In a world of increasing crime, many organizations are interested in examining incident details to learn from and prevent future crime. Our client, based in Los Angeles County, was interested in this exact thing. They asked us to examine the data to answer several questions; among them, what was the rate of increase or decrease in crime from 2020 to 2023, and which ethnicity or group of people were targeted the most.

Our data was collected from Kaggle.com at the following link:

https://www.kaggle.com/datasets/nathaniellybrand/los-angeles-crime-dataset-2020-present

It was cleaned, examined for further errors, and the analysis performed using RStudio. The results of this analysis are in the attached PDF entitled: "crime_data_analysis_report." Please feel free to review the results as well as follow along with the dataset on your own machine.

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.

Prelude

This dataset is a cleaned version of the Chicago Crime Dataset, which can be found here. All rights for the dataset go to the original owners. The purpose of this dataset is to display my skills in visualizations and creating dashboards. To be specific, I will attempt to create a dashboard that will allow users to see metrics for a specific crime within a given year using filters and metrics. Due to this, there will not be much of a focus on the analysis of the data, but there will be portions discussing the validity of the dataset, the steps I took to clean the data, and how I organized it. The cleaned datasets can be found below, the Query (which utilized BigQuery) can be found here and the Tableau dashboard can be found here.

About the Dataset

Important Facts

The dataset comes directly from the City of Chicago's website under the page "City Data Catalog." The data is gathered directly from the Chicago Police's CLEAR (Citizen Law Enforcement Analysis and Reporting) and is updated daily to present the information accurately. This means that a crime on a specific date may be changed to better display the case. The dataset represents crimes starting all the way from 2001 to seven days prior to today's date.

Reliability

Using the ROCCC method, we can see that: * The data has high reliability: The data covers the entirety of Chicago from a little over 2 decades. It covers all the wards within Chicago and even gives the street names. While we may not have an idea for how big the sample size is, I do believe that the dataset has high reliability since it geographically covers the entirety of Chicago. * The data has high originality: The dataset was gained directly from the Chicago Police Dept. using their database, so we can say this dataset is original. * The data is somewhat comprehensive: While we do have important information such as the types of crimes committed and their geographic location, I do not think this gives us proper insights as to why these crimes take place. We can pinpoint the location of the crime, but we are limited by the information we have. How hot was the day of the crime? Did the crime take place in a neighborhood with low-income? I believe that these key factors prevent us from getting proper insights as to why these crimes take place, so I would say that this dataset is subpar with how comprehensive it is. * The data is current: The dataset is updated frequently to display crimes that took place seven days prior to today's date and may even update past crimes as more information comes to light. Due to the frequent updates, I do believe the data is current. * The data is cited: As mentioned prior, the data is collected directly from the polices CLEAR system, so we can say that the data is cited.

Processing the Data

Cleaning the Dataset

The purpose of this step is to clean the dataset such that there are no outliers in the dashboard. To do this, we are going to do the following: * Check for any null values and determine whether we should remove them. * Update any values where there may be typos. * Check for outliers and determine if we should remove them.

The following steps will be explained in the code segments below. (I used BigQuery for this so the coding will follow BigQuery's syntax) ```

Examining the dataset

There are over 7.5 million rows of data

Putting a limit so it does not take a long time to run

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime LIMIT 1000;

Seeing which points are null

There are 85,000 null points so we can exclude them as it's not a significant amount since it is only ~1.3% of the dataset

Most of the null points are in the lat and long, which we will need later

Because we don't have the full address, we can't estimate the lat and long in SQL so we will have to delete the rows with Null Data

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime WHERE unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Deleting all null rows

DELETE FROM portfolioproject-350601.ChicagoCrime.Crime WHERE
unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Checking for any duplicates in the unique keys

None to be found

SELECT unique_key, COUNT(unique_key) FROM `portfolioproject-350601.ChicagoCrime....

Description: This dataset contains detailed records of crimes reported across various regions from 2020 to the present. It provides valuable insights into crime trends, patterns, and changes in crime rates over time. The data is suitable for researchers, data analysts, law enforcement agencies, and policymakers looking to analyze crime dynamics or develop predictive models to enhance public safety measures.

Applications:

Trend Analysis: Identify seasonal or yearly patterns in crime rates.

Predictive Modeling: Develop machine learning models to forecast high-risk areas.

Policy Planning: Support policymakers in designing targeted crime prevention strategies.

Visualization Projects: Create heatmaps, dashboards, and visual reports for crime data.

The global market for crime analytics tools is experiencing robust growth, driven by the increasing need for efficient law enforcement and proactive crime prevention strategies. The rising adoption of advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics is transforming crime analysis, enabling predictive policing, improved resource allocation, and faster response times. This market is segmented by application (police stations, schools, research institutes) and deployment type (cloud-based, on-premises). Cloud-based solutions are gaining traction due to their scalability, cost-effectiveness, and ease of access. Key players like LexisNexis, IBM, and Motorola Solutions are driving innovation and market penetration through their comprehensive software suites and services. The North American market currently holds a significant share, owing to advanced technological infrastructure and increased government spending on public safety. However, the Asia-Pacific region is projected to witness rapid growth in the coming years, fuelled by rising urbanization, increasing crime rates, and the adoption of sophisticated crime analytics solutions. While data privacy concerns and the high initial investment costs present certain restraints, the overall market outlook remains optimistic. The forecast period (2025-2033) indicates continued expansion, driven by factors like improved data integration capabilities, the growing availability of crime-related datasets, and the increasing sophistication of analytical algorithms. The competitive landscape is characterized by both established players and emerging technology providers. The market is poised for further consolidation as companies expand their product portfolios and geographical reach. The development of specialized solutions tailored to specific crime types and geographical regions will be a key growth driver. Furthermore, the integration of crime analytics with other smart city initiatives will unlock new opportunities for market expansion. Government initiatives aimed at enhancing public safety and promoting the use of technology in law enforcement will also contribute to market growth.

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....

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

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! 😊💝

Checkout my top datasets

• Top Spotify Songs in 73 Countries
• Wages by Education in the USA
• Amazon Products Dataset (1.4M Products)
• TMDB 950K Movies
• Amazon Kindle Books (130K Books)

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.

Crime Analytics Tool Market Outlook

The global crime analytics tool market size was valued at approximately USD 5.4 billion in 2023 and is projected to reach around USD 12.1 billion by 2032, growing at a CAGR of 9.5% during the forecast period. The substantial growth in the crime analytics tool market can be attributed to the increasing adoption of advanced technologies by law enforcement agencies and the rising incidences of crime globally, which necessitates more sophisticated methods of crime prevention and analysis.

One of the main growth factors driving the crime analytics tool market is the rapid technological advancements in big data analytics and artificial intelligence (AI). These technologies are being increasingly integrated into crime analytics tools, providing law enforcement agencies with powerful capabilities to analyze vast amounts of data quickly and accurately. Additionally, the proliferation of smart city initiatives across the globe is further fueling the demand for these tools, as they play a crucial role in enhancing the security infrastructure of urban environments. The ability of crime analytics tools to predict and prevent criminal activities by analyzing patterns and trends is proving to be invaluable in maintaining public safety.

Another significant driver of market growth is the increasing collaboration between public and private sectors in enhancing security measures. With the rise in cybercrimes and terrorism, both government agencies and private security firms are investing heavily in advanced crime analytics solutions. This collaboration is not only improving the overall effectiveness of crime prevention strategies but also driving innovations within the market. Furthermore, the growing awareness among law enforcement agencies about the benefits of crime analytics tools, such as improved response times and resource allocation, is contributing to the market's expansion.

The integration of Internet of Things (IoT) devices and surveillance systems with crime analytics tools is also propelling the market forward. IoT devices generate massive amounts of data that can be analyzed to gain insights into potential threats and criminal activities. By incorporating data from various sources such as CCTV footage, social media, and other digital platforms, crime analytics tools can provide a comprehensive overview of the security landscape, aiding in more effective decision-making. This holistic approach to crime prevention is becoming increasingly essential in today's interconnected world.

Regionally, North America holds the largest market share due to the early adoption of advanced technologies and the presence of several key players in the region. The strong focus on homeland security and substantial investments in public safety infrastructure are also contributing factors. Europe follows closely, with significant growth driven by stringent regulations and increasing efforts to combat organized crime. The Asia Pacific region is expected to witness the highest CAGR during the forecast period, fueled by rapid urbanization, increasing crime rates, and significant government investments in smart city projects. Latin America and the Middle East & Africa are also expected to see notable growth, driven by improving economic conditions and heightened security concerns.

Component Analysis

The crime analytics tool market is segmented into three primary components: software, hardware, and services. The software component dominates the market, driven by the increasing demand for advanced analytical solutions capable of processing large datasets and generating actionable insights. Crime analytics software includes various applications such as predictive analytics, data mining, and visualization tools that enable law enforcement agencies to identify crime patterns and trends effectively. The continuous advancements in AI and machine learning algorithms are further enhancing the capabilities of these software solutions, making them indispensable tools for modern crime prevention.

Hardware components, although smaller in market share compared to software, play a crucial role in the overall crime analytics ecosystem. This segment includes surveillance cameras, sensors, and other IoT devices that collect real-time data essential for comprehensive crime analysis. The integration of high-definition cameras, facial recognition systems, and biometric devices with crime analytics software is significantly improving the accuracy and efficiency of crime detection and prevention efforts. As the demand for robust security infrastructure continues to rise,

Evidence about the relationship between lighting and crime is mixed. Although a review of evidence found that improved road / street lighting was associated with reductions in crime, these reductions occurred in daylight as well as after dark, suggesting any effect was not due only to changes in visual conditions. One limitation of previous studies is that crime data are reported in aggregate and thus previous analyses were required to make simplifications concerning types of crimes or locations. We will overcome that by working with a UK police force to access records of individual crimes. We will use these data to determine whether the risk of crime at a specific time of day is greater after dark than during daylight. If no difference is found, this would suggest improvements to visual conditions after dark through lighting would have no effect. If however the risk of crime occurring after dark was greater than during daylight, quantifying this effect would provide a measure to assess the potential effectiveness of lighting in reducing crime risk after dark. We will use a case and control approach to analyse ten years of crime data. We will compare counts of crimes in ‘case’ hours, that are in daylight and darkness at different times of the year, and ‘control’ hours, that are in daylight throughout the year. From these counts we will calculate odds ratios as a measure of the effect of darkness on risk of crime, using these to answer three questions: 1) Is the risk of overall crime occurring greater after dark than during daylight? 2) Does the risk of crime occurring after dark vary depending on the category of crime? 3) Does the risk of crime occurring after dark vary depending on the geographical area?

🌍 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.

Gain critical insights into crime trends, risk assessment, and public safety with our comprehensive Crime Dataset. Designed for law enforcement agencies, researchers, and analysts, this dataset provides structured and reliable crime data to support investigations, policy-making, and crime prevention strategies.

Dataset Features

Crime Reports: Access detailed records of reported crimes, including incident type, date, time, and location. Law Enforcement Data: Extract information on arrests, case statuses, and law enforcement responses. Geospatial Crime Mapping: Analyze crime distribution across different regions, cities, and neighborhoods. Trends & Patterns: Identify crime trends over time, including seasonal fluctuations and high-risk areas. Demographic Insights: Understand crime demographics, including offender and victim profiles.

Customizable Subsets for Specific Needs Our Crime Dataset is fully customizable, allowing you to filter data based on crime type, location, time period, or law enforcement jurisdiction. Whether you need broad coverage for national crime analysis or focused data for local risk assessment, we tailor the dataset to your needs.

Popular Use Cases

Crime Risk Assessment & Prevention: Identify high-crime areas, assess risk factors, and develop crime prevention strategies. Law Enforcement & Investigations: Support law enforcement agencies with structured crime data for case analysis and intelligence gathering. Urban Planning & Public Safety: Use crime data to inform city planning, improve public safety measures, and allocate resources effectively. AI & Predictive Analytics: Train AI models for crime forecasting, anomaly detection, and predictive policing. Policy & Legal Research: Analyze crime trends to support policy-making, legal studies, and criminal justice reforms.

Whether you're analyzing crime trends, supporting law enforcement, or developing predictive models, our Crime Dataset provides the structured data you need. Get started today and customize your dataset to fit your research and security objectives.

Discover the booming crime analytics tool market! Explore its $2.5B (2025) value, 15% CAGR, key players (LexisNexis, IBM, Motorola), and driving trends like AI & predictive policing. Get insights into market segmentation, regional growth, and future forecasts until 2033.

Crime Analytics Software Market Outlook

The global crime analytics software market size is poised for robust growth, with projections indicating a rise from USD 2.5 billion in 2023 to an impressive USD 5.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of approximately 9.5%. This substantial growth is driven by the increasing need for sophisticated crime detection and prevention tools in both public and private sectors, spurred by rising security concerns globally.

One of the primary growth factors fueling this market is the escalating incidence of criminal activities worldwide, which necessitates advanced analytical tools for effective crime prevention and investigation. Governments and law enforcement agencies are increasingly investing in advanced technologies to enhance their crime-fighting capabilities. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in crime analytics software is revolutionizing the way data is analyzed and utilized, leading to more accurate and timely insights.

Furthermore, the advancement in big data analytics is another significant driver for the crime analytics software market. With the exponential growth of data generated from various sources such as social media, surveillance cameras, and sensors, there is a crucial need for systems that can handle and analyze large volumes of data efficiently. Big data analytics enables the extraction of actionable insights from complex datasets, aiding in predictive policing and crime pattern analysis.

Moreover, the growing adoption of cloud-based solutions is contributing significantly to market growth. Cloud-based crime analytics software offers several advantages, including scalability, cost-effectiveness, and enhanced accessibility. These solutions allow organizations to manage and analyze large datasets without the need for substantial investments in infrastructure. As a result, small and medium-sized enterprises (SMEs) and government agencies with limited budgets are increasingly turning to cloud-based solutions.

Regionally, North America holds the largest share of the crime analytics software market due to the early adoption of advanced technologies and the presence of major market players in the region. Additionally, the stringent regulatory framework and high focus on public safety and national security drive the demand for crime analytics solutions in this region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rising focus on smart city initiatives and increasing government investments in security infrastructure.

Component Analysis

The crime analytics software market can be segmented by components into software and services. The software component includes various types of crime analytics platforms and solutions that are used for data collection, analysis, and visualization. These software solutions are designed to provide law enforcement agencies and security organizations with the tools needed to analyze crime data effectively and make informed decisions. The increasing demand for real-time analytics and the integration of advanced technologies like AI and ML are driving the growth of the software segment.

On the other hand, the services segment encompasses a range of support and maintenance services, consulting services, and training programs offered by vendors to ensure the effective deployment and utilization of crime analytics software. These services are crucial for the smooth operation and optimization of crime analytics systems. As organizations look to maximize the value of their crime analytics investments, the demand for professional services is expected to rise. This segment is also driven by the increasing complexity of analytics solutions, which requires specialized expertise for deployment and maintenance.

In the software segment, the adoption of AI-powered crime analytics tools is gaining momentum. These tools leverage machine learning algorithms to identify patterns, predict potential criminal activities, and provide actionable insights. The ability to process and analyze vast amounts of data in real-time is a key factor driving the adoption of such solutions. Additionally, advancements in natural language processing (NLP) are enhancing the capabilities of crime analytics software, enabling it to understand and analyze unstructured data from sources like social media and open-source intelligence.

Furthermore, the services segment is witnessing significant grow

The Law Enforcement Agency Reported Crime Analysis Tool (LEARCAT) dashboard enables users to examine crime information (incident-based data ) reported by participating law enforcement agencies to the FBI’s National Incident-Based Reporting System (NIBRS). The FBI’s Uniform Crime Reporting Program (UCR) defines an incident as one or more offenses committed by the same offender, or group of offenders, acting in concert at the same time and place. A single incident can contain multiple victims, victim types, crimes, and arrests. The NIBRS data are not nationally representative and law enforcement participation varies by state.

The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in from December 2016 to present. Note that records are included in the dataset based on when an incident is reported which could result in an occurrence date before December 2016. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows)details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

This study examines the relationship between socio-economic factors and crime distribution using a dataset that includes variables such as unemployment rates, literacy rates, per capita income, and population density. The analysis explores how these factors influence crime rates across different regions, comparing urban and rural areas to identify variations in crime patterns due to economic and social disparities. Additionally, the study investigates cultural and psychological influences on criminal activities. The findings offer valuable insights for policymakers to develop more effective crime prevention strategies.This dataset supports the manuscript ‘Crime and Socio-Economic Inequalities: Leveraging Deep Learning and Generative AI for Comprehensive Analysis.’ It includes:- CrimeEconomicsData.csv: Original dataset with 114 observations across 10 socio-economic variables (Per Capita Income, Population Density, Unemployment, Literacy Rate, Happiness Index, Crime Rate).- supplementary_data.zip: Contains: - table_ii_metrics.csv: Performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) for machine learning and deep learning models in Table II. - figure_2_confusion_matrices.csv: Confusion matrix data for each model, supporting Figure 2’s visualizations. - README.txt: Description of the files and their purpose.Preprocessed datasets are not included, as preprocessing steps (e.g., mean imputation, standardization, PCA) are detailed in the manuscript and can be replicated using CrimeEconomicsData.csv.

Crime Analytics Market Outlook

According to our latest research, the global crime analytics market size reached USD 9.2 billion in 2024, reflecting robust demand from both public and private sectors. The market is expected to grow at a strong CAGR of 14.6% during the forecast period, reaching a projected value of USD 29.1 billion by 2033. This impressive growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence by law enforcement agencies and other organizations seeking to enhance public safety, prevent crime, and improve operational efficiency. The proliferation of digital data, rising concerns about security threats, and the need for real-time actionable insights are further fueling the expansion of the crime analytics market globally.

One of the key growth factors propelling the crime analytics market is the rapid digital transformation across law enforcement and public safety organizations. As cities and communities become more interconnected and digitalized, the volume of data generated from surveillance systems, social media, financial transactions, and other sources has surged exponentially. This vast data landscape presents both challenges and opportunities for crime prevention and investigation. Crime analytics solutions leverage advanced technologies such as artificial intelligence, machine learning, and big data analytics to process, analyze, and interpret these massive datasets, enabling stakeholders to identify crime patterns, predict potential threats, and deploy resources more effectively. The increasing reliance on data-driven decision-making in policing and security operations is expected to continue driving market growth over the coming years.

Another significant driver for the crime analytics market is the growing need for proactive security measures in response to evolving and sophisticated criminal activities. With the rise of cybercrime, organized crime, and terrorism, traditional reactive approaches are no longer sufficient to ensure public safety. Crime analytics tools empower agencies to shift from reactive to proactive strategies by enabling predictive policing, real-time incident monitoring, and rapid response coordination. Advanced solutions such as geospatial analysis and social network analysis help authorities uncover hidden relationships, track criminal networks, and anticipate criminal behavior, thereby enhancing their ability to prevent crimes before they occur. The integration of crime analytics into homeland security, financial institutions, and other sectors further broadens the market’s scope and impact.

The increasing emphasis on inter-agency collaboration and information sharing is also contributing to the expansion of the crime analytics market. Governments and organizations worldwide recognize the importance of breaking down silos and fostering cross-functional partnerships to combat complex and transnational crimes. Crime analytics platforms facilitate seamless data exchange and collaborative investigations by providing centralized, secure, and interoperable solutions. These platforms support multi-agency task forces, joint operations, and intelligence-led policing initiatives, thereby improving overall crime-fighting capabilities. The adoption of cloud-based deployment models further enhances accessibility, scalability, and cost-effectiveness, making crime analytics solutions attractive to organizations of all sizes and resource levels.

Regionally, North America continues to dominate the global crime analytics market, accounting for the largest share in 2024, driven by substantial investments in public safety infrastructure, advanced technology adoption, and the presence of leading solution providers. Europe and Asia Pacific are also witnessing rapid growth, fueled by increasing security concerns, government initiatives, and the digitalization of law enforcement agencies. Emerging economies in Latin America and the Middle East & Africa are gradually embracing crime analytics solutions to address rising crime rates and improve public safety outcomes. The regional landscape is expected to remain dynamic, with tailored strategies and localized offerings playing a crucial role in market expansion.

Component Analysis

The crime analytics market is segmented by component into software, hardware, and services, each playing a pivotal role in the deployment and functionality of crime analytics solutions. Software forms the core of the marke

The global crime risk report market is a rapidly expanding sector, projected to reach a substantial market size. While the provided data mentions a market size of 13,370 (presumably in millions) and a study period of 2019-2033, a precise CAGR is missing. Considering the growth drivers in the industry—increasing cybercrime, heightened security concerns for businesses and individuals, and the rising adoption of advanced analytics for risk assessment—a conservative estimate for CAGR during the forecast period (2025-2033) could be around 8-10%. This growth is fueled by the increasing demand for proactive risk management strategies from various sectors, including finance, insurance, and government agencies. Companies like IBM, PwC, and Verisk Analytics are major players, leveraging their expertise in data analytics, security, and consulting to provide comprehensive crime risk reports. The market segmentation is currently unknown, but likely includes various report types based on geography, crime type, and industry focus. The historical period (2019-2024) likely witnessed significant market expansion driven by evolving technological advancements and increasing awareness of potential risks. The market's future growth trajectory will be shaped by ongoing technological advancements such as AI-powered predictive analytics, the increasing adoption of big data and IoT for crime prevention, and the growing regulatory landscape driving compliance needs. Competition in this market is high, with established players competing with niche providers. The market's regional distribution is unknown but is expected to be diversified across North America, Europe, and Asia-Pacific, mirroring global economic activity and crime rates. Further market expansion will depend on factors like government investment in crime prevention technologies, public-private partnerships, and advancements in crime data analysis capabilities.