Interactive dashboard for open data portal. Displays crimes by zip code.

***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.

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

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.

The objective of the Seattle Neighborhoods and Crime Survey (SNCS) was to test multilevel theories of neighborhood social organization and criminal violence. It was funded by the National Science Foundation (SES-0004324), and the National Consortium on Violence Research (SBR-9513040). Using the concept of differential neighborhood organization, the investigators posited that neighborhood crime is a function of informal social control against crime and informal organization in favor of crime. Informal neighborhood control against crime consists of neighborhood attachment, social capital, and collective efficacy. The study tested the hypothesis that individual social ties are explained by a rational choice model, which in turn produces neighborhood social capital that can be used to achieve collective goals. It also tested the hypothesis that neighborhoods rich in social capital had greater collective efficacy, which in turn, helped produce safe neighborhoods. Organization in favor of crime consists of violent codes of the street. The study tested the hypothesis that residents from disadvantaged neighborhoods tend to distrust police and other agents of conventional institutions, and consequently are more likely to participate in street culture, in which violence is a way of obtaining street credibility and status, as well as resolving disputes. The project has also examined dimensions of neighboring, and the causes and consequences of fear of crime. The study used a telephone survey of households within all 123 census tracts in the city of Seattle, WA, conducted in 2002-2003. The sampling frame was designed by investigators at the University of Washington, with three objectives in mind: (a) to gain a random sample of households within each of 123 census tracts; (b) to obtain a disproportionate number of racial and ethnic minorities using an ethnic oversample; and (c) to obtain a replication sample of Terrance Miethe's 1990 victimization survey in 100 Seattle neighborhoods [Testing Theories of Criminality and Victimization in Seattle, 1960-1990]. Specific samples were drawn by Genesys, a sampling firm in Philadelphia, PA, using a constantly-updated compilation of white pages. Telephone interviews were conducted by the Social and Behavioral Research Institute at California State University, San Marcos, using computer-assisted telephone interviewing (CATI) technology. Respondents were asked about household demographics, such as race, gender, residential mobility, age distribution of the household, and income, their perceptions and assessments of their neighborhoods (including safety, disorder, and crime), neighbors, and relations with police. A variety of questions about neighboring were asked, including social capital (intergenerational closure, reciprocated exchange, and participation in neighborhood associations), attachment to their neighborhood, and collective efficacy (child-centered social control). Respondents were asked about routine activities including taking steps to protect their homes, spending time in bars and nightclubs, and leaving their home unattended. Questions about fear of crime included personal fear as well as altruistic fear for other members of the household, and questions about racial attitudes included residential preferences by race composition of the neighborhood. A victimization inventory modeled after the National Crime Victimization Survey was used for burglary, vandalism, stolen property, violence, and robbery. Demographic information includes age, race, sex, education, martial status, household income, whether respondent was a student, employment status, religious affiliation, approximate value of home, monthly rent including utilities, residence history in the last five years, whether respondent was born in the Unites States, and number of people currently living in the respondent's household.

The violent crime rate measures the number of Part 1 crimes identified as being violent (homicide, rape, aggravated assault, and robbery) that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore Police DepartmentYears Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

Data is no longer provided by the Calgary Police Service. To access latest data click here. This data is considered cumulative as late-reported incidents are often received well after an offence has occurred. Therefore, crime counts are subject to change as they are updated. Crime count is based on the most serious violation (MSV) per incident. Violence: These figures include all violent crime offences as defined by the Centre for Canadian Justice Statistics Universal Crime Reporting (UCR) rules. Domestic violence is excluded. Break and Enter: Residential B&E includes both House and ‘Other’ structure break and enters due to the predominantly residential nature of this type of break in (e.g. detached garages, sheds). B&Es incidents include attempts.

This table contains figures on property, vandalism and public order and violent crimes registered by the police. In addition to absolute numbers, the numbers per 1000 inhabitants of some types of crime are also included. The figures are broken down by municipality, district and neighbourhood. Crimes for which the crime scene cannot be attributed to a neighborhood are not included in this table. Data available on: 2018 Status of the figures: The figures are final. Changes as of February 18, 2021: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. Crime figures per neighborhood can be found on the police data portal. See section 3.

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.

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).

Comprehensive crime data for Toronto neighborhoods

Replication Data for: Social Interactions and Crime Revisited: An Investigation Using Individual Offender Data in Dutch Neighborhoods

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.

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

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. 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). ##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

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...

Data available in consultation with NSCR. Please contact the datamanager [datamanagement@nscr.nl]

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled “sharing of information” between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online.

This data collection was designed to test the "incivilities thesis": that incivilities such as extant neighborhood physical conditions of disrepair or abandonment and troubling street behaviors contribute to residents' concerns for personal safety and their desire to leave their neighborhood. The collection examines between-individual versus between-neighborhood and between-city differences with respect to fear of crime and neighborhood commitment and also explores whether some perceived incivilities are more relevant to these outcomes than others. The data represent a secondary analysis of five ICPSR collections: (1) CHARACTERISTICS OF HIGH AND LOW CRIME NEIGHBORHOODS IN ATLANTA, 1980 (ICPSR 7951), (2) CRIME CHANGES IN BALTIMORE, 1970-1994 (ICPSR 2352), (3) CITIZEN PARTICIPATION AND COMMUNITY CRIME PREVENTION, 1979: CHICAGO METROPOLITAN AREA SURVEY (ICPSR 8086), (4) CRIME, FEAR, AND CONTROL IN NEIGHBORHOOD COMMERCIAL CENTERS: MINNEAPOLIS AND ST. PAUL, 1970-1982 (ICPSR 8167), and (5) TESTING THEORIES OF CRIMINALITY AND VICTIMIZATION IN SEATTLE, 1960-1990 (ICPSR 9741). Part 1, Survey Data, is an individual-level file that contains measures of residents' fear of victimization, avoidance of dangerous places, self-protection, neighborhood satisfaction, perceived incivilities (presence of litter, abandoned buildings, vandalism, and teens congregating), and demographic variables such as sex, age, and education. Part 2, Neighborhood Data, contains crime data and demographic variables from Part 1 aggregated to the neighborhood level, including percentage of the neighborhood that was African-American, gender percentages, average age and educational attainment of residents, average household size and length of residence, and information on home ownership.

A web map used for the Police Service Area Details web application.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.Printable PDF versions of each district map are available on the district pages. Residents and visitors may also access the PSA Finder to easily locate a PSA and other resources within a geographic area. 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 https://mpdc.dc.gov for more information.