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.

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.

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.

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).

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

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.

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.

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.

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.

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.

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 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. Firearm counts are derived from taking the number of violent crimes which involve a firearm. Population data are provided every year by the FBI, based on US Census information. 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.

Crime severity index (violent, non-violent, youth) and weighted clearance rates (violent, non-violent), Canada, provinces, territories and Census Metropolitan Areas, 1998 to 2024.

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.

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 study examines the question of how some urban neighborhoods maintain a low crime rate despite their proximity and similarity to relatively high crime areas. The purpose of the study is to investigate differences in various dimensions of the concept of territoriality (spatial identity, local ties, social cohesion, informal social control) and physical characteristics (land use, housing, street type, boundary characteristics) in three pairs of neighborhoods in Atlanta, Georgia. The study neighborhoods were selected by locating pairs of adjacent neighborhoods with distinctly different crime levels. The criteria for selection, other than the difference in crime rates and physical adjacency, were comparable racial composition and comparable economic status. This data collection is divided into two files. Part 1, Atlanta Plan File, contains information on every parcel of land within the six neighborhoods in the study. The variables include ownership, type of land use, physical characteristics, characteristics of structures, and assessed value of each parcel of land within the six neighborhoods. This file was used in the data analysis to measure a number of physical characteristics of parcels and blocks in the study neighborhoods, and as the sampling frame for the household survey. The original data were collected by the City of Atlanta Planning Bureau. Part 2, Atlanta Survey File, contains the results of a household survey administered to a stratified random sample of households within each of the study neighborhoods. Variables cover respondents' attitudes and behavior related to the neighborhood, fear of crime, avoidance and protective measures, and victimization experiences. Crime rates, land use, and housing characteristics of the block in which the respondent resided were coded onto each case record.

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.

This dataset contains official crime records reported in Los Angeles City from January 2020 to December 2023.

The data provides valuable information about reported crimes, including the date, area, crime details, victim information, premises, weapons used, and status.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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Interesting Task Ideas:

1. Analyze the temporal patterns of crimes in different areas of Los Angeles City.
2. Identify the most common crimes.
3. Explore the correlation between victim age and crime types.
4. Investigate the crime rates in different areas of the city.
5. Examine the relationship between premises and specific crime codes.
6. Visualize the distribution of crimes on a map of Los Angeles City.
7. Analyze the relationship between crime status and occurrence location.
8. Use machine learning techniques to predict crime categories based on available features.