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 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.

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

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.

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/

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

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

Content

Data is a culmination of separate csv files found at https://data.louisvilleky.gov/dataset/crime-data

Each row represents a reported crime

The following description is from https://data.louisvilleky.gov/dataset/crime-data:

DATE_REPORTED - the date the incident was reported to LMPD

DATE_OCCURED - the date the incident actually occurred

UOR_DESC - Uniform Offense Reporting code for the criminal act committed

CRIME_TYPE - the crime type category

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

UCR_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 occurred

LMPD_BEAT - the LMPD beat in which the incident actually occurred

PREMISE_TYPE - 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

ID - Unique identifier for internal database

Acknowledgements

Thank you to Louisville OPEN DATA!

https://data.louisvilleky.gov/dataset/crime-data

Inspiration

Which crimes are most common? In which zip codes is crime more likely to occur? Is there a trend of some crimes increasing and other decreasing in number? Which crimes take longest to report? Which beats handle the most homicides?

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.

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.

***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 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.

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.

Data for violent crimes per Police Incident Data taken from the records managment sysmte

Part 1 crimes, as defined by the Federal Bureau of Investigation (FBI), are:

Criminal Homicide Forcible Rape Robbery Aggravated Assault Burglary Larceny Theft Grand Theft Auto Arson

Part 2 crimes, as defined by the Federal Bureau of Investigation (FBI), are:

Forgery Fraud And NSF Checks Sex Offenses Felonies Sex Offenses Misdemeanors Non-Aggravated Assaults Weapon Laws Offenses Against Family Narcotics Liquor Laws Drunk / Alcohol / Drugs Disorderly Conduct Vagrancy Gambling Drunk Driving Vehicle / Boat Vehicle / Boating Laws Vandalism Warrants Receiving Stolen Property Federal Offenses without Money Federal Offenses with Money Felonies Miscellaneous Misdemeanors Miscellaneous

Note About Date Fields:By default, the cloud database assumes all date fields are provided in UTC time zone. As a result, the system attempts to convert to the local time zone in your browser resulting in dates that appear differently than the source file. For example, a user viewing the data in PST will see times that are 8 hours behind. For an example of how dates are displayed, see the example below: Source & Download File Online Database Table Display (Example for PST User)

3/18/2023 8:07:00 AM PST 3/18/2023 8:07:00 AM UTC 3/18/2023 12:07:00 AM DATA DICTIONARY:

Field Name
Field Description


LURN_SAK
System assigned number for the case


Incident Date
Date the crime incident occurred


Incident Reported Date
Date the crime was reported to LASD


Category
Incident crime category


Stat Code
A three digit numerical coding system to identify the primary crime category for an incident


Stat Code Desc
The definition of the statistical code number


Address
The street number, street name, state and zip where the incident occurred


Street
The street number and street name where the incident occurred


City
The city where the incident occurred


Zip
The zip code of the location where the incident occurred


Incident ID
The URN #, or Uniform Report Number, is a unique # assigned to every criminal and noncriminal incident


Reporting District
A geographical area defined by LASD which is within a city or unincorporated area where the incident occurred


Sequential (per Station)
Each incident for each station is issued a unique sequence # within a given year


Gang Related
Indicates if the crime incident was gang related


Unit ID
ORI # is a number issued by the FBI for every law enforcement agency


Unit Name
Station Name


Longitude
Longitude (as plotted on the nearest half block street segment)


Latitude
Latitude (as plotted on the nearest half block street segment)


Part Category
Part I Crime or Part II Crime indicator

This is the most current information as of the date of upload. This provides the user the ability to view the most current crime information within Kansas City, Missouri. The displayed information is the most current information from the data source as of the date of upload. The data source is dynamic and therefore constantly changing. Changes to the information may occur, as incident information is refined. While the Board of Police Commissioners of Kansas City, Missouri (Board) makes every effort to maintain and distribute accurate information, no warranties and/or representations of any kind are made regarding information, data or services provided. The Board is not responsible for misinterpretation of this information and makes no inference or judgment as to the relative safety to any particular area or neighborhood. In no event shall the Board be liable in any way to the users of this data. Users of this data shall hold the Board harmless in all matters and accounts arising from the use and/or accuracy of this data.

This map shows the total crime index in the U.S. in 2018 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. 2018/2023 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

There were three key objectives to this study: (1) to determine the relative importance of crime-related as well as business-related factors in business relocation decisions, including business ownership, type of business, and business size, (2) to ascertain how businesses respond to crime and fear of crime, such as by moving, adding more security, requesting police protection, or cooperating with other businesses, and (3) to identify the types of crime prevention measures and assistance that businesses currently need and to assess the roles of business associations and police departments in providing enhanced crime prevention assistance. From November 1995 through February 1996 a mail survey was distributed to a sample of three different groups of businesses in Austin's 14 highest crime ZIP codes. The groups consisted of: (1) businesses that remained within the same ZIP code between 1990 and 1993, (2) new firms that either moved into a high-crime ZIP code area between 1990 and 1993 or were created in a high-crime ZIP code between 1990 and 1993, and (3) businesses that relocated from high-crime ZIP code areas to other locations in Austin's metropolitan area or elsewhere in Texas. Variables include type of business, ownership of business, number of employees, reasons for moving or staying in neighborhood, types of crime that affected business, owner's response to business crime, customer safety, and the role of business associations and the police in preventing crime.

This dataset is a filtered view of LASD-published year-to-date crime data for the City of West Hollywood, updated monthly. It is presented in its raw format and is completely unaltered.

Please contact the Los Angeles Sheriff's Department with any questions regarding the underlying data.

Incident Date = Date the crime incident occurred Incident Reported Date = Date the crime was reported to LASD Category = Incident crime category Stat = A three digit numerical coding system to identify the primary crime category for an incident Stat Desc = The definition of the statistical code number Address (last two digits of # rounded to 00) = The street number, street name, state and zip where the incident occurred Street (last two digits of # rounded to 00) = The street number and street name where the incident occurred City = The city where the incident occurred Zip = The zip code of the location where the incident occurred Incident ID = The URN #, or Uniform Report Number, is a unique # assigned to every criminal and noncriminal incident Reporting District = A geographical area defined by LASD which is within a city or unincorporated area where the incident occurred Seq = Each incident for each station is issued a unique sequence # within a given year Gang Related = Indicates if the crime incident was gang related (column added 08/02/2012) Unit ID = ORI # is a number issued by the FBI for every law enforcement agency Unit Name = Station Name Longitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Latitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Part Category = Part I Crime or Part II Crime indicator (replaced DELETED column 01/04/2021)