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...

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.

When incidents happened, information about offenders, the victim’s perception of the incident, and what items were stolen. Annual data from the Crime Survey for England and Wales (CSEW).

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.

When incidents happened, information about offenders, the victim’s perception of the incident, injuries sustained, use of weapons and if the offender was under the influence of alcohol and drugs. Annual data from the Crime Survey for England and Wales (CSEW).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was a secondary analysis of data from the National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) for the period 1998-2007. The analysis calculates two separate measures of the incidents of violence that occurred during burglaries. The study addressed the following research questions: Is burglary a violent crime? Are different levels of violence associated with residential versus nonresidential burglaries? How frequently is a household member present during a residential burglary? How frequently does violence occur in the commission of a burglary? What forms does burglary-related violence take? Are there differences in rates of violence between attempted and completed burglaries? What constitutes the crime of burglary in current statutory law? How do the federal government and the various states define burglary (grades and elements)? Does statutory law comport with empirical observations of what the typical characteristics of acts of burglary are? The SPSS code distributed here alters an existing dataset drawn from pre-existing studies. In order to use this code users must first create the original data file drawn from National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) data from the period of 1998-2007. All data used for this study are publicly available through ICPSR. See the variable description section for a comprehensive list of, and direct links to, all datasets used to create this original dataset.

This dataset includes all criminal offenses reported to the Colorado Springs Police Department. Each case report (incident) may have several offenses. Each offense may have multiple suspects and/or victims.

Important: This dataset provided by CSPD does not apply the same counting rules as official data reported to the Colorado Bureau of Investigations and the Federal Bureau of Investigation. This means comparisons to those datasets would be inaccurate.

📘 About the Dataset

This dataset contains detailed records of crimes reported to the Los Angeles Police Department (LAPD) from 2020 to the present. It includes information about the type of crime, when and where it occurred, the victim’s demographic details, the weapon used (if any), and the status of the investigation. The dataset is useful for: Crime trend analysis Victim-focused studies Location-based risk assessment Vehicle-related crime insights Understanding contributing factors such as weapons, MO codes, and case status It also helps build projects related to data analytics, machine learning, pattern detection, risk forecasting, and urban safety studies.

📂 What the Dataset Includes Key columns include: DATE OCC & DATE RPTD – When the crime happened and when it was reported Crm Cd / Crime Category – Type of crime AREA / Area Name – LAPD division where the incident occurred Victim Age, Gender, and Descent Weapon Used (if applicable) MO Codes – Method of Operation, describing how the crime was carried out Premise Code – Location type (street, residence, parking lot, etc.) Status – Case outcome (Investigation Continued, Adult Arrest, etc.) Vehicle-related fields (for theft and break-ins)

⭐ Why This Dataset Is Valuable Covers millions of crime records over multiple years Updated regularly by the LAPD Suitable for EDA, visualization, predictive modeling, and geospatial analysis Granular information helps identify patterns across time, location, victims, and crime methods

🔗 Original Source

This dataset is sourced from the U.S. Government open data portal: https://catalog.data.gov/dataset/crime-data-from-2020-to-present

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.

Women's Crimes in India

Characteristics, Frequency, and Motives

By Rajanand Ilangovan [source]

About this dataset

This dataset contains extensive information about various types of crimes that happened in India from 2001 to 2019. Using this dataset, one can gain a deep insight into the crime trend and various factors that can be identified for analysing it. From Area_Name, Year, Sub_Group and CPA Cases Registered to Persons Acquitted- This dataset covers almost every single aspect of Crime against women in India while also giving a glance at other related aspects such as Auto-Theft Coordinated or Traced and Trials completed by courts. It is immensely helpful in understanding the crime patterns of India over time and make predictions accordingly

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How to use the dataset

Using this dataset, we can gain unparalleled insight into the prevalence and distribution of crimes against women over this period in different parts across India as well as within each state. This could be used for further research into the social impact on certain areas with heightened crime rates or for governmental organizations striving for initiatives to combat such criminal activities.

Research Ideas

• Analyzing patterns in violent crimes against women and children, such as the number of reported cases, total convictions and acquittals.
• Examining trends in different types of crime by state or city over time to identify hotspots or regional crime issues.
• Comparing police personnel performance to analyze effectiveness of action taken against certain types of crime in different areas over time

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - 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.

Columns

File: 25_Complaints_against_police.csv | Column name | Description | |:--------------------------------------------------------------------|:-------------------------------------------------------------------------------| | Area_Name | Name of the area where the crime was committed. (String) | | Year | Year in which the crime was committed. (Integer) | | Sub_group | Type of crime committed. (String) | | CPA_-_Cases_Registered | Number of cases registered in the given year. (Integer) | | CPA_-_Cases_Reported_for_Dept._Action | Number of cases reported to the department for action. (Integer) | | CPA_-_Complaints/Cases_Declared_False/Unsubstantiated | Number of complaints/cases declared false or unsubstantiated. (Integer) | | CPA_-_Complaints_Received/Alleged | Number of complaints received or alleged. (Integer) | | CPA_-_No_of_Departmental_Enquiries | Number of departmental enquiries. (Integer) | | CPA_-_No_of_Magisterial_Enquiries | Number of magisterial enquiries. (Integer) | | CPA-_Cases_Sent_for_Trials/Charge-sheeted | Number of cases sent for trial or charge-sheeted. (Integer) | | CPA-_No_of_Judicial_Enquiries | Number of judicial enquiries. (Integer) | | CPB_-_Police_Personnel_Acquitted | Number of police personnel acquitted. (Integer) | | CPB_-_Police_Personnel_Convicted ...

This dataset provides the crime clearance rate nationally and for the City of Tempe. An overall clearance rate is developed as part of the Department’s report for the Federal Bureau of Investigation (FBI) Uniform Crime Report (UCR) Program. The statistics in the UCR Program are based on reports the Tempe Police Department officially submits to the Arizona Department of Public Safety (DPS).In the UCR Program, there are two ways that a law enforcement agency can report that an offense is cleared:(1) cleared by arrest or solved for crime reporting purposes, or(2) cleared by exceptional means.An offense is cleared by arrest, or solved for crime reporting purposes, when three specific conditions have been met. The three conditions are that at least one person has been: (1) arrested; (2) charged with the commission of the offense; and (3) turned over to the court for prosecution.In some situations, an agency may be prevented from arresting and formally charging an offender due to factors outside of the agency's control. In these cases, an offense can be cleared by exceptional means, if the following four conditions are met: (1) identified the offender; (2) gathered enough evidence to support an arrest, make a charge, and turn over the offender to the court for prosecution; (3) identified offender’s exact location so that suspect can immediately be taken into custody; and (4) encountered a circumstance outside law enforcement"s control that prohibits arresting, charging and prosecuting the offender.The UCR clearance rate is one tool for helping the police to understand and assess success at investigating crimes. However, these rates should be interpreted with an understanding of the unique challenges faced in reporting and investigating crimes. Clearance rates for a given year may be greater than 100% because a clearance is reported for the year the clearance occurs, which may not be the same year that the crime occurred. Often, investigations may take months or years, resulting in cases being cleared years after the actual offense. Additionally, there may be delays in the reporting of crimes, which would push the clearance of the case out beyond the year it happened.This page provides data for the Violent Cases Clearance Rate performance measure. The performance measure dashboard is available at 1.12 Violent Cases Clearance Rate.Additional InformationSource: Tempe Police Department (TPD) Versadex Records Management System (RMS) submitted to Arizona Department of Public Safety (AZ DPS), which submits data to the Federal Bureau of Investigation (FBI)Contact (author): Contact E-Mail (author): Contact (maintainer): Brooks LoutonContact E-Mail (maintainer): Brooks_Louton@tempe.govData Source Type: ExcelPreparation Method: Drawn from the Annual FBI Crime In the United States PublicationPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

NOTICE: This dataset is not currently updating correctly and does not reflect current Crime Statistics. We hope to get this technical issue resolved shortly. In the interim, please direct all data requests to the Syracuse FOIL team. https://www.syr.gov/Departments/Law/FOIL Dated: 7/21/2025This 2025 crime data is the list of calls for service that the Syracuse Police Department responded to in 2025. These records does not include rape offenses as well as any crimes that have been sealed by the court. These records are derived from the records management system utilized by the SPD. The data is then anonymized by SPD Crime Analysts weekly. After this data is received weekly from the SPD, this data is then mapped to the approximate location of that incident, using the 100 block level and a Geolocator File from Onondaga County GIS Department. This data is then updated on the Open Data Portal. The points should not be construed to be the exact point this incidents were reported to occur, rather the block where these incident is reported to occur. Crimes are reported to the FBI in two major categories under the Uniform Crime Reports specification: Part 1 and Part 2 crimes. Part 1 crimes include criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. In these records, rape offenses have been excluded due to victim privacy concerns. Part 2 crimes include all other offenses. A more detailed guide to Part 1 crimes is listed below. More details about Part 2 Crimes is listed in the Part 2 Crimes Dataset. When using the data, the date and time provided are when the crime was actually reported. This means that though a larceny might be reported at noon, the actual crime could have happened at 8am, but was not realized until someone noticed hours later. Similarly, if a home break-in happens during a holiday weekend when the owners are out of town, the crime report may not come in until they return home and notice the crime took place previously. The address in the dataset is where the crime occurred. The location is also anonymized to the block level, so a crime that occurred at 123 Main St. will appear as occurring on the 100 block of Main St. This is to protect the privacy of all involved. Finally, information about crimes is fluid, and details about the crime could change.Data Dictionary Date End -Date that the crime was reported. It could have happened earlier. This is in the format of DD-MON-YY (Ex. 01-Jan-22). Time start and time end -Listed in military time (2400) - Burglaries and larcenies are often a time frame. Address- Where the crime occurred. All addresses are in the 100’s because the Syracuse Police Department allows privacy for residents and only lists the block number. Code Defined-Offense names are listed as crime categories group for ease of understanding. There may have been other offenses also, but the one displayed is the highest Unified Crime Reporting (UCR) category. Arrest- Means that there was an arrest, but not necessarily for that crime. Larceny Code- Indicates the type of larceny (Example: From Building or From Motor Vehicle). LAT - The approximate latitude (not actual) that this call for service occurred. LONG- The approximate latitude (not actual) that this call for service occurred.DisclaimerData derived from the Syracuse Police Department record management system, any data not listed is not currently available. Part I Crime DefinitionsCriminal homicide—a.) Murder and non-negligent manslaughter: the willful (non-negligent) killing of one human being by another. Deaths caused by negligence, attempts to kill, assaults to kill, suicides, and accidental deaths are excluded. The program classifies justifiable homicides separately and limits the definition to: (1) the killing of a felon by a law enforcement officer in the line of duty; or (2) the killing of a felon, during the commission of a felony, by a private citizen. b.) Manslaughter by negligence: the killing of another person through gross negligence. Deaths of persons due to their own negligence, accidental deaths not resulting from gross negligence, and traffic fatalities are not included in the category Manslaughter by Negligence. Robbery—The taking or attempting to take anything of value from the care, custody, or control of a person or persons by force or threat of force or violence and/or by putting the victim in fear. Aggravated assault—An unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury. This type of assault usually is accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Simple assaults are excluded. Burglary(breaking or entering)—The unlawful entry of a structure to commit a felony or a theft. Attempted forcible entry is included. Larceny-theft (except motor vehicle theft)—The unlawful taking, carrying, leading, or riding away of property from the possession or constructive possession of another. Examples are thefts of bicycles, motor vehicle parts and accessories, shoplifting, pocket picking, or the stealing of any property or article that is not taken by force and violence or by fraud. Attempted larcenies are included. Embezzlement, confidence games, forgery, check fraud, etc., are excluded. Motor vehicle theft—The theft or attempted theft of a motor vehicle. A motor vehicle is self-propelled and runs on land surface and not on rails. Motorboats, construction equipment, airplanes, and farming equipment are specifically excluded from this category. Dataset Contact Information: Organization: Syracuse Police Department (SPD)Position: Data Program ManagerCity: Syracuse, NY E-Mail Address: opendata@syrgov.net

This table contains figures on the number of registered crimes per month and per year. These are broken down by type of crime and by municipality/neighbourhood. Attempts are also included in the registered crimes. For some crimes (for example, murder/manslaughter) this yields a much higher number than just the number of completed crimes. The data per municipality are presented for all years according to the municipal classification of 1 January 2022.

Since July 2018, it is no longer possible to record multiple offenses that are related to each other (concurrence) in one registration. An example of this is a street robbery in which a firearm (possession of weapons) is used. If several offenses occur in one registration, only the most serious offense was counted before July 2018. As a result of this adjustment, a number of offenses show an increase compared to 2018. This mainly concerns domestic trespassing, special laws including money laundering, arms trade including possession of weapons, drug trafficking, violation of public order and social integrity, including insult. The increase was therefore mainly visible in the last 6 months of 2018. This adjustment has only a limited effect on the total number of crimes. For the whole of 2018, this caused an increase of approximately 1.0%. Since April 30, 2020, it is possible to report WhatsApp fraud (also known as friend in need fraud) via the Internet. This was immediately used extensively. In the months of May to December 2020, approximately 20,000 reports of WhatsApp fraud were made.

Reports concern registered crimes for which an 'statement report' has been drawn up. Multiple reports can be made per crime. Internet declarations can only be made for a selected number of offenses and only if there is no indication for an investigation.

Data available from: 2012

Status of the figures: The figures in this table are regularly updated. This may cause minor differences with previous publications. Updating of the figures is necessary, for example, to be able to retroactively process the reclassification of municipalities or adjustment of coding with retroactive effect. Figures on declarations and internet declarations are updated every quarter.

When will new numbers be released? 17 Oktober the figures for August will be added.

TABLE OF CONTENTS

1. Explanation
2. Definitions and Explanation of Symbols
3. Links to relevant tables and articles
4. Resources and Methods
5. More information

1. EXPLANATION

This table contains figures on the number of registered crimes per month and per year. These are broken down by type of crime and by municipality/neighbourhood. Attempts are also included in the registered crimes. For some crimes (for example, murder/manslaughter) this yields a much higher number than just the number of completed crimes. The data per municipality are presented for all years according to the municipal classification of 1 January 2022.

Since July 2018, it is no longer possible to record multiple offenses that are related to each other (concurrence) in one registration. An example of this is a street robbery in which a firearm (possession of weapons) is used. If several offenses occur in one registration, only the most serious offense was counted before July 2018. As a result of this adjustment, a number of offenses show an increase compared to 2018. This mainly concerns domestic trespassing, special laws including money laundering, arms trade including possession of weapons, drug trafficking, violation of public order and social integrity, including insult. The increase was therefore mainly visible in the last 6 months of 2018. This adjustment has only a limited effect on the total number of crimes. For the whole of 2018, this caused an increase of approximately 1.0%. Since April 30, 2020, it is possible to report WhatsApp fraud (also known as friend in need fraud) via the Internet. This was immediately used extensively. In the months of May to December 2020, approximately 20,000 reports of WhatsApp fraud were made.

Reports concern registered crimes for which an 'statement report' has been drawn up. Multiple reports can be made per crime. Internet declarations can only be made for a selected number of offenses and only if there is no indication for an investigation.

Data available from: 2012

Status of the figures:

The figures in this table are regularly updated. This may cause minor differences with previous publications. Updating of the figures is necessary, for example, to be able to retroactively process the reclassification of municipalities or adjustment of coding with retroactive effect. Figures on declarations and internet declarations are updated every quarter.

Changes as of August 15, 2022: Figures for July have been added.

When will new numbers be released? 15 September the figures for August will be added.

2. DEFINITI...

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.

Suburb-based crime statistics for crimes against the person and crimes against property. The Crime statistics datasets contain all offences against the person and property that were reported to police in that respective financial year. The Family and Domestic Abuse-related offences datasets are a subset of this, in that a separate file is presented for these offences that were flagged as being of a family and domestic abuse nature for that financial year. Consequently the two files for the same financial year must not be added together. Data is point in time.

This dataset reflects reported incidents of crime that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. Any use of the information for commercial purposes is strictly prohibited. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily.

This study investigates the factors and conditions in robbery events that cause victim injury or death. The investigators compare three robbery events: those that resulted in death, those that cause injury, and nonfatal robberies of all types. The events were compared on a variety of demographic variables. The data address the following questions: (1) To what extent are homicides resulting from robbery misclassified as homicides for which motives are undetermined? (2) How often do homicides resulting from robbery involve individuals who do not know each other? (3) Are robberies that involve illicit drugs more likely to result in the death of the victim? (4) To what extent does a weapon used in a robbery affect the probability that a victim will die? (5) To what extent does victim resistance affect the likelihood of victim death? (6) To what extent does robbery lead to physical injury? (7) Do individuals of different races suffer disproportionately from injuries resulting from robbery? (8) Are injuries and homicides resulting from robbery more likely to occur in a residence, commercial establishment, or on the street? (9) Are women or men more likely to be victims of homicide or injury resulting from robbery? (10) To what extent does robbery (with or without a homicide) occur between or within races? (12) How long does it take to solve robbery-related crimes? Major variables characterizing the unit of observation, the robbery event, include: location of the robbery incident, numbers of offenders and victims involved in the incident, victim's and offender's prior arrest and conviction histories, the extent of injury, whether or not drugs were involved in any way, type of weapon used, victim/offender relationship, and the extent of victim resistance.

Context

With this dataset I hope to raise awareness on the trends in crime.

Content

For NYPD Complaint Data, each row represents a crime. For information on the columns, please see the attached csv, "Crime_Column_Description". Reported crime go back 5 years but I only attached reported crime from 2014-2015 due to file size. The full report can be found at NYC Open Data (https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i)

Acknowledgements

I would like to thank NYC Open Data for the dataset.

Inspiration

Additional things I would like to better understand: 1. Differences in crime that exist between the 5 boroughs 2. A mapping of the crimes per borough 3. Where do the most dangerous crimes happen and what time?