland, Ohio, in the 1990 decennial Census for the years 1990 to 1997 and 1999 to 2001. Data on drug crimes for 1990 to 1997 and 1999 to 2001 were obtained from Cleveland Police Department (CPD) arrest records and used to produce counts of the number of drug offenses that occurred in each tract in each year and the number of arrestees for drug offenses who lived in each tract. Other variables include counts and rates of other crimes committed in each census tract in each year, the social characteristics and housing conditions of each census tract, and net migration for each census tract. This study investigated changes in the geographic concentration of drug crimes in Cleveland from 1990 to 2001. The main objectives of the study were: (1) to identify neighborhoods in which drug crimes were concentrated and neighborhoods where persons arrested for drug crimes resided, (2) to describe changes in concentrations of drug offending over time, and (3) to explain changes in patterns of drug offending in relation to changes in the social and physical structure of neighborhoods. The study looked at both the locations of drug incidents and where drug offenders lived in order to explore factors that bring residents from one neighborhood into other neighborhoods to engage in drug-related activities. This study used data collected for the 224 census tracts in Cleveland, Ohio, in the 1990 decennial census for the years 1990 to 1997 and 1999 to 2001. All of the data other than the United States Census data and the drug crime data are available on-line from the Center on Urban Poverty and Social Change's community database, Cleveland Area Network for Data and Organizing (CAN DO). Data on drug crimes for 1990 to 1997 and 1999 to 2001 were obtained from Cleveland Police Department (CPD) arrest records. These records provided the address of the incident and the residential address of the person arrested. These addresses were geocoded into their 1990 census tracts, with a match rate of over 95 percent, to produce counts of the number of drug trafficking and possession incidents occurring within each tract in each year and the number of arrestees for drug trafficking and possession living in each tract. (Users should note that no geocoded data are included in this dataset.) In 1998 the CPD changed the way that drug crimes were recorded, and the accuracy with which types of drug crimes were reported was significantly reduced. As a result, while data on the total number of drug incidents in census tracts were available for the entire length of the study, data on whether these incidents involved drug trafficking or possession were only available for 1990 to 1997. CPD arrest records for non-drug crimes and Cuyahoga County Juvenile Court data were used to produce count and rate data on non-drug crimes for each census tract. Data on the social characteristics and housing conditions of each census tract were gathered from the 1990 and 2000 Censuses. Migration into and out of each tract between 1990 and 2000 was estimated using 1990 and 2000 Census population counts and Ohio Department of Health vital statistics data on births and deaths from 1990 to 2000. Data on the number of schools in each census tract were obtained from the Cleveland Municipal School District. Several sources of data were used to develop measures of the physical characteristics of areas. These included the Cuyahoga County Auditor's parcel-level data (from 1990 to 2000) on land-use patterns, characteristics of dwellings, tax delinquencies, and assessed value, and the Home Mortgage Disclosure Act data (for 1992 to 2001) on home purchase loans and home improvement loans. Variables include 1990 census tract number, year, the City of Cleveland Statistical Planning Area that each census tract belonged to, counts and rates of violent crimes, robberies, robberies with firearms, burglaries committed by adults in each census tract in each year, robberies and violent crimes committed by juveniles in each census tract in each year, number of drug trafficking and possession in...

Pelotas/RS-Brazil, 2012.*Chi-square test of heterogeneity.&Wald test of heterogeneity.Adjustment for sex, skin color, age, schooling, income and place of residence.Association between leisure-time physical activity and insecurity from crime, restricted to individuals who reported practicing these activities close their neighborhoods.

Comprehensive crime data for Toronto neighborhoods

Report Volume Data The report counts in this data set are broken down by activity sector, report type, the year and month of receipt, and reporting entity location. The reporting entity location is represented by the forward sortation area (FSA) component of the Canadian postal code (i.e. the first three characters of the Canadian postal code, e.g. “K1P”) that designates the postal district where the reporting entity is located. ##Modernization of Reporting Forms The Financial Transactions and Reports Analysis Centre of Canada (FINTRAC) is engaged in a multi-year initiative to implement important changes to its reporting forms. Part of this modernization now allows reporting entities to submit reports containing transactions from multiple locations, increasing efficiency and reducing burden. This will change how FINTRAC publishes statistics for reports that have been modernized. FINTRAC implemented the new Large Cash Transaction Report in October 2023. As such, reporting volume statistics for the Large Cash Transaction Report will be published at a national level for quarters 3 and 4 of 2023-2024 (from October to December, and January to March, respectively). The publication of this dataset has been temporarily paused to accommodate the transition to new reporting forms. This transition is part of our ongoing efforts to enhance the quality and consistency of the data we collect. ##Protecting the Identity of Reporting Entities By law, FINTRAC must protect the identity of the persons and entities that are required to submit financial transaction reports to the Centre under the Proceeds of Crime (Money Laundering) and Terrorist Financing Act. In keeping with this responsibility to protect information, FINTRAC cannot provide more specific geographic data than is contained in this data set. Whenever possible, the data set includes the full FSA to identify the location of reporting entities submitting reports to FINTRAC. However, in any case where a certain location contains fewer than five reporting entities, or fewer than five reports submitted, only partial characters of the FSA are shown. This means that certain FSAs may contain only one or two characters (e.g., K or K1) instead of the standard three characters (e.g., K1P). In rare cases, it was not possible to provide a reporting entity location or report count without risking revealing the identity of reporting entities in a given activity sector or identifying a specific report, and so the data is provided at a national level only. All FSA levels are hierarchically inclusive. This means, for example, that the total number of report counts for K1 includes all reports submitted within all FSAs that start with K1 (i.e. K1A, K1B, K1C), including those that may not be visible because they include fewer than five reporting entities in a given activity sector. E&OE

Characteristics of students, streets, and tracts for the total sample and the subsample of students included in the fixed effects models.

The objective of this survey is to collect baseline information on police personnel and expenditures to enable detection of historical trends as well as permit comparisons at the provincial/territorial and municipal levels. For current Police Administration Survey data refer to Statistics Canada Access data here

The Boston Neighborhood Survey (BNS) was conducted by the Injury Control Research Center at the Harvard T.H. Chan School of Public Health (HSPH). The BNS was a telephone survey administered to Boston residents over three waves, in 2006, 2008, and 2010. The survey covered topics ranging from public safety to collective efficacy to social networks.

In addition to Police Districts, every resident lives in a Police Service Area (PSA), and every PSA has a team of police officers and officials assigned to it. Residents should get to know their PSA team members and learn how to work with them to fight crime and disorder in their neighborhoods. Each police district has between seven and nine PSAs. There are a total of 56 PSAs in the District of Columbia. Just enter an address or place name and click the magnifying glass to search, or just click on the map. The results will provide the geopolitical and public safety information for the address; it will also display a map of the nearest police station(s).Each Police Service Area generally holds meetings once a month. To learn more about the meeting time and location in your PSA, please contact your Community Outreach Coordinator. To reach a coordinator, choose your police district from the list below. The coordinators are included as part of each district's Roster.Visit mpdc.dc.gov for more information.