Serious violent crimes consist of Part 1 offenses as defined by the U.S. Department of Justice’s Uniform Reporting Statistics. These include murders, nonnegligent homicides, rapes (legacy and revised), robberies, and aggravated assaults. LAPD data were used for City of Los Angeles, LASD data were used for unincorporated areas and cities that contract with LASD for law enforcement services, and CA Attorney General data were used for all other cities with local police departments. This indicator is based on location of residence. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Neighborhood violence and crime can have a harmful impact on all members of a community. Living in communities with high rates of violence and crime not only exposes residents to a greater personal risk of injury or death, but it can also render individuals more susceptible to many adverse health outcomes. People who are regularly exposed to violence and crime are more likely to suffer from chronic stress, depression, anxiety, and other mental health conditions. They are also less likely to be able to use their parks and neighborhoods for recreation and physical activity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries. The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indices Permitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

This dataset aggregates Seattle Police Department crime statistics with spatial ZIP code boundaries and US Census data to determine the property crime rate per 1,000 residents. The following sources were used to create this dataset:

King County ZIP Code Boundaries

Source: https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::zip-codes/explore

King County provides approximate ZIP code boundaries, updated quarterly and published by the city of Seattle.

SPD Crime Data: 2008-Present

Source: https://data.seattle.gov/Public-Safety/SPD-Crime-Data-2008-Present/tazs-3rd5

The Seattle Police Department publishes data for reported crimes from 2008 to the present, refreshed daily. This data includes whether the crime is classified as against a person, against property, or against society.

2020 ACS 5-year estimates for population

Source: https://data.census.gov/cedsci/table?q=population%20seattle&g=0400000US53%248600000&tid=ACSDP5Y2020.DP05

The US Census Department American Community Survey (ACS) publishes 5-year estimates of population by a variety of geographies, including ZIP Code Tabulation Areas (ZCTAs), geographic approximations of each ZIP code.

Data Processing

Using the pandas and geopandas libraries within python, the following processing steps were followed to prepare this dataset: - Converted the date and time reported field in the SPD dataset to a datetime object and extracted the year - Filtered to crimes reported between 2008 and 2021 - Filtered to only crimes against property - Dropped rows with null values for year, crime against category, longitude, or latitude - Performed a spatial join using the latitude and longitude for each report in the SPD data to append a ZIP code from the King County ZIP Code boundary shapefile - Summarized to calculate a count of property crimes reported for each combination of year and ZIP code - Summarized by ZIP code to calculate the count of years with at least one crime reported and the total number of property crimes reported - Calculated the average number of property crimes reported per year in each ZIP code - Merged with the ACS population estimates - Calculated the number of property crimes reported per year per 1,000 population for each zip code

Photo by Justus Hayes: https://www.pexels.com/photo/a-bicycle-chained-to-a-metal-post-6355944/

This map shows the total crime index in the U.S. in 2020 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indicesThe values are all referenced by an index value. The index values for the US level are 100, representing average crime for the country. A value of more than 100 represents higher crime than the national average, and a value of less than 100 represents lower crime than the national average. For example, an index of 120 implies that crime in the area is 20 percent higher than the US average; an index of 80 implies that crime is 20 percent lower than the US average.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layersPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The study integrated neighborhood-level data on robbery and burglary gathered from local police agencies across the United States, foreclosure data from RealtyTrac (a real estate information company), and a wide variety of social, economic, and demographic control variables from multiple sources. Using census tracts to approximate neighborhoods, the study regressed 2009 neighborhood robbery and burglary rates on foreclosure rates measured for 2007-2008 (a period during which foreclosure spiked dramatically in the nation), while accounting for 2007 robbery and burglary rates and other control variables that captured differences in social, economic, and demographic context across American neighborhoods and cities for this period. The analysis was based on more than 7,200 census tracts in over 60 large cities spread across 29 states. Core research questions were addressed with a series of multivariate multilevel and single-level regression models that accounted for the skewed nature of neighborhood crime patterns and the well-documented spatial dependence of crime. The study contains one data file with 8,198 cases and 99 variables.

This dataset reflects reported incidents of crime that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. 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. Any use of the information for commercial purposes is strictly prohibited. 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.

The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on communitycrimemap.comData Dictionary:

Field Name

Field Description

Incident Number

the number associated with either the incident or used as reference to store the items in our evidence rooms

Date Reported

the date the incident was reported to LMPD

Date Occurred

the date the incident actually occurred

Badge ID

Badge ID of responding Officer

Offense Classification

NIBRS Reporting category for the criminal act committed

Offense Code Name

NIBRS Reporting code for the criminal act committed

NIBRS_CODE

the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/view

NIBRS Group

hierarchy that follows the guidelines of the FBI National Incident Based Reporting System

Was Offense Completed

Status indicating whether the incident was an attempted crime or a completed crime.

LMPD Division

the LMPD division in which the incident actually occurred

LMPD Beat

the LMPD beat in which the incident actually occurred

Location Category

the type of location in which the incident occurred (e.g. Restaurant)

Block Address

the location the incident occurred

City

the city associated to the incident block location

Zip Code

the zip code associated to the incident block location

Contact:LMPD Open Records lmpdopenrecords@louisvilleky.gov

Comprehensive crime statistics for Los Angeles County's safest neighborhoods including violent crime rates, property crime rates, and annual victimization chances by neighborhood for 2024-2025.

Much research has examined how crime rates vary across urban neighborhoods, focusing particularly on community-level demographic and social characteristics. A parallel line of work has treated crime at the individual level as an expression of certain behavioral patterns (e.g., impulsivity). Little work has considered, however, whether the prevalence of such behavioral patterns in a neighborhood might be predictive of local crime, in large part because such measures are hard to come by and often subjective. The Facebook Advertising API offers a special opportunity to examine this question as it provides an extensive list of “interests” that can be tabulated at various geographic scales. Here we conduct an analysis of the association between the prevalence of interests among the Facebook population of a ZIP code and the local rate of assaults, burglaries, and robberies across 9 highly populated cities in the US. We fit various regression models to predict crime rates as a function of the Facebook and census demographic variables. In general, models using the variables for the interests of the whole adult population on Facebook perform better than those using data on specific demographic groups (such as Males 18-34). In terms of predictive performance, models combining Facebook data with demographic data generally have lower error rates than models using only demographic data. We find that interests associated with media consumption and mating competition are predictive of crime rates above and beyond demographic factors. We discuss how this might integrate with existing criminological theory.

Note: Due to a system migration, this data will cease to update on March 14th, 2023. The current projection is to restart the updates on or around July 17th, 2023.Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on Crimemapping.comData Dictionary:INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence roomsDATE_REPORTED - the date the incident was reported to LMPDDATE_OCCURED - the date the incident actually occurredUOR_DESC - Uniform Offense Reporting code for the criminal act committedCRIME_TYPE - the crime type categoryNIBRS_CODE - the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/viewUCR_HIERARCHY - hierarchy that follows the guidelines of the FBI Uniform Crime Reporting. For more details visit https://ucr.fbi.gov/ATT_COMP - Status indicating whether the incident was an attempted crime or a completed crime.LMPD_DIVISION - the LMPD division in which the incident actually occurredLMPD_BEAT - the LMPD beat in which the incident actually occurredPREMISE_TYPE - the type of location in which the incident occurred (e.g. Restaurant)BLOCK_ADDRESS - the location the incident occurredCITY - the city associated to the incident block locationZIP_CODE - the zip code associated to the incident block locationID - Unique identifier for internal databaseContact:Crime Information CenterCrimeInfoCenterDL@louisvilleky.gov

Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on Crimemapping.comData Dictionary:INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence roomsDATE_REPORTED - the date the incident was reported to LMPDDATE_OCCURED - the date the incident actually occurredUOR_DESC - Uniform Offense Reporting code for the criminal act committedCRIME_TYPE - the crime type categoryNIBRS_CODE - the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/viewUCR_HIERARCHY - hierarchy that follows the guidelines of the FBI Uniform Crime Reporting. For more details visit https://ucr.fbi.gov/ATT_COMP - Status indicating whether the incident was an attempted crime or a completed crime.LMPD_DIVISION - the LMPD division in which the incident actually occurredLMPD_BEAT - the LMPD beat in which the incident actually occurredPREMISE_TYPE - the type of location in which the incident occurred (e.g. Restaurant)BLOCK_ADDRESS - the location the incident occurredCITY - the city associated to the incident block locationZIP_CODE - the zip code associated to the incident block locationID - Unique identifier for internal databaseContact:Crime Information CenterCrimeInfoCenterDL@louisvilleky.gov

This map shows the total crime index in the U.S. in 2021 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indicesThe values are all referenced by an index value. The index values for the US level are 100, representing average crime for the country. A value of more than 100 represents higher crime than the national average, and a value of less than 100 represents lower crime than the national average. For example, an index of 120 implies that crime in the area is 20 percent higher than the US average; an index of 80 implies that crime is 20 percent lower than the US average.For more information about the AGS Crime Indices, click here. Additional Esri Resources:Esri DemographicsU.S. 2021/2026 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layersPermitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

This study examines the question of how some urban neighborhoods maintain a low crime rate despite their proximity and similarity to relatively high crime areas. The purpose of the study is to investigate differences in various dimensions of the concept of territoriality (spatial identity, local ties, social cohesion, informal social control) and physical characteristics (land use, housing, street type, boundary characteristics) in three pairs of neighborhoods in Atlanta, Georgia. The study neighborhoods were selected by locating pairs of adjacent neighborhoods with distinctly different crime levels. The criteria for selection, other than the difference in crime rates and physical adjacency, were comparable racial composition and comparable economic status. This data collection is divided into two files. Part 1, Atlanta Plan File, contains information on every parcel of land within the six neighborhoods in the study. The variables include ownership, type of land use, physical characteristics, characteristics of structures, and assessed value of each parcel of land within the six neighborhoods. This file was used in the data analysis to measure a number of physical characteristics of parcels and blocks in the study neighborhoods, and as the sampling frame for the household survey. The original data were collected by the City of Atlanta Planning Bureau. Part 2, Atlanta Survey File, contains the results of a household survey administered to a stratified random sample of households within each of the study neighborhoods. Variables cover respondents' attitudes and behavior related to the neighborhood, fear of crime, avoidance and protective measures, and victimization experiences. Crime rates, land use, and housing characteristics of the block in which the respondent resided were coded onto each case record.

In February 2019, we updated the neighborhood assignment with regards to the new police record system.

The data set is refreshed on the third day of the month at 8:45 AM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.

Update Frequency: Daily

Current year to date. The data included in this dataset has been reviewed and approved by a Milwaukee Police Department supervisor and the Milwaukee Police Department’s Records Management Division. This approval process can take a few weeks from the reported date of the crime. For preliminary crime data, please visit the Milwaukee Police Department’s Crime Maps and Statistics dashboard at https://city.milwaukee.gov/police/Information-Services/Crime-Maps-and-Statistics.

Wisconsin Incident Based Report (WIBR) Group A Offenses.

The Crime Data represents incident level data defined by Wisconsin Incident Based Reporting System (WIBRS) codes. WIBRS reporting is a crime reporting standard and can not be compared to any previous UCR report. Therefore, the Crime Data may reflect:

• Information not yet verified by further investigation
• Preliminary crime classifications that may be changed at a later date based upon further investigation
• Information that may include mechanical or human error

Neither the City of Milwaukee nor the Milwaukee Police Department guarantee (either express or implied) the accuracy, completeness, timeliness, or correct sequencing of the Crime Data. The City of Milwaukee and the Milwaukee Police Department shall have no liability for any error or omission, or for the use of, or the results obtained from the use of the Crime Data. In addition, the City of Milwaukee and the Milwaukee Police Department caution against using the Crime Data to make decisions/comparisons regarding the safety of or the amount of crime occurring in a particular area. When reviewing the Crime Data, the site user should consider that:

• The information represents only police services where a report was made and does not include other calls for police service
• The information does not reflect or certify "safe" or "unsafe" areas
• The information will sometimes reflect where the crime was reported versus where the crime occurred

This data is not intended to represent a total number/sum of crimes, rather 1 = True and 0 = False.

The use of the Crime Data indicates the site user's unconditional acceptance of all risks associated with the use of the Crime Data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page. XY fields in data is in projection Wisconsin State Plane South NAD27 (WKID 32054).

This table contains data on the rate of violent crime (crimes per 1,000 population) for California, its regions, counties, cities and towns. Crime and population data are from the Federal Bureau of Investigations, Uniform Crime Reports. Rates above the city/town level include data from city, university and college, county, state, tribal, and federal law enforcement agencies. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Ten percent of all deaths in young California adults aged 15-44 years are related to assault and homicide. In 2010, California law enforcement agencies reported 1,809 murders, 8,331 rapes, and over 95,000 aggravated assaults. African Americans in California are 11 times more likely to die of assault and homicide than Whites. More information about the data table and a data dictionary can be found in the About/Attachments section.

This method returns total crime rates, crime rates by crime types, area ratings by total crime, and area ratings by crime type for small areas (Lower Layer Super Output Areas, or LSOAs) and Local Authority Districts (LADs). The results are determined by the inclusion of the submitted postcode/coordinates/UPRN within the corresponding LSOA or LAD.

All figures are annual (for the last 12 months).

The crime rates are calculated per 1,000 resident population derived from the census 2021.

The dataset is updated on a monthly basis, with a 3-month lag between the current date and the most recent data.

Esri's Crime Indexes data incorporates information from the AGS national CrimeRisk database that is based on an extensive analysis of several years of crime incidents reported by most US law enforcement jurisdictions. The Crime Indexes database includes standardized indexes for a range of serious crimes against both persons and property. The data vintage is 2019. All attributes are available at the following geography levels: State, County, Tract, Block Group, ZIP Code, Place, CBSA and DMA. Attributes include total crime index, personal crime index, and other indexes for serious crimes. To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics5/USA_Crime.

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