This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

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

In 2023, the violent crime rate in the United States was 363.8 cases per 100,000 of the population. Even though the violent crime rate has been decreasing since 1990, the United States tops the ranking of countries with the most prisoners. In addition, due to the FBI's transition to a new crime reporting system in which law enforcement agencies voluntarily submit crime reports, data may not accurately reflect the total number of crimes committed in recent years. Reported violent crime rate in the United States The United States Federal Bureau of Investigation tracks the rate of reported violent crimes per 100,000 U.S. inhabitants. In the timeline above, rates are shown starting in 1990. The rate of reported violent crime has fallen since a high of 758.20 reported crimes in 1991 to a low of 363.6 reported violent crimes in 2014. In 2023, there were around 1.22 million violent crimes reported to the FBI in the United States. This number can be compared to the total number of property crimes, roughly 6.41 million that year. Of violent crimes in 2023, aggravated assaults were the most common offenses in the United States, while homicide offenses were the least common. Law enforcement officers and crime clearance Though the violent crime rate was down in 2013, the number of law enforcement officers also fell. Between 2005 and 2009, the number of law enforcement officers in the United States rose from around 673,100 to 708,800. However, since 2009, the number of officers fell to a low of 626,900 officers in 2013. The number of law enforcement officers has since grown, reaching 720,652 in 2023. In 2023, the crime clearance rate in the U.S. was highest for murder and non-negligent manslaughter charges, with around 57.8 percent of murders being solved by investigators and a suspect being charged with the crime. Additionally, roughly 46.1 percent of aggravated assaults were cleared in that year. A statistics report on violent crime in the U.S. can be found here.

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.

These data on 19th- and early 20th-century police department and arrest behavior were collected between 1975 and 1978 for a study of police and crime in the United States. Raw and aggregated time-series data are presented in Parts 1 and 3 on 23 American cities for most years during the period 1860-1920. The data were drawn from annual reports of police departments found in the Library of Congress or in newspapers and legislative reports located elsewhere. Variables in Part 1, for which the city is the unit of analysis, include arrests for drunkenness, conditional offenses and homicides, persons dismissed or held, police personnel, and population. Part 3 aggregates the data by year and reports some of these variables on a per capita basis, using a linear interpolation from the last decennial census to estimate population. Part 2 contains data for 267 United States cities for the period 1880-1890 and was generated from the 1880 federal census volume, REPORT ON THE DEFECTIVE, DEPENDENT, AND DELINQUENT CLASSES, published in 1888, and from the 1890 federal census volume, SOCIAL STATISTICS OF CITIES. Information includes police personnel and expenditures, arrests, persons held overnight, trains entering town, and population.

In 2023, ********* property and violent crimes were reported in California - the most out of any state. Texas followed behind, with ******* reported crimes. However, as the FBI estimates national trends of crime by asking law enforcement agencies across the country to self-report their crime data, the reported number of crimes committed in each state is dependent upon whether they provided the information to the Bureau's crime reporting system. For example, the state of Florida reported only *** percent of their crime data in 2022, raising the question of whether Florida has again failed to report the majority of their crimes in 2023 and if they should be higher up on this list. As many states have neglected to report all of their crime data to the FBI in the last few years, the total numbers may not accurately represent the number of crimes committed in each state.

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.

The violent crime rate indicator includes both the total number of violent crime incidents per year in Champaign County, and the number of violent crime incidents per 100,000 people per year in Champaign County. “Violent crimes” are those counted in the following categories in the Illinois State Police’s annual Crime in Illinois report: Criminal Homicide, Criminal Sexual Assault (Rape), Robbery, Aggravated Assault, and Aggravated Battery. The incidence of violent crime is an integral part of understanding the safety of a given community.

Both the total number of offenses in Champaign County and the rate per 100,000 population were significantly lower in 2021 than at the start of the measured time period, 1996. The most recent rise in both of these figures was in 2019-2020, before falling again in 2021. The year with the lowest number of total offenses and the rate per 100,000 population in the study period was 2015; both measures are slightly higher since then.

This data is sourced from the Illinois State Police’s annually released Crime in Illinois: Annual Uniform Crime Report, available on the Uniform Crime Report Index Offense Explorer.

Sources: Illinois State Police. (2021). Crime in Illinois: Annual Uniform Crime Report 2021. Illinois State Police. (2020). Crime in Illinois: Annual Uniform Crime Report 2020. Illinois State Police. (2019). Crime in Illinois: Annual Uniform Crime Report 2019. Illinois State Police. (2018). Crime in Illinois: Annual Uniform Crime Report 2018. Illinois State Police. (2017). Crime in Illinois: Annual Uniform Crime Report 2017.Illinois State Police. (2016). Crime in Illinois: Annual Uniform Crime Report 2016. Illinois State Police. (2015). Crime in Illinois: Annual Uniform Crime Report 2015. Illinois State Police. (2014). Crime in Illinois: Annual Uniform Crime Report 2014.; Illinois State Police. (2012). Crime in Illinois: Annual Uniform Crime Report 2012.; Illinois State Police. (2011). Crime in Illinois: Annual Uniform Crime Report 2010-2011.; Illinois State Police. (2009). Crime in Illinois: Annual Uniform Crime Report 2009.; Illinois State Police. (2007). Crime in Illinois: Annual Uniform Crime Report 2007.; Illinois State Police. (2005). Crime in Illinois: Annual Uniform Crime Report 2005.; Illinois State Police. (2003). Crime in Illinois: Annual Uniform Crime Report 2003.; Illinois State Police. (2001). Crime in Illinois: Annual Uniform Crime Report 2001.; Illinois State Police. (1999). Crime in Illinois: Annual Uniform Crime Report 1999.; Illinois State Police. (1997). Crime in Illinois: Annual Uniform Crime Report 1997.

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 Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs' departments. DCJS compiles these reports as New York's official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2020 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.

The property crime rate indicator includes both the total number of property crime incidents per year in Champaign County, and the number of property crime incidents per 100,000 people per year in Champaign County. “Property crimes” are those counted in the following categories in the Illinois State Police’s annual Crime in Illinois report: Burglary, Theft (Larceny), Motor Vehicle Theft, and Arson. Like violent crime, property crime is also a major indicator of community safety.

The property crime data spans the same time period as the violent crime data: 1996 to 2021. The total number of offenses and rate per 100,000 population are both substantially lower as of 2021 than at the beginning of the study period in 1996. 2021 actually saw the lowest number of offenses and the lowest rate per 100,000 population in the study period. There are significantly more property crime offenses in Champaign County than violent crime incidents.

This data is sourced from the Illinois State Police’s annually released Crime in Illinois: Annual Uniform Crime Report, available on the Uniform Crime Report Index Offense Explorer.

Sources: Illinois State Police. (2021). Crime in Illinois: Annual Uniform Crime Report 2021. Illinois State Police. (2020). Crime in Illinois: Annual Uniform Crime Report 2020. Illinois State Police. (2019). Crime in Illinois: Annual Uniform Crime Report 2019. Illinois State Police. (2018). Crime in Illinois: Annual Uniform Crime Report 2018. Illinois State Police. (2017). Crime in Illinois: Annual Uniform Crime Report 2017. Illinois State Police. (2018). Crime in Illinois: Annual Uniform Crime Report 2018. Illinois State Police. (2017). Crime in Illinois: Annual Uniform Crime Report 2017. Illinois State Police. (2016). Crime in Illinois: Annual Uniform Crime Report 2016. Illinois State Police. (2015). Crime in Illinois: Annual Uniform Crime Report 2015. Illinois State Police. (2014). Crime in Illinois: Annual Uniform Crime Report 2014.; Illinois State Police. (2012). Crime in Illinois: Annual Uniform Crime Report 2012.; Illinois State Police. (2011). Crime in Illinois: Annual Uniform Crime Report 2010-2011.; Illinois State Police. (2009). Crime in Illinois: Annual Uniform Crime Report 2009.; Illinois State Police. (2007). Crime in Illinois: Annual Uniform Crime Report 2007.; Illinois State Police. (2005). Crime in Illinois: Annual Uniform Crime Report 2005.; Illinois State Police. (2003). Crime in Illinois: Annual Uniform Crime Report 2003.; Illinois State Police. (2001). Crime in Illinois: Annual Uniform Crime Report 2001.; Illinois State Police. (1999). Crime in Illinois: Annual Uniform Crime Report 1999.; Illinois State Police. (1997). Crime in Illinois: Annual Uniform Crime Report 1997.

This project examined different aspects of campus crime -- specifically, the prevalence of crimes among college students, whether the crime rate was increasing or decreasing on college campuses, and the factors related to campus crime. Researchers made the assumption that crimes committed by and against college students were likely to be related to drug and alcohol use. Specific questions designed to be answered by the data include: (1) Do students who commit crimes differ in their use of drugs and alcohol from students who do not commit crimes? (2) Do students who are victims of crimes differ in their use of drugs and alcohol from students who are not victims? (3) How do multiple offenders differ from single offenders in their use of drugs and alcohol? (4) How do victims of violent crimes differ from victims of nonviolent crimes in their use of drugs and alcohol? (5) What types of student crimes are more strongly related to drug or alcohol use than others? (6) Other than drug and alcohol use, in what ways can victims and perpetrators of crimes be differentiated from students who have had no direct experiences with crime? Variables include basic demographic information, academic information, drug use information, and experiences with crime since becoming a student.

***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.

Violent Crime in CA

Regional, County, City/Town Rates 2006-2010

By Health [source]

About this dataset

This dataset contains information on the rate of violent crime across California - its regions, counties, cities and towns. The data was collected as part of a larger effort by the Office of Health Equity to better understand public health indicators and ensure equitable outcomes for all.

The numbers reflect more than just a problem in California communities - it reflects a problem with unequal access to resources and opportunity across race, ethnicities and geographies. African Americans in California are 11 times more likely to die from assault or homicide compared to white Californians. Similarly, certain regions report higher crime rates than others at the county level- indicating underlying issues with poverty or institutionalized inequality.

Law enforcement agencies teamed up with the Federal Bureau of Investigations’ Uniform Crime Reports to collect this data table which includes details such as reported number of violent crimes (numerator), population size (denominator), rate per 1,000 population (ratex1000) confidence intervals (LL_95CI & UL_95CI ) standard errors & relative standard errors (se & rse) as well as ratios between city/town rates vs state rates (RR_city2state). Additionally, each record is classified according to region name/code and race/ethnicity code/name , giving researchers further insight into these troubling statistics at both macro and micro levels.

Armed with this information we can explore new ways identify inequitable areas and begin looking for potential solutions that combat health disparities within our communities like never before!

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How to use the dataset

The data is presented with twenty columns providing various segments within each row including:

• Crime definition
• Race/ethnicity code
• Region code
• Geographic area identifier
• Numerator and Denominator values of population
• Standard Error and 95% Confidence Intervals
• Relatvie Standard Error (RSE) value

• Ratios related to city/towns rate to state rate

  The information provided can be used for a variety of applications such as creating visualizations or developing predictive models. It is important to note that rates are expressed per 1,000 population for their respective geographic area during each period noted by the report year field within the dataset. Additionally CA_decile column may be useful in comparing counties due numerical grading system identifying a region’s percentile ranking when compared to other counties within the current year’s entire dataset as well as ratios present under RR_city2state which presents ratio comparison between city/town rate and state rate outside given geographic area have made this an extremely valuable dataset for further analysis

Research Ideas

• Developing a crime prediction and prevention program that uses machine learning models to identify criminal hotspots and direct resources to those areas
• Exploring the connection between race/ethnicity and rates of violence in California
• Creating visualizations and interactive maps to display types of violent crime across different counties within California

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Violent_Crime_Rate_California_2006-2010-DD.csv

File: rows.csv | Column name | Description ...

This study focused on the effect of economic resources and racial/ethnic composition on the change in crime rates from 1970-2004 in United States cities in metropolitan areas that experienced a large growth in population after World War II. A total of 352 cities in the following United States metropolitan areas were selected for this study: Atlanta, Dallas, Denver, Houston, Las Vegas, Miami, Orange County, Orlando, Phoenix, Riverside, San Bernardino, San Diego, Silicon Valley (Santa Clara), and Tampa/St. Petersburg. Selection was based on the fact that these areas developed during a similar time period and followed comparable development trajectories. In particular, these 14 areas, known as the "boomburbs" for their dramatic, post-World War II population growth, all faced issues relating to the rapid growth of tract-style housing and the subsequent development of low density, urban sprawls. The study combined place-level data obtained from the United States Census with crime data from the Uniform Crime Reports for five categories of Type I crimes: aggravated assaults, robberies, murders, burglaries, and motor vehicle thefts. The dataset contains a total of 247 variables pertaining to crime, economic resources, and race/ethnic composition.

This dataset was compiled by the Illinois Criminal Justice Information Authority (ICJIA) at the request of the Governor’s Children’s Cabinet. This data contains the population of youth ages 13-26 in each county, the total population of each county, and the number and rate of index crimes reported, with domestic violence offenses and rates reported separately for every year between 2006 and 2015.

For the purpose of this analysis the crime data was gathered from the Illinois State Police Annual report Crime in Illinois. This publication is produced by the Illinois State Police every year using the UCR data that is submitted to them by individual jurisdictions throughout the state. The accuracy of this data presented is dependent on the local jurisdictions reporting their index crime and domestic violence offenses to ISP, so it can be included in the annual report.

Therefore, if there is large decrease in number of index crimes reported in the dataset it is likely that one or more jurisdictions did not report data for that year to ISP. If there is a large increase from year to year within a county it is likely that a jurisdiction within the county, who previously had not reported crime data, did report crime data for that year. If there is no reported crime in a certain year that means no jurisdictions, or a small jurisdiction with no crime from that county reported data to the Illinois State Police. The annual Crime in Illinois reports can be found on the ISP website www.isp.state.il.us.

A direct link to that annual reports is: http://www.isp.state.il.us/crime/ucrhome.cfm#anlrpts.

The Illinois Criminal Justice Information Authority did not record the data that is expressed in the dataset. ICJIA simply used the ISP reports to compile that yearly crime data into one chart that could be provided to the Illinois Governor’s Children’s Cabinet. This data set has be critically examined to be accurate according to the annual Crime in Illinois Reports. If there are issues with the data set provided please contact the Illinois State Police or the individual jurisdictions within a specific county.

**Index offenses do not include every crime event that occurs. Prior to 2014 there were 8 index crimes reported by the Illinois State Police in their annual reports, Criminal Homicide, Rape, Robbery, Aggravated Battery/Aggravated Assault, Burglary, Theft, Motor Vehicle Theft, and Arson. In 2014 there were two new offenses added to the list of index crimes these were Human Trafficking – Commercial Sex Acts and Human Trafficking – Involuntary Servitude. These are the index crimes that are recorded in the chart provided.

**“Domestic offenses are defined as offenses committed between family or household members. Family or household members include spouses; former spouses; parents; children; foster parents; foster children; legal guardians and their wards; stepchildren; other persons related by blood (aunt, uncle, cousin) or by present or previous marriage (in-laws); persons who share, or formerly shared, a common dwelling; persons who have, or allegedly have, a child in common; persons who share, or allegedly share, a blood relationship through a child; persons who have, or have had, a dating or engagement relationship; and persons with disabilities, their personal care assistants, or care givers outside the context of an employee of a public or private care facility. Every offense that occurs, when a domestic relationship exists between the victim and offender, must be reported (Illinois State Police).”

**“Offenses reported are not limited to domestic battery and violations of orders of protection; offenses most commonly associated with domestic violence (Illinois State Police).”

The crime rate was compiled using the total population, and the index crime. The Index crime whether all crime or Domestic Violence crime was divided by the total population then multiplied by 10,000, hence crime rate per 10,000.

The sources of data are the Illinois Uniform Crime Reporting Program and the U.S. Census Bureau.

The source of the description is the Illinois State Police and their Reporting guidelines and forms.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

This Dataset contains year, US state, offender race, offender ethnicity, offence name, bias description and victim type-wise total number of hate crime individual victims and offenders in USA

GapMaps offers advanced and reliable Crime Risk Location Data sourced from Applied Geographic Solutions (AGS), a trusted provider of premium demographic insights with over 20 years of experience. Leveraged by thousands of businesses, AGS use advanced statistical methodologies and a rolling seven-year database of FBI and local agency statistics to provide a highly accurate view of the relative risk of specific crime types for any geographic area empowering organizations to make informed decisions in areas such as insurance, urban planning, and real estate.

The AGS Crime Risk dataset includes: - Standardised indexes for a range of serious crimes against both persons and property such as murder, rape, robbery, assault, burglary, theft, and motor vehicle theft - Aggregate measures of crime risk, including crimes against persons, crimes against property, and overall crime risk, offering a comprehensive overview of an area’s safety. - 5-Year Projections: Added in 2020, these projections enhance the dataset by forecasting future crime risks, providing valuable insights for long-term planning. - High-Resolution Data: Crime risk indexes are available at the block group level, allowing insurers to identify variations in crime risk across specific land uses such as motor vehicle theft from parking structures.

Use cases: 1. Insurance underwriting and risk mitigation. 2. Evaluating the security measures needed to protect employees and customers at retail facilities. 3. The study of the effects of neighborhood crime on wellness and health care outcomes.

Methodology: Crime is tracked for multiple years using both FBI aggregate crime reports and for many parts of the country at the individual incident level. A complex set of statistical models are used to estimate and forecast risk of each individual crime type by using land use data in conjunction with demographic and business characteristics.