Each quarter, ACT Policing issues crime statistics illustrating the offences reported or becoming known in suburbs across Canberra.

The selected offences highlighted in the statistics include: assault, sexual offences, robbery, burglary, motor vehicle theft, other theft (such as shoplifting and fraud) and property damage. It is important to note that these numbers may fluctuate as new complainants come forward, more Traffic Infringement Notices are downloaded into the system, or when complaints are withdrawn.

It should also be noted that the individual geographical areas will not combine to the ACT totals due to the exclusion of rural sectors and other regions.

It is important for the community to understand there may be a straight-forward explanation for a spike in offences in their neighbourhood.

For example, sexual offences in Narrabundah increased from two in the January to March last year, to 32 in the first quarter of 2012. These 32 sexual offences relate to one historical case which was reported to police in January 2012, and which has since been finalised.

The smaller the number of reported offences involved, the greater the chance for a dramatic percentage increase.

An interactive crime map is also available on the ACT Policing website https://www.policenews.act.gov.au/crime-statistics-and-data/crime-statistics

In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.

Overview:

This project aims to investigate the potential correlation between the Gross Domestic Product (GDP) of approximately 190 countries for the years 2021 and 2023 and their corresponding crime ratings. The crime ratings are represented on a scale from 0 to 10, with 0 indicating minimal or null crime activity and 10 representing the highest level of criminal activity.

Dataset:

The dataset used in this project comprises GDP data for the years 2021 and 2023 for around 190 countries, sourced from reputable international databases. Additionally, crime rating scores for the same countries and years are collected from credible sources such as governmental agencies, law enforcement organizations, or reputable research institutions.

Methodology:

• Data Collection: GDP data for 2021 and 2023, along with crime rating scores, are gathered for approximately 190 countries.
• Data Preprocessing: The collected data is cleaned and standardized to ensure consistency and compatibility across different datasets.
• Analysis: Statistical methods and data visualization techniques are employed to explore the potential relationship between GDP and crime ratings.
• Interpretation: Findings from the analysis are interpreted to determine the strength and direction of any observed correlations between GDP and crime ratings.
• Conclusion: Based on the analysis results, conclusions are drawn regarding the existence and significance of the relationship between GDP and crime ratings.

Expected Outcomes:

Identification of any significant correlations or patterns between GDP and crime ratings across different countries. Insights into the potential socioeconomic factors influencing crime rates and their relationship with economic indicators like GDP. Implications for policymakers, law enforcement agencies, and researchers in understanding the dynamics between economic development and crime prevalence.

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.

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 includes Pittsburgh Bureau of Police crime incidents.

The Monthly Criminal Activity Dashboard can utilize this data: Monthly Criminal Activity Dashboard

This data follows the National Incident-Based Reporting System (NIBRS) reporting standard. More detail can be found here: https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs

Similar data was previously published at Police Incident Blotter (Archive): https://data.wprdc.org/dataset/uniform-crime-reporting-data

Recorded crime figures for CSP areas. Number of offences for the last two years, percentage change, and rates per 1,000 population for the latest year.

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

🌍 Dataset Description

This dataset provides detailed information about criminal incidents, capturing various characteristics of both the offenders and victims. It includes records of crimes along with demographic details such as age, gender, race, and the status of the individuals involved. The data also contains information on the disposition of the case (whether it was closed or open) and the nature of the crime.

The dataset covers a wide range of crime categories such as theft, vandalism, violence, sexual crimes, and drug/weapon-related offenses. This allows for an in-depth analysis of criminal activities, their impact on different demographics, and potential correlations between various factors such as age, gender, and the type of crime committed.

📊 Columns:

• Disposition: The current status of the case (Closed or Open).
• OffenderStatus: The status of the offender (e.g., ARRESTED).
• Offender_Race: The race of the offender (e.g., BLACK, WHITE, ASIAN, etc.).
• Offender_Gender: The gender of the offender (MALE or FEMALE).
• Offender_Age: The age of the offender (numerical value).
• PersonType: Type of person involved in the case (e.g., VICTIM, REPORTING PERSON, INTERVIEW).
• Victim_Race: The race of the victim (e.g., BLACK, WHITE, ASIAN, etc.).
• Victim_Gender: The gender of the victim (MALE or FEMALE).
• Victim_Age: The age of the victim (numerical value).
• Victim_Fatal_Status: Indicates if the victim’s injuries were fatal or non-fatal.
• Report Type: Type of report filed (e.g., Supplemental Report, Incident Report).
• Category: The category of crime (e.g., Theft, Vandalism, Violence, etc.).

🔍 Use Cases:

This dataset is ideal for analyzing criminal incidents, studying the relationship between various demographic factors and crime types, and performing predictive modeling for crime occurrence. It is useful for investigating crime patterns and trends, assessing how crime impacts different groups, and can assist in law enforcement resource allocation and policy-making. The data can also be utilized in machine learning applications to classify or predict crime outcomes based on offender and victim details.

Translated and Tidy dataset of official crime stats for the Country of Mexico.

Data Card for Mexican Crime Statistics Dataset

Title: Mexican Crime Statistics: Comprehensive Incident Dataset

Subtitle: An Extensive Compilation of Criminal Incidents in Mexico, Sourced from Official Government Data

Source: Official Mexican Government Website

Description: This dataset is a compilation of criminal incidents reported across Mexico. It includes detailed records of various criminal activities, offering insights into crime patterns and trends in different regions. The dataset is ideal for analysis in criminology, public policy, and data science.

• Dataset Description:

year: The year when the crime was reported. This is a numeric field representing the calendar year (e.g., 2015).

entity_code: A numeric code representing a specific entity (state or region) within Mexico. Each number corresponds to a unique entity.

entity: The name of the Mexican state or region where the crime occurred. This is a textual field (e.g., Aguascalientes).

affected_legal_good: A categorical field describing the broad category of the legal good (i.e., personal or societal interest) affected by the crime. Examples include 'Personal freedom' and 'Sexual freedom and security'.

type_of_crime: A categorical field indicating the general type of crime. This field is more specific than 'affected_legal_good' but less specific than 'subtype_of_crime'. Examples include 'Abduction', 'Sexual abuse', and 'Robbery'.

subtype_of_crime: A further categorization of the type of crime. This field provides more specific details within the general type of crime. Examples include 'Sexual Harassment', 'Simple Rape', and 'Home Burglary'.

modality: The specific nature or method of the crime. This field details how the crime was committed or any specific characteristic that differentiates it within its subtype. Examples include 'With violence', 'Without violence', 'Sexual Bullying'.

month: The month when the crime was reported. This is a textual field representing the month (e.g., January).

count: The number of reported incidents for the specific crime type, subtype, and modality in the given entity and month. This is a numeric field

• Dataset Composition:

Type of Data: Structured data, CSV format Number of Records: Shape (332416, 9) Date Range: 2015-2023 ( up to October) Nov - Dec not release yet

• Use Cases:

Intended Use: Research in criminology, public policy analysis, crime trend analysis Example Analyses: Crime rate trends over time, regional crime analysis, type of crime frequency analysis

• Data Collection Method:

Collection Process: Data aggregated from official crime reports and records maintained by the Mexican government Data Authenticity: Sourced from Gobierno de Mexico

• Data Quality:

Accuracy: Official - part of the Mexican Government's push for openness Completeness: Comprehensive coverage of reported incidents within the specified period Limitations: Possible underreporting or inconsistencies in crime reporting across regions. Nov 2023- Dec 2023 not release yet

• Maintenance Plan:

Update Frequency: Quarterly (or as new data becomes available)

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.

In the late 1970s, the Rand Corporation pioneered a method of collecting crime rate statistics. They obtained reports of offending behavior--types and frequencies of crimes committed--directly from offenders serving prison sentences. The current study extends this research by exploring the extent to which variation in the methodological approach affects prisoners' self-reports of criminal activity. If the crime rates reported in this survey remained constant across methods, perhaps one of the new techniques developed would be easier and/or less expensive to administer. Also, the self-reported offending rate data for female offenders in this collection represents the first time such data has been collected for females. Male and female prisoners recently admitted to the Diagnostic Unit of the Colorado Department of Corrections were selected for participation in the study. Prisoners were given one of two different survey instruments, referred to as the long form and short form. Both questionnaires dealt with the number of times respondents committed each of eight types of crimes during a 12-month measurement period. The crimes of interest were burglary, robbery, assault, theft, auto theft, forgery/credit card and check-writing crimes, fraud, and drug dealing. The long form of the instrument focused on juvenile and adult criminal activity and covered the offender's childhood and family. It also contained questions about the offender's rap sheet as one of the bases for validating the self-reported data. The crime count sections of the long form contained questions about motivation, initiative, whether the offender usually acted alone or with others, and if the crimes recorded included crimes against people he or she knew. Long-form data are given in Part 1. The short form of the survey had fewer or no questions compared with the long form on areas such as the respondent's rap sheet, the number of crimes committed as a juvenile, the number of times the respondent was on probation or parole, the respondent's childhood experiences, and the respondent's perception of his criminal career. These data are contained in Part 2. In addition, the surveys were administered under different conditions of confidentiality. Prisoners given what were called "confidential" interviews had their names identified with the survey. Those interviewed under conditions of anonymity did not have their names associated with the survey. The short forms were all administered anonymously, while the long forms were either anonymous or confidential. In addition to the surveys, data were collected from official records, which are presented in Part 3. The official record data collection form was designed to collect detailed criminal history information, particularly during the measurement period identified in the questionnaires, plus a number of demographic and drug-use items. This information, when compared with the self-reported offense data from the measurement period in both the short and long forms, allows a validity analysis to be performed.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99

This is a dataset on reported swedish crime statistics from 1950 to 2023 taken from https://bra.se/bra-in-english/home/crime-and-statistics/crime-statistics.html

The dataset includes - Total number of crimes - Life and Death crimes - Murder, Manslaughter and lethal assault - Assault/Aggravated Assult - Sexual Offences - Rape, Aggravated Rape - Theft, Robbery, Stealing - Burglary not involving firearm - Burglary in flats, Private Houses - Vehicle theft - Theft out of a motor vehicle - Theft from shops, Department stores etc. - Robbery, Aggravated robbery - Fraud and other acts of dishonesty - Criminal Damage - Narcotics - Driving under the influence

Things to consider:

1 Statistic figures on reported completed murder and manslaughter are higher than the actual number of murder and manslaughter.

2 Year 1950-1984 also including "sexual violation".

3 Year 1965-1967 also including burglary involving fire arms, ammunition and explosives.

4 Including crimes against the Narcotics Regulation year 1950-1983. From 1 July 1983 crimes against the Narcotics Regulation is covered by the Narcotics Drugs (Penal) Act section 5a.

5 Since 1 July 1999 also including driving under the influence of illicit drugs.

6 Four cases of embezzlement reported in 1996 covered appx 24'500 offences.

7 Statistics on crimes against creditors (Penal code ch. 11) and tax crimes are incomplete year 1998 and 1999 due incomplete reporting to Brå.

8 Data for year 1999 and 2000 may have been affected by a system change in year 1999/2000.

9 Excluding data where crime is uncategorized. These data were removed from the statistics from year 2003.

The "2019 Crimes Data" dataset contains information about reported crimes in various regions through the year 2000-2019. This dataset aims to provide insights into crime trends, patterns, and rates for analysis and predictive modeling. The data can be used for exploratory data analysis (EDA), trend identification, and forecasting future crime activities and rates. The dataset is particularly valuable for law enforcement agencies, researchers, and analysts interested in understanding crime dynamics. ***Source of Data*:** The data was collected from official crime statistics sources, police department records, and news articles from reputable sources. All efforts were made to ensure accuracy and reliability in data collection.

Usage: This dataset is suitable for a wide range of analyses, including exploratory data analysis, trend visualization, and predictive modeling. Analysts and researchers can use this data to uncover crime patterns, identify hotspots, and develop models for predicting crime rates in upcoming years. Law enforcement agencies, such as the FBI, can benefit from insights gained through this dataset to improve crime prevention and resource allocation strategies.

Trends in Crime Survey for England and Wales (CSEW) crime (accredited official statistics) and Home Office police recorded crime (official statistics) for England and Wales, by offence type. Also includes more detailed data on crime such as violence, fraud and anti-social behaviour.

The number of criminal offenses recorded in Germany fluctuated during the specified period. In 2023, around 5.94 million crimes were registered, compared to 6.33 million in 2015. The data are based on police criminal statistics, which are compiled by the Federal Criminal Police Office (Bundeskriminalamt). The statistics include all criminal offenses known to the police, as well as attempts to commit a crime. Not included are minor breaches of the law, crimes against the state and traffic offenses.