***Starting on March 7th, 2024, the Los Angeles Police Department (LAPD) will adopt a new Records Management System for reporting crimes and arrests. This new system is being implemented to comply with the FBI's mandate to collect NIBRS-only data (NIBRS — FBI - https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs). During this transition, users will temporarily see only incidents reported in the retiring system. However, the LAPD is actively working on generating new NIBRS datasets to ensure a smoother and more efficient reporting system. *** **Update 1/18/2024 - LAPD is facing issues with posting the Crime data, but we are taking immediate action to resolve the problem. We understand the importance of providing reliable and up-to-date information and are committed to delivering it. As we work through the issues, we have temporarily reduced our updates from weekly to bi-weekly to ensure that we provide accurate information. Our team is actively working to identify and resolve these issues promptly. We apologize for any inconvenience this may cause and appreciate your understanding. Rest assured, we are doing everything we can to fix the problem and get back to providing weekly updates as soon as possible. ** This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

In 2024, about 56 percent of survey respondents said crime was a very serious or extremely serious problem in the United States. However, only 14 percent felt their local area had a serious problem with crime. This is a slight decrease from the previous year, when 17 percent of respondents felt that crime was a serious problem in their area.

In 2023/24 there were 25,205 crimes against public justice recorded by the police in Scotland, with the 2020/21 figure the highest for this type of crime since 2011/12, when there were 26,635 crimes of this type recorded.

This research study analysed the crime rate spatially and it examined the relationship between crime and spatial factors in Saudi Arabia. It reviewed the related literature that has utilised crime mapping techniques, such as Geographic Information Systems (GIS) and remote sensing (RS); these techniques are a basic part of effectively helping security and authority agencies by providing them with a clear perception of crime patterns and a surveillance direction to track and tackle crime. This study analysed the spatial relationships between crime and place, immigration, changes in urban areas, weather and transportation networks. The research study was divided into six parts to investigate the correlation between crime and these factors. The first part of the research study examined the relationship between crime and place across the 13 provinces of Saudi Arabia using GIS techniques based on population density in order to identify and visualise the spatial distributions of national and regional crime rates for drug crimes, thefts, murders, assaults, and alcohol-related and ‘outrageous crimes’ (offences against Islam) over a 10-year period from 2003 to 2012. Social disorganisation theory was employed to guide the study and explain the diversity in crime patterns across the country. The highest rates of overall crimes were identified in the Northern Borders Province and Jizan, which are located in the northern and southern regions of the country, respectively; the eastern area of the country was found to have the lowest crime rate. Most drug offences occurred in the Northern Borders Province and Jizan; high rates of theft were recorded in the Northern Borders Province, Jouf Province and Makkah Province, while the highest rates of homicide occurred in Asir Province. The second part of the research study aimed to determine the trends of overall crime in relation to six crime categories: drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous or sex-related crimes, in Saudi Arabia’s 13 provinces over a 10-year period from 2003 to 2012. The study analysed the spatial and temporal changes of criminal cases. Spatial changes were used to determine the differences over the time period of 2003–2012 to show the provincial rates of change for each crime category. Temporal changes were used to compute the trends of the overall crime rate and crimes in the six categories per 1,000 people per year. The results showed that the overall crime rate increased steadily until 2008; thereafter it decreased in all areas except for the Northern Borders Province and Jizan, which recorded the highest crime rates throughout the study period. We have explained that decrease in terms of changes in wages, support for the unemployed and service improvements, which were factors that previous studies also emphasised as being the primary cause for the decrease. This study includes a detailed discussion to contribute to the understanding of the changes in the crime rates in these categories throughout this period in the 13 provinces of Saudi Arabia. The third part of the research study aimed to explain the effects of immigration on the overall crime rate in the six most significant categories of crime in Saudi Arabia, which are drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous crimes, during a 10-year period from 2003 to 2012, in all 13 administrative provinces. It also sought to identify the provinces most affected by the criminal activities of immigrants during this period. No positive association between immigrants and criminal cases was found. It was clearly visible that the highest rate of overall criminal activities was in the south, north and Makkah areas, where there is a high probability of illegal immigrants. This finding supports the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study’s results provide recommendations to the Saudi government, policy-makers, decision-makers and immigration authorities, which could assist in reducing crimes perpetrated by immigrants. In the fourth part of the research study, urban areas were examined in relation to crime rates. Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased population growth and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanisation and crime are related. However, those studies focused on land uses, types of land use and urban forms, such as the physical features of neighbourhoods, roads, shopping centres and bus stations. It is very important for criminologists and urban planning decision-makers to understand the correlation between urban area expansion and crime. In this research, satellite images were used to measure urban expansion over a 10-year period; the study tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. The findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. Moreover, the results show that the correlations are stronger in areas that have undergone greater urban growth. This study did not evaluate many other factors that might affect the crime rate, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, the quality of neighbourhoods, and the number of police officers. However, this research will be of particular interest to those who aim to use remote sensing to study crime patterns. The fifth part of the research study investigated the impacts of weather on crime rates in two different cities: Riyadh and Makkah. While a number of studies have examined climate influences on crime and human behaviour by investigating the correlation between climate and weather elements, such as temperature, humidity and precipitation, and crime rates, few studies have focused on haze as a weather element and its correlation with crime. This research examined haze as a weather variable to investigate its effects on criminal activity and compare its effects with those of temperature and humidity. Monthly crime data and monthly weather records were used to build a regression model to predict crime cases based on three weather factors using temperature, humidity and haze values. This model was applied to two provinces in Saudi Arabia with different types of climates: Riyadh and Makkah. Riyadh Province is a desert area in which haze occurs approximately 17 days per month on average. Makkah Province is a coastal area where it is hazy an average of 4 days per month. A measurable relationship was found between each of these three variables and criminal activity. However, haze had a greater effect on theft, drug-related crimes and assault in Riyadh Province than temperature and humidity. Temperature and humidity were the efficacious variables in Makkah Province, while haze had no significant influence in that region. Finally, the sixth part of the research study examined the influence of the quality and extent of road networks on crime rates in both urban and rural areas in Jizan Province, Saudi Arabia. We performed both Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) where crime rate was the dependent variable and paved (sealed) roads, non-paved (unsealed/gravel) roads and population density were the explanatory variables. Population density was a control variable. The findings reveal that, across all 14 districts in that province, the districts with better quality paved road networks had lower rates of crime than the districts with unpaved roads. Furthermore, the more extensive the road networks, the lower the crime rate whether or not the roads were paved. These findings concur with those reported in studies conducted in other countries, which revealed that rural areas are not always the safe, crime-free places they are often believed to be. This research contributes knowledge about the geographical information of criminal movement, and it offers some conceivable reasons for crime rates and patterns in relation to the spatial factors and the socio-cultural perspectives of Saudi Arabian life. More geographical research is still needed in terms of criminology, which will provide a better understanding of crime patterns, particularly in Saudi Arabia, and across the globe, where the spatial distribution of criminal cases is an essential base in crime research. Furthermore, additional studies are needed to investigate the complex interventions of the effect of different spatial variables on crime and the uncertainties correlation with the impact of environmental factors. This can help predict the impact of socioeconomic and environmental factors. The greater part of such an investigation will enhance the understanding of crime patterns, which is imperative for advancing a framework that can be used to address crime reduction and crime prevention.

Note: Due to the RMS change for CPS, this data set stops on 6/2/2024. For records beginning on 6/3/2024, please see the dataset at this link: https://data.cincinnati-oh.gov/safety/Reported-Crime-STARS-Category-Offenses-/7aqy-xrv9/about_data

The combined data will be available by 3/10/2025 at the linke above.

Data Description: This data represents reported Crime Incidents in the City of Cincinnati. Incidents are the records, of reported crimes, collated by an agency for management. Incidents are typically housed in a Records Management System (RMS) that stores agency-wide data about law enforcement operations. This does not include police calls for service, arrest information, final case determination, or any other incident outcome data.

Data Creation: The Cincinnati Police Department's (CPD) records crime incidents in the City through Records Management System (RMS) that stores agency-wide data about law enforcement operations.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/8eaa-xrvz

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

The Victims of Crime Research Digest will be an annual, joint publication featuring short articles dedicated to victims of crime research. Research is an important tool in helping to incorporate victims' voices on many issues in the criminal justice system and to affect change in legislation, policy or practice. In Canada, the body of research on victim issues is growing, but there remains much data to be collected to further our understanding of criminal justice processes, as well as the expectations, perceptions and needs of victims. We hope that the Digest will help to highlight some of the research that is being undertaken and that it will help share some of the findings.

Overview:

This dataset contains detailed information on cases where a hate or bias crime has been reported to the Bloomington Police Department. Hate crimes are criminal offenses motivated by bias against race, religion, ethnicity, sexual orientation, gender identity, or other protected characteristics. This dataset provides insights into the nature and demographics of hate crimes in Bloomington, aiding in understanding and addressing these incidents.

Dataset Details:

The dataset includes the following columns:

Key Features:

• Comprehensive Crime Data: Provides detailed information on hate crimes, including demographics of victims and offenders, types of offenses, and bias motivations.
• Temporal Analysis: Includes timestamps for each incident, allowing for analysis of trends over time.
• Demographic Insights: Offers data on race and gender of both victims and offenders, helping to identify patterns and target interventions.
• Location Information: Contains details about the type of location where the offense occurred, useful for spatial analysis and preventive measures.

Usage:

This dataset can be used for:

• Crime Analysis: Analyzing trends and patterns in hate crimes to inform law enforcement strategies and policies.
• Community Safety: Identifying high-risk areas and times to improve community policing and preventive measures.
• Research and Advocacy: Supporting academic research and advocacy efforts focused on combating hate crimes and promoting social justice.
• Policy Development: Assisting policymakers in developing targeted initiatives to reduce hate crimes and support affected communities.

Data Maintenance:

• Last Updated: July 7, 2024
• Source: Bloomington Police Department Data Portal
• Revisions: The dataset is annually updated to ensure the inclusion of the latest incidents and to maintain data accuracy. Historical data is preserved to support long-term analyses.

Additional Notes

• Data Accuracy: The Bloomington Police Department strives for accuracy in open data; however, errors may occur due to the nature of data collection from multiple sources.
• Data Interpretation: Users should be aware that the dataset may change over time as new information becomes available or corrections are made.
• Race and District Codes: The dataset uses specific codes for race and reading districts, which are detailed in the accompanying documentation to ensure proper interpretation.
• License: Open Data Commons Public Domain Dedication and License

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.

These data were collected to examine the relationships among crime rates, residents' attitudes, physical deterioration, and neighborhood structure in selected urban Baltimore neighborhoods. The data collection provides both block- and individual-level neighborhood data for two time periods, 1981-1982 and 1994. The block-level files (Parts 1-6) include information about physical conditions, land use, people counts, and crime rates. Parts 1-3, the block assessment files, contain researchers' observations of street layout, traffic, housing type, and general upkeep of the neighborhoods. Part 1, Block Assessments, 1981 and 1994, contains the researchers' observations of sampled blocks in 1981, plus selected variables from Part 3 that correspond to items observed in 1981. Nonsampled blocks (in Part 2) are areas where block assessments were done, but no interviews were conducted. The "people counts" file (Part 4) is an actual count of people seen by the researchers on the sampled blocks in 1994. Variables for this file include the number, gender, and approximate age of the people seen and the types of activities they were engaged in during the assessment. Part 5, Land Use Inventory for Sampled Blocks, 1994, is composed of variables describing the types of buildings in the neighborhood and their physical condition. Part 6, Crime Rates and Census Data for All Baltimore Neighborhoods, 1970-1992, includes crime rates from the Baltimore Police Department for aggravated assault, burglary, homicide, larceny, auto theft, rape, and robbery for 1970-1992, and census information from the 1970, 1980, and 1990 United States Censuses on the composition of the housing units and the age, gender, race, education, employment, and income of residents. The individual-level files (Parts 7-9) contain data from interviews with neighborhood leaders, as well as telephone surveys of residents. Part 7, Interviews with Neighborhood Leaders, 1994, includes assessments of the level of involvement in the community by the organization to which the leader belongs and the types of activities sponsored by the organization. The 1982 and 1994 surveys of residents (Parts 8 and 9) asked respondents about different aspects of their neighborhoods, such as physical appearance, problems, and crime and safety issues, as well as the respondents' level of satisfaction with and involvement in their neighborhoods. Demographic information on respondents, such as household size, length of residence, marital status, income, gender, and race, is also provided in this file.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

This paper deals with the problem of corruption, with a focus on both individual and country-specific institutional factors that may affect this problem. We analyse the determinants of the incidence of corruption as well as the tolerance of corruption. We used logit regressions that utilised data derived from Eurobarometer. The results strongly suggest gender, age, and education are important factors. We may say that anti-corruption policy ought to be targeted towards younger, less-educated, self-employed people with no children. On the other hand, a better-educated man in his early 30s seems to be a typical victim of corruption. The same is true for those having problems paying their expenses. Furthermore, contact with public officials appears to be one of the key issues, with Internet-based interactions with the government perhaps serving as the most effective solution to this problem. The rule of law, government effectiveness, and public accountability seem to be other factors that negatively correlate with the level of corruption within a country.

Introduction

Crime statistics: Crime Statistics serve as a crucial tool for understanding and addressing criminal activities within a society. In India, the National Crime Records Bureau (NCRB), established in 1986, is responsible for collecting and analyzing crime data across the country. This data collection aids in identifying trends, allocating resources, and formulating policies to combat crime effectively.

In 2024, India reported a crime rate of 445.9 incidents per 100,000 people, reflecting a slight decrease of 0.56% compared to the previous year. The most prevalent crimes included theft, robbery, and assault. Notably, rape cases increased by 1.1%, and kidnappings saw a surge of 5.1%.

Regional disparities were evident, with Uttar Pradesh recording the highest per capita crime rate at 7.4, followed by Arunachal Pradesh at 5.8, and Jharkhand at 5.3. Urban areas continued to experience higher crime rates compared to rural regions.

The NCRB employs a systematic approach to crime data analysis, encompassing five key steps: collection, categorization, analysis, dissemination, and evaluation. This methodology ensures that the data is not only accurate but also actionable, facilitating informed decision-making by law enforcement agencies and policymakers.

Understanding crime statistics is essential for developing effective strategies to enhance public safety and reduce criminal activities across the nation.

These numbers don't tell the whole story, but they give us a good starting point to understand what's happening in our communities. They can be used as tools to help criminal justice professionals anticipate increased risk of crime.

This Alberta Official Statistic describes the violent crime rates for Canada and provinces for the years from 1998 to 2014. The rate is based on the incidence of violent crime per 100,000 population in each province. The Canadian Centre for Justice Statistics (CCJS), in co-operation with the policing community, collects police-reported crime statistics through the Uniform Crime Reporting (UCR) Survey. The UCR Survey was designed to measure the incidence of crime in Canadian society and its characteristics. UCR data reflect reported crime that has been substantiated by police. Information collected by the survey includes the number of criminal incidents, the clearance status of those incidents and persons-charged information. The UCR Survey produces a continuous historical record of crime and traffic statistics reported by every police agency in Canada since 1962. In 1988, a new version of the survey (UCR3) was created, which is referred to as the "incident-based" survey. It captures microdata on characteristics of incidents, victims and accused. Data from the UCR Survey provide key information for crime analysis, resource planning and program development for the policing community. Municipal and provincial governments use the data to aid decisions about the distribution of police resources, definitions of provincial standards and for comparisons with other departments and provinces. To the federal government, the UCR survey provides information for policy and legislative development, evaluation of new legislative initiatives, and international comparisons. To the public, the UCR survey offers information on the nature and extent of police-reported crime and crime trends in Canada. As well, media, academics and researchers use these data to examine specific issues about crime.

December 2023 Issue 36 "Special Issue on Criminal Policy and Crime Prevention Research"Special Feature on Justice Social Work and Crime Prevention

Since 1973, the Ministry of Justice's "Criminal Research Center" has annually compiled the book "Crime Status and Its Analysis," which consolidates important statistical data on the government's handling of criminal cases and provides explanatory text. Due to its long history and detailed content, it has been an important reference for academia in the study of criminal policy and criminology, as well as a crucial reference for the practical understanding of the overall crime issues within the country and the formulation of relevant crime prevention strategies. In order to enhance the depth and breadth of research and analysis in "Crime Status and Its Analysis," it has gradually aligned with international crime prevention research. This study takes into account the statistical systems and content of advanced countries to address the crime situation in Taiwan in 2016 from the perspective of criminal policy and criminology. Through systematic collection and analysis of government statistical data, the study aims to achieve four main objectives: (1) strengthen the international orientation and communication aspect; (2) deepen the depth of research and analysis, in line with societal needs; (3) enhance data and chart interpretation tools to promote research and analysis functions; (4) propose specific policy recommendations as references for government administration.

This dataset is for RMS Crime Incidents for 2025. For the comprehensive dataset which includes all records please refer to the RMS Crime Incidents dataset. The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects reported criminal offenses that have occurred in the City of Detroit. Incident-based offense data is extracted from the Detroit Police Department's records management system hourly. This data set contains the most recent data available and is updated anytime DPD sends official crime records contributing to the Michigan Incident Crime Reporting (MICR) or the National Incident Based Reporting systems (reflected by the IBR Date field). It should be noted that some incidents involve the commission of multiple offenses, such as a domestic assault where property was also vandalized. In such cases, there is a row in the dataset for each offense, and the related offenses share a common Crime ID and Report Number.

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

The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on communitycrimemap.comData Dictionary:Field NameField DescriptionIncident Numberthe number associated with either the incident or used as reference to store the items in our evidence roomsDate Reportedthe date the incident was reported to LMPDDate Occurredthe date the incident actually occurredBadge IDBadge ID of responding OfficerOffense ClassificationNIBRS Reporting category for the criminal act committedOffense Code NameNIBRS Reporting code for the criminal act committedNIBRS_CODEthe code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/viewNIBRS Grouphierarchy that follows the guidelines of the FBI National Incident Based Reporting SystemWas Offense CompletedStatus indicating whether the incident was an attempted crime or a completed crime.LMPD Divisionthe LMPD division in which the incident actually occurredLMPD Beatthe LMPD beat in which the incident actually occurredLocation Categorythe type of location in which the incident occurred (e.g. Restaurant)Block Addressthe location the incident occurredCitythe city associated to the incident block locationZip Codethe zip code associated to the incident block locationContact:LMPD Open Records lmpdopenrecords@louisvilleky.gov

The global crime analytics software market is expected to grow at CAGR of 8.2% for the forecast period 2023-2030.

Growing demand for effective crime prevention and reduction techniques due to rising crime rate is expected to drive the growth of the crime analytics software market
North America dominates the crime analytics software market

Key Dynamics of Crime Analytics Software Market.

Key Drivers of Crime Analytics Software Market.

Increasing Urban Crime Rates and Concerns for Public Safety: As urban populations expand and criminal activities become more sophisticated, law enforcement agencies face mounting pressure to take proactive measures. The use of crime analytics software facilitates real-time monitoring, predictive policing, and data-driven decision-making, all aimed at enhancing public safety and optimizing resource allocation.

Government and Law Enforcement Agency Adoption: Across various regions, governments are making significant investments in smart policing infrastructure. Crime analytics tools are being incorporated into national security and policing frameworks to identify patterns, anticipate threats, and enable quicker responses. Such investments are a major driver of market growth.

Advancements in AI, Big Data, and Geospatial Technologies: The advancement of artificial intelligence, machine learning, and GIS technologies significantly boosts the capabilities of crime analytics software. These innovations support real-time crime mapping, recognition of behavioral patterns, and the generation of actionable insights, which contribute to more effective crime prevention and resolution.

Key Restrains for Crime Analytics Software Market.

Concerns Regarding Data Privacy and Ethics: The utilization of personal data for predictive analytics raises critical issues related to surveillance, bias, and civil liberties. Any misuse or lack of transparency in data collection and analysis can result in legal challenges and public discontent.

High Costs of Implementation and Integration: The deployment of crime analytics systems necessitates substantial investment in hardware, software, training, and data infrastructure. For smaller municipalities or agencies with constrained budgets, the significant initial and ongoing expenses may hinder or restrict adoption.

Challenges of Inconsistent Data Sources and System Fragmentation: Crime data is frequently sourced from various entities—law enforcement, public safety, social media, etc.—which may not adhere to standardization or interoperability. This fragmentation can impede data accuracy and the overall effectiveness of crime analysis platforms.

Key Trends in Crime Analytics Software Market.

Increasing Adoption of Predictive Policing Models: Law enforcement agencies are progressively utilizing predictive analytics to identify potential crime hotspots and strategically deploy officers. These models analyze historical crime data, along with factors such as time, location, and environmental conditions, to predict incidents and mitigate crime rates.

Integration with Body Cameras and Surveillance Systems: Crime analytics systems are being combined with live video feeds, CCTV networks, and body-worn cameras. This integration facilitates real-time monitoring, evidence gathering, and automated identification of suspects, thereby improving overall situational awareness.

Expansion of Cloud-Based and Mobile Solutions: Cloud-based and mobile-compatible crime analytics applications provide remote access, enable collaboration across different jurisdictions, and offer real-time data updates. These solutions are becoming increasingly favored due to their scalability, cost-effectiveness, and enhanced operational flexibility for law enforcement agencies.

The COVID-19 impact on the Crime Analytics Software Market.

The COVID-19 pandemic has had a significant impact on the crime analytics software market, resulting in both challenges and opportunities for the industry. The most immediate impact of the pandemic was the widespread imposition of travel restrictions, lockdowns, and quarantines. Due to the lockdowns, social distancing measures, and changes in daily routines, the burglary and street-level crimes have noticed some reduction. Crime analytics software would have been crucial in identifying and analysing these shifts. Remote work became essential during the pandemic, including for law enforcement agencies....

In 2023, German police registered around 214,100 cases of violent crime, which was a large increase compared with the year before. During the specified period, figures peaked in 2007. Violent crimes are characterized by the use of force or even weapons on a victim.