Each record in this dataset shows information about an arrest executed by the Chicago Police Department (CPD). Source data comes from the CPD Automated Arrest application. This electronic application is part of the CPD CLEAR (Citizen Law Enforcement Analysis and Reporting) system, and is used to process arrests Department-wide.

A more-detailed version of this dataset is available to media by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Arrests Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup. New data fields may be added to this public dataset in the future. Requests for individual arrest reports or any other related data other than access to the more-detailed dataset should be directed to CPD, through contact information on that site or a Freedom of Information Act (FOIA) request.

The data is limited to adult arrests, defined as any arrest where the arrestee was 18 years of age or older on the date of arrest. The data excludes arrest records expunged by CPD pursuant to the Illinois Criminal Identification Act (20 ILCS 2630/5.2).

Department members use charges that appear in Illinois Compiled Statutes or Municipal Code of Chicago. Arrestees may be charged with multiple offenses from these sources. Each record in the dataset includes up to four charges, ordered by severity and with CHARGE1 as the most severe charge. Severity is defined based on charge class and charge type, criteria that are routinely used by Illinois court systems to determine penalties for conviction. In case of a tie, charges are presented in the order that the arresting officer listed the charges on the arrest report. By policy, Department members are provided general instructions to emphasize seriousness of the offense when ordering charges on an arrest report.

Each record has an additional set of columns where a charge characteristic (statute, description, type, or class) for all four charges, or fewer if there were not four charges, is concatenated with the | character. These columns can be used with the Filter function's "Contains" operator to find all records where a value appears, without having to search four separate columns.

Users interested in learning more about CPD arrest processes can review current directives, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). Relevant directives include:

• Special Order S06-01-11 – CLEAR Automated Arrest System: describes the application used by Department members to enter arrest data. • Special Order S06-01-04 – Arrestee Identification Process: describes processes related to obtaining and using CB numbers. • Special Order S09-03-04 – Assignment and Processing of Records Division Numbers: describes processes related to obtaining and using RD numbers. • Special Order 06-01 – Processing Persons Under Department Control: describes required tasks associated with arrestee processing, include the requirement that Department members order charges based on severity.

This comprehensive dataset offers detailed crime data from 2020 to the present, encompassing a wide range of criminal offenses, arrest statistics, and law enforcement activities across the United States. The dataset captures the evolving landscape of crime during a pivotal period shaped by the COVID-19 pandemic, social justice movements, and shifting socio-political dynamics.

Each record provides granular information on crime types, including violent crimes, property offenses, drug-related incidents, and more. The data includes key variables such as location details, timeframes, demographic information of offenders and victims, and arrest outcomes, enabling deep analysis of crime trends at national, state, and local levels.

This dataset is a valuable resource for criminal justice researchers, policy makers, law enforcement agencies, and data analysts, offering crucial insights for understanding patterns in public safety, shaping crime prevention strategies, and informing data-driven policy decisions. It supports comparative studies on crime fluctuations during and after significant societal events, helping stakeholders address pressing issues in public safety and community well-being.

The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in from December 2016 to present. Note that records are included in the dataset based on when an incident is reported which could result in an occurrence date before December 2016. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows)details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2015 dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.

Context

This dataset aggregates all police records from the Stanford Open Policing Project from cities in North Carolina. It provides over 4.5 million records of police stops that have been cleaned and formatted into an easy to use format. It's also the data used to power the models used in the web application policexray.

It's designed to make exploring and understanding police outcomes accessible for aspiring data scientists.

Content

Every row in this dataset represents a police stop, and it records information about the subject's location (city), demographics (sex, ethnicity), whether or not they were searched or arrested, if any contraband was found, and why they were stopped and why they were pulled over. It's been cleaned and distilled from the original source to condense the most useful information people would have available to them at the time of the event, and the data has been cleaned to make it more accessible and easier to use for analysis.

Acknowledgements

This data would not be possible without all of the work done at Stanford Open Policing, who made the original versions possible.

Inspiration

With all of the attention being given to policing outcomes and the possibility of bias in policing, creating pathways that allow people of different backgrounds and specialties to explore source data and uncover what patterns emerge under different scenarios.

Arrest data contains information on people taken into custody by City of Pittsburgh police officers. More serious crimes such as felony offenses are more likely to result in an arrest. However, arrests can occur as a result of other offenses, such as parole violations or a failure to appear for trial. All data is reported at the block/intersection level, with the exception of sex crimes, which are reported at the police zone level. This dataset only contains information reported by City of Pittsburgh Police. It does not contain information about incidents that solely involve other police departments operating within the city (for example, campus police or Port Authority police). More documentation is available in our Crime Data Guide.

This data set contains data from individuals arrested by a police officer in Montgomery County, including whether the arrest location is within 500 feet of a school.

About Montgomery County Maryland’s Digital Government Strategy

https://montgomeryenergyconnection.org/wp-content/uploads/2019/10/MC_Seal.png" alt="">

The County has been an early adopter of technology and promoter of a transparent and efficient government. This represents the County’s commitment to being an inclusive, innovative and transparent government that is accountable and responsive and maintains a strong and vibrant economy. The County has been a leader in open government since the launch of its e-Government website initiative in 1992. Since then, the County has sustained a strong commitment to digitizing its information and services.

Context

This ArcGIS Online dashboard provides key indicators of arrest activity in Tempe for the past 90 days. "Total Arrests" may include the same individual more than once, while "Individuals Arrested" counts unique people, some of whom may have been arrested multiple times. "Arresting Officers" reflects the number of different officers involved in arrests, and "Charges" includes all charges filed, where multiple charges may stem from a single arrest event. For full details and historical trends, visit the Recent and Historic Arrest Demographics dashboards on the Arrest Demographics Hub page. The dashboard is featured on the Tempe Police Data Hub and is updated regularly to reflect the latest arrest activity.For detailed guidance, please refer to the User Guide: Arrests Demographics Hub Page.

Context

Crime data analysis is essential for understanding patterns of criminal activity, identifying risk factors, and informing public safety policies. This dataset provides a detailed look at reported offenses in Indiana for the year 2023, offering valuable insights into demographic trends, geographic crime distribution, and seasonal variations. By analyzing this dataset, researchers, policymakers, and data enthusiasts can uncover key factors influencing crime rates and develop data-driven strategies for prevention and intervention.

Content

This dataset compiles crime records from Indiana in 2023, structured to facilitate in-depth analysis across various dimensions. It includes:

• Demographics – Age, race, and gender details of individuals involved in reported offenses, enabling a deeper understanding of crime patterns among different population groups.
• Offense Details – Categorized crime types, including theft, violent crimes, drug-related offenses, and property crimes, to reveal crime distribution across Indiana.
• Temporal Patterns – A breakdown of crimes by month to identify seasonal crime trends and fluctuations throughout the year.
• Geographic Distribution – County-level crime data that highlight high-crime areas and differences in crime rates between urban and rural regions.
• Arrest Rates – Information on arrests linked to various offenses, providing insights into law enforcement actions and policy effectiveness.

Inspiration

This dataset presents several opportunities for exploration and analysis:

• Which demographics are most affected by specific types of crime?
• How do crime rates vary across different counties, and what factors contribute to regional differences?
• Are there seasonal patterns in criminal activity that could inform law enforcement strategies?
• What are the relationships between arrest rates and specific types of offenses?

Potential Applications

This dataset is well-suited for various analytical and research purposes, including:

• Demographic Analysis – Examining which age, race, and gender groups are most affected by certain offenses.
• Geographic Insights – Analyzing county-level crime rates to understand how population density and urbanization impact crime trends.
• Temporal Analysis – Identifying seasonal crime patterns to assist in resource allocation and crime prevention strategies.
• Policy Recommendations – Using data insights to propose interventions aimed at reducing crime and improving community safety.
• Machine Learning Applications – Developing predictive models for crime forecasting and classification.

Ideal For

• Beginners and intermediate analysts looking to apply data cleaning, visualization, and storytelling skills.
• Machine learning enthusiasts interested in crime prediction models.
• Policymakers, law enforcement agencies, and public safety organizations seeking data-driven insights for decision-making.

Dataset Origin

This dataset was curated from publicly available Indiana crime records and compiled for educational and analytical purposes. All personally identifiable information has been anonymized to ensure privacy.

Licensing and Restrictions

This dataset is open for non-commercial projects. Attribution to the original source is appreciated when sharing findings or insights.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code.
The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

This version (V3) fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported. This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. The monthly zip files contain one data file per year(57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, feather, Stata) the data is in. Due to file size limits on open ICPSR, not all file types were included for all the data. The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file here. https://github.com/jacobkap/crime_data/blob/master/R_code/offenses_known.RThe zip files labeled "yearly" contain yearly data rather than monthly. These also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. Each zip folder contains two files: a data file in whatever format you choose and a codebook. The data file is aggregated yearly and has already combined every year 1960-2016. For the code I used to do this, see here https://github.com/jacobkap/crime_data/blob/master/R_code/yearly_offenses_known.R.If you find any mistakes in the data or have any suggestions, please email me at jkkaplan6@gmail.comAs a description of what UCR Offenses Known and Clearances By Arrest data contains, the following is copied from ICPSR's 2015 page for the data.The Uniform Crime Reporting Program Data: Offenses Known and Clearances By Arrest dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.

Archived as of 9/25/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. This dataset is the underlying data for the Public Safety portion of the Equity Data Portal displaying Indiana's re-arrests by demographics. Re-arrest rates are given for a cohort of releases within a given year. The re-arrest rate is calculated based upon the number of individuals released that had an arrest within a year of their release date. County is based upon county of commitment. Parole violations (& possibly probation violations) are not captured in Indiana State Police (ISP) arrest data and will therefore be underrepresented in the data. Arrest data comes from the Criminal History Repository System (CHRIS). Data feeding into the CHRIS system comes from three main sources. Arrest data comes from the LiveScan system, which is used for fingerprinting and capturing other pertinent information at the time of the arrest. Criminal disposition data are maintained by prosecutors in ProsLink system, and by the courts in the Odyssey system. Arrest data are sent to ISP soon after the arrest occurs, but disposition data have a lag of approximately seven months as the case makes its way through the legal system. Text description of the original offenses are provided by the arresting officer when the offender is arrested. Later, the prosecutor's office or court provides a text description of the filed offenses, along with the Indiana Code title, article, chapter, and section (e.g.35-48-4-6). The filed offense may be amended later. The data refers to the "most recent" offenses (arrest or dispositioned). The date range for the data is 2013 to 2020. The data provides a one-year post-release analysis on the charges of recently released individuals.

This data ceased updating with the transition to a new records management system on 11/14/2023. Access to the updated data set has been added as of April 11, 2025 here: Crime Data Guide.

The data represents individuals arrested with a marijuana charge, regardless of whether there was a more serious secondary charge. If an arrestee was charged with multiple marijuana charges, the arrest is only counted once under the more serious charge type (Manufacture/Cultivation > Distribution > Possession with Intent to Distribute > Possession > Public Consumption). The category of “Manufacture or Cultivation” was added in the 2019 data and for future years, but is not utilized in prior years.MPD collects race and ethnicity data according to the United States Census Bureau standards (https://www.census.gov/topics/population/race/about.html). Hispanic, which was previously categorized under the Race field prior to August 2015, is now captured under Ethnicity. All records prior to August 2015 have been updated to “Unknown (Race), Hispanic (Ethnicity).” Data on race and ethnicity prior to November 9, 2018 was based on officer observation; on and after November 9, 2018, the data is based on the arrestee’s response.MPD cannot release exact addresses to the general public unless proof of ownership or subpoena is submitted. The GeoX and GeoY values represent the block location (approximately 232 ft. radius) as of the date of the arrest. Due to the Department’s redistricting efforts in 2012 and 2019, data may not be comparable in some years.Arrestee age is calculated based on the number of days between the self-reported or verified date of birth (DOB) of the arrestee and the date of the arrest; DOB data may not be accurate if self-reported or if the arrestee refused to provide it.Due to the sensitive nature of juvenile data and to protect the arrestee’s confidentiality, any arrest records for defendants under the age of 18 have been coded as “NA” for the following fields:• Arrest Hour• CCN• Age• Offense Location Block GeoX/Y• Defendant Race• Defendant Ethnicity• Defendant Sex• Arrest Location Block Address• Arrest Location Block GeoX/YThis data may not match other marijuana data requests that may have included all law enforcement agencies in the District, or only the most serious charge. Figures are subject to change due to record sealing, expungements, and data quality audits.

Crime rate against Children-India

Investigating Crime Trends and Patterns Across India

By Bhavna Chawla [source]

About this dataset

This dataset provides an in-depth look at crime against children throughout India. The data, collected from state and union territories throughout the country, tracks arrests made in response to a variety of crimes including infanticide, murder of children, rape of Children, kidnapping and abduction of children, foeticide, abetment of suicide, exposure and abandonment. Additionally it looks at procuration of minor girls as well as buying or selling minors for prostitution. It also illustrates arrests made related to violation or prevention under the Prohibition Of Child Marriage Act (PCMA).

The dataset paints an unfortunately dark image across India with rising numbers each year - painfully representing the suffering these innocent minors have faced over time. Through this dataset we can not only get a better understanding on who is leading the charge in terms of crime rate but also uncover startling patterns about type specified categories that are particularly egregious when it comes to number of arrests made. By examining this data more closely together we can unravel meaningful solutions which ultimately could help protect our beloved child population from needless harm and distress

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For more datasets, click here.

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How to use the dataset

This dataset is suitable for researchers interested in learning more about crime against children as well as government planners who may want to analyze which states have higher rates of various types of crimes and identify strategies for managing them.

To use this dataset, start by examining the main columns – STATE/UT, CRIME HEAD, 2001-2012 – which provide additional information about each row such as state or UT name and type of crime committed respectively. Then you can use a visualized comparison to evaluate trends across all the listed years: a look at total numbers or changes over time will help reveal how arrests vary among different categories or within a particular year; it will also identify areas with particularly high numbers that need more attention from policy makers. These visualizations can also be compared with statistics on population density or socio-economic characteristics such as literacy rate or poverty levels to get further insights into characterizing patterns for targeted interventions that could reduce criminal activities towards vulnerable communities.

Additionally, you could use this dataset combined with other external sources/variables (governance measures taken against certain categories etc.) to build predictive models that identify relationships between risks factors associated with higher rate of specific type(s) criminal activities prevailing amongst certain age group(s). Such approaches would help contribute towards evidence informed public safety interventions, public health initiatives and legal systems strengthening over time specifically targeting those districts where higher rates are taking place so that people especially women & girls are protected from any form physical abuse & harassment leading potential threat on their living condition & livelihood opportunities eventually affecting national development levels if left unchecked regularly each year progressing forward

Research Ideas

• This dataset could be used to identify the states with the highest crime rates against children, and explore any potential correlations between crime statistics and social or economic factors in those states.
• This dataset can also be used to analyze state-wise trends over time to assess whether government initiatives aimed at curbing crimes against children have been effective or not.
• The dataset can also help researchers examine which type of crimes are most prevalent in each state/UT and come up with ways to reduce these crimes via policy measures or public outreach programs, etc

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Crime head-wise persons arrested under crime against children during 2001-2012.csv | Column name | Description | |:---------------|:----------------------------------------------------------------| | STATE/UT | The state or union territory in India. (String) | | CRIME HEAD | The type of crime against chi...

Los Angeles Crime Data (2020 - Present)

📌 Updated: February 7, 2025

Overview

This dataset contains reported crime incidents in the City of Los Angeles from 2020 to the present, provided by the Los Angeles Police Department (LAPD). It includes key details such as crime type, location (anonymized), and date. The dataset is derived from official LAPD records and is regularly updated.

⚠️ Note: LAPD transitioned to a new Records Management System (RMS) on March 7, 2024, to comply with the FBI’s NIBRS (National Incident-Based Reporting System). During this transition, some crime data may still reflect the older system.

Dataset Highlights

✔ Crime Incidents: Reported cases from 2020 onwards ✔ Location Details: Anonymized to the nearest hundred block ✔ Reporting System: Transition to FBI's NIBRS compliance ✔ Data Accuracy: Transcribed from original LAPD reports

Important Notes

🔹 Temporary Reporting Delays – LAPD is experiencing technical issues affecting data updates. Until resolved, updates will be bi-weekly instead of weekly. 🔹 Data Limitations – Some missing location fields are recorded as (0°, 0°) due to privacy constraints. 🔹 Possible Inaccuracies – Crime reports are transcribed manually, leading to potential data errors.

Usage & Applications

✅ Crime trend analysis over time ✅ Crime hotspot detection & mapping ✅ Law enforcement and policy research ✅ Machine learning applications (predictive modeling)

Column Descriptions for Los Angeles Crime Data (2020 - Present)

Column Name and Description

DR_NO: Unique crime report number assigned by LAPD. Date Rptd: Date when the crime was reported to the LAPD (MM/DD/YYYY HH:MM:SS AM/PM). DATE OCC: Date when the crime occurred (MM/DD/YYYY HH:MM:SS AM/PM). TIME OCC: Time when the crime occurred, in 24-hour format (e.g., 2130 = 9:30 PM). AREA: Numerical code representing the LAPD division where the crime occurred. AREA NAME: Name of the LAPD division (e.g., Wilshire, Central, Southwest, etc.). Rpt Dist No: Reporting district number used internally by LAPD. Part 1-2: Crime category: 1 = Serious (violent/property crimes), 2 = Less serious crimes. Crm Cd: Crime classification code assigned by LAPD. Crm Cd Desc: Description of the crime, such as "Vehicle - Stolen" or "Burglary from Vehicle". Mocodes: Modus Operandi (MO) codes, which indicate methods used by criminals. Vict Age: Age of the victim (0 may indicate missing data). Vict Sex: Gender of the victim (M = Male, F = Female, X = Unknown). Vict Descent: Ethnicity of the victim, encoded as: W (White), B (Black), H (Hispanic), A (Asian), O (Other), etc. Premis Cd: Numerical code representing the type of location where the crime occurred. Premis Desc: Description of the location, such as "Street," "Bus Stop," "Apartment," etc. Weapon Used Cd: Weapon code, if a weapon was used in the crime (NaN if no weapon was involved). Weapon Desc: Description of the weapon (e.g., "Handgun", "Knife", "None"). Status: Case status, such as IC (Investigation Continued) or AA (Adult Arrest). Status Desc: Description of the case status, e.g., "Investigation Continued" or "Adult Arrest". Crm Cd 1 - Crm Cd 4: Additional crime codes, if multiple offenses occurred in the same incident. LOCATION: Nearest street address where the crime occurred. Cross Street: Cross street (if available) for additional location context. LAT Latitude: of the crime location. LON Longitude: of the crime location.

License & Attribution

Source: Los Angeles Police Department (LAPD) Terms of Use: This dataset follows specific non-federal licensing rules different from Data.gov. Attribution: If you use this dataset, please credit LAPD & Data.gov.

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Archived as of 11/14/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. This dataset is the underlying data for the Public Safety portion of the Equity Data Portal displaying Indiana's total arrests by demographics. This data is from the Criminal History Records Information System (CHRIS), which comes from three main sources. Arrest data comes from the Live Scan system, which is used for finger printing and capturing other pertinent information at the time of the arrest. Criminal disposition data are maintained by prosecutors in the ProsLink system, and by courts in the Odyssey system. Arrest county is determined by the location of the booking agency. If the booking agency is missing, then the arresting agency is used. The % of IN Population will not equal 100% because we are excluding non-represented racial category "Two or More Races," which accounts for ~1.7% of Indiana's population. Because some arrests are not included in the individual race categories shown here, total counts and percentages from the individual race categories add up to less than the totals for “All” races. This dashboard uses 2010 Census data.