Chicago Crime Incident Data

65,000+ Records of Arrests, Locations, and Descriptions

By City of Chicago [source]

About this dataset

This dataset is a compilation of reported crimes that have taken place in the City of Chicago over the past year, and provides an invaluable insight into the criminal activity occurring within our city. Featuring more than 65,000 records of data, it contains information on the date of each incident, its location (down to the block level), type of crime committed (determined by FBI Crime Classification Codes) and whether or not an arrest has been made in connection with each crime. As this dataset reveals detailed information on crime incidents which may lead to personal identification, addresses are masked beyond block level and specific locations are not disclosed.

For additional questions regarding this dataset, please do not hesitate to reach out to The Research & Development Division at 312.745.6071 or RandDchicagopolice.com who will be more than happy to help answer any inquiries you may have about our data findings! All visualized maps should be considered approximate however—it is prohibited for any attempts to derive specific addresses from them as accuracy cannot be guaranteed with regards to mechanical or human error when collecting this data over time. So come join us as we explore a year's worth of criminal activities throughout Chicago!

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How to use the dataset

This guide will provide an overview on how to use this dataset to analyze patterns or draw conclusions about crime incidents in and around Chicago.

Secondly, become familiar with columns names which appear at top most row of your opened file which helps you understand what kind of data is stored at each column such as - CASE# - Unique identifier for the crime incident., DATE OF OCCURRENCE - Date when crime incident occurred , BLOCK - Block where event took place , LOCATION DESCRIPTION- Description of location where incident happened . Through these columns name you can easily recognize what kind of data exists within that record/row. That’s why it’s important to get familiar with them first before diving into raw datasets because they’ll help make exploring and understanding large sets easier later on when we go further into illustrating charts & graphs using programs such as Tableau & Power BI or even spreadsheets (Excel). After understanding column names its time to explore further by digging deeper into each record/row and apply filters if required e.g below $100 value will show only those rows having value less than 100 thus it will filter entire dataset according to your requirement. Lastly analyse collected datasets either Visually through plotting graphs with help tableau software OR By using Mathematical mathematical equations based on research questions such as finding out average values after applying sum/avg functions from respective cells etc

Research Ideas

• Creating a visualization mapping tool to help visualize the types of crimes and their locations over time within Chicago.
• An analysis tool for city officials or police departments so they can understand correlations between crime type, geography, and other factors like weather changes or economic downturns in order to develop long-term plans for crime prevention.
• Developing an AI model that would be able to predict what areas may be more vulnerable for certain types of crimes or even predict crimes ahead of time based on the data from this dataset

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: crimes-one-year-prior-to-present-1.csv | Column name | Description | |:-------------------------|:------------------------------------------------------------------------------| | CASE# | Unique identifier for each crime incident (String) | | BLOCK | Block where the crime incident occurred (String) | | LOCATION DESCRIPTION | Description of where an incident took place (String) | | ARREST | Indicates if an arrest was made in connection with a crime incident (Boolean) | | DOMESTIC | Indicates if a reported incident is domestic related (Boolean) | | BEAT ...

World crime rate per 100K population for 2020 was 5.61, a 0.74% increase from 2019.

<ul style='margin-top:20px;'>

<li>World crime rate per 100K population for 2019 was <strong>5.56</strong>, a <strong>3.65% decline</strong> from 2018.</li>
<li>World crime rate per 100K population for 2018 was <strong>5.77</strong>, a <strong>2.24% decline</strong> from 2017.</li>
<li>World crime rate per 100K population for 2017 was <strong>5.91</strong>, a <strong>0.69% decline</strong> from 2016.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

The average for 2017 based on 79 countries was 105 robberies per 100,000 people. The highest value was in Costa Rica: 1587 robberies per 100,000 people and the lowest value was in Oman: 1 robberies per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

Turks and Caicos Islands saw a murder rate of ***** per 100,000 inhabitants, making it the most dangerous country for this kind of crime worldwide as of 2024. Interestingly, El Salvador, which long had the highest global homicide rates, has dropped out of the top 29 after a high number of gang members have been incarcerated. Meanwhile, Colima in Mexico was the most dangerous city for murders. Violent conflicts worldwide Notably, these figures do not include deaths that resulted from war or a violent conflict. While there is a persistent number of conflicts worldwide, resulting casualties are not considered murders. Partially due to this reason, homicide rates in Latin America are higher than those in Afghanistan or Syria. A different definition of murder in these circumstances could change the rate significantly in some countries. Causes of death Also, noteworthy is that murders are usually not random events. In the United States, the circumstances of murders are most commonly arguments, followed by narcotics incidents and robberies. Additionally, murders are not a leading cause of death. Heart diseases, strokes and cancer pose a greater threat to life than violent crime.

Women's Crimes in India

Characteristics, Frequency, and Motives

By Rajanand Ilangovan [source]

About this dataset

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

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How to use the dataset

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

Research Ideas

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

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

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

The average for 2017 based on 97 countries was 7.4 homicides per 100,000 people. The highest value was in El Salvador: 61.8 homicides per 100,000 people and the lowest value was in Japan: 0.2 homicides per 100,000 people. The indicator is available from 1990 to 2017. Below is a chart for all countries where data are available.

India crime rate per 100K population for 2021 was 2.94, a 0.86% increase from 2020.

<ul style='margin-top:20px;'>

<li>India crime rate per 100K population for 2020 was <strong>2.91</strong>, a <strong>0.53% decline</strong> from 2019.</li>
<li>India crime rate per 100K population for 2019 was <strong>2.93</strong>, a <strong>2.24% decline</strong> from 2018.</li>
<li>India crime rate per 100K population for 2018 was <strong>2.99</strong>, a <strong>1.16% decline</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

In 2023, the FBI reported that there were 9,284 Black murder victims in the United States and 7,289 white murder victims. In comparison, there were 554 murder victims of unknown race and 586 victims of another race. Victims of inequality? In recent years, the role of racial inequality in violent crimes such as robberies, assaults, and homicides has gained public attention. In particular, the issue of police brutality has led to increasing attention following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Studies show that the rate of fatal police shootings for Black Americans was more than double the rate reported of other races. Crime reporting National crime data in the United States is based off the Federal Bureau of Investigation’s new crime reporting system, which requires law enforcement agencies to self-report their data in detail. Due to the recent implementation of this system, less crime data has been reported, with some states such as Delaware and Pennsylvania declining to report any data to the FBI at all in the last few years, suggesting that the Bureau's data may not fully reflect accurate information on crime in the United States.

The average for 2016 based on 74 countries was 783 thefts per 100,000 people. The highest value was in Denmark: 3949 thefts per 100,000 people and the lowest value was in Senegal: 1 thefts per 100,000 people. The indicator is available from 2003 to 2016. Below is a chart for all countries where data are available.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

Real-world Anomaly Detection in Surveillance Videos

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work.

Problem & Motivation

One critical task in video surveillance is detecting anomalous events such as traffic accidents, crimes or illegal activities. Generally, anomalous events rarely occur as compared to normal activities. Therefore, to alleviate the waste of labor and time, developing intelligent computer vision algorithms for automatic video anomaly detection is a pressing need. The goal of a practical anomaly detection system is to timely signal an activity that deviates normal patterns and identify the time window of the occurring anomaly. Therefore, anomaly detection can be considered as coarse level video understanding, which filters out anomalies from normal patterns. Once an anomaly is detected, it can further be categorized into one of the specific activities using classification techniques. In this work, we propose an anomaly detection algorithm using weakly labeled training videos. That is we only know the video-level labels, i.e. a video is normal or contains anomaly somewhere, but we do not know where. This is intriguing because we can easily annotate a large number of videos by only assigning video-level labels. To formulate a weakly-supervised learning approach, we resort to multiple instance learning. Specifically, we propose to learn anomaly through a deep MIL framework by treating normal and anomalous surveillance videos as bags and short segments/clips of each video as instances in a bag. Based on training videos, we automatically learn an anomaly ranking model that predicts high anomaly scores for anomalous segments in a video. During testing, a longuntrimmed video is divided into segments and fed into our deep network which assigns anomaly score for each video segment such that an anomaly can be detected.

Method

Our proposed approach (summarized in Figure 1) begins with dividing surveillance videos into a fixed number of segments during training. These segments make instances in a bag. Using both positive (anomalous) and negative (normal) bags, we train the anomaly detection model using the proposed deep MIL ranking loss. https://www.crcv.ucf.edu/projects/real-world/method.png

UCF-Crime Dataset

We construct a new large-scale dataset, called UCF-Crime, to evaluate our method. It consists of long untrimmed surveillance videos which cover 13 realworld anomalies, including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. We compare our dataset with previous anomaly detection datasets in Table 1. For more details about the UCF-Crime dataset, please refer to our paper. A short description of each anomalous event is given below. Abuse: This event contains videos which show bad, cruel or violent behavior against children, old people, animals, and women. Burglary: This event contains videos that show people (thieves) entering into a building or house with the intention to commit theft. It does not include use of force against people. Robbery: This event contains videos showing thieves taking money unlawfully by force or threat of force. These videos do not include shootings. Stealing: This event contains videos showing people taking property or money without permission. They do not include shoplifting. Shooting: This event contains videos showing act of shooting someone with a gun. Shoplifting: This event contains videos showing people stealing goods from a shop while posing as a shopper. Assault: This event contains videos showing a sudden or violent physical attack on someone. Note that in these videos the person who is assaulted does not fight back. Fighting: This event contains videos displaying two are more people attacking one another. Arson: This event contains videos showing people deliberately setting fire to property. Explosion: This event contains videos showing destructive event of something blowing apart. This event does not include videos where a person intentionally sets a fire or sets off an explosion. Arrest: This event contains videos showing police arresting individuals. Road Accident: This event contains videos showing traffic accidents involving vehicles, pedestrians or cyclists. Vandalism: This event contains videos showing action involving deliberate destruction of or damage to public or private property. The term includes property damage, such as graffiti and defacement directed towards any property without permission of the owner. Normal Event: This event contains videos where no crime occurred. These videos include both indoor (such as a shopping mall) and outdoor scenes as well as day and night-time scenes. https://www.crcv.ucf.edu/projects/real-world/dataset_table.png https://www.crcv.ucf.edu/projects/real-world/method.png

The average for 2017 based on 65 countries was 1.8 kidnappings per 100,000 people. The highest value was in Belgium: 10.3 kidnappings per 100,000 people and the lowest value was in Bermuda: 0 kidnappings per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

In 2023, the number of crimes committed in Singapore for every 100,000 individuals was 1,188. This was a ten-year high, and mostly due to the increase in scams and cybercrimes cases. Low crime rates in Singapore Singapore has a reputation for being one of the safest cities in the world. Violent crime in Singapore is rare – as of 2021, such crimes accounted for nine per 100 thousand population. One reason for this could be the harsh penalties for offenders, as well as a strict ban on weapons for those not in law enforcement. Singapore still carries out capital punishment for crimes such as murder and the illegal possession of firearms carry the death penalty. Increase in commercial crime The most common type of crime committed in Singapore were commercial crimes, especially scams. As Singaporeans carry out more aspects of everyday life online, so too are criminals looking to take advantage of unsuspecting victims. In 2021, scams involving e-commerce transactions were the most common of such crimes. These typically involve the fraudulent sale of products on C2C commercial sites, which are harder to track. Such scams, however, usually involve smaller amounts of money, unlike investment scams. These involve targeting individuals and tricking them into wiring large sums of money for supposed financial investments. In 2021, individuals in Singapore who fell victim to such scams were cheated out of around 191 million Singapore dollars.

China: Homicides per 100,000 people: The latest value from 2017 is 0.6 homicides per 100,000 people, unchanged from 0.6 homicides per 100,000 people in 2016. In comparison, the world average is 7.4 homicides per 100,000 people, based on data from 97 countries. Historically, the average for China from 1995 to 2017 is 1.4 homicides per 100,000 people. The minimum value, 0.6 homicides per 100,000 people, was reached in 2016 while the maximum of 2.2 homicides per 100,000 people was recorded in 1995.

Police Analytics Software Market Outlook

The global police analytics software market size was valued at approximately USD 5.1 billion in 2023 and is expected to reach around USD 12.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.8% during the forecast period. This robust growth is driven by several factors, including the increasing need for law enforcement agencies to improve crime detection, prevention, and resolution rates using advanced technological tools. As crime becomes more sophisticated, the deployment of analytical and predictive tools is instrumental in aiding police departments to manage and utilize vast amounts of data effectively.

One of the primary growth factors for the police analytics software market is the heightened emphasis on data-driven decision-making within law enforcement agencies. With the proliferation of digital data from various sources such as surveillance cameras, social media, and public databases, agencies are recognizing the potential of analytics software to process and extract actionable insights from this data. This technology enables better resource allocation, enhances situational awareness, and ultimately aids in reducing crime rates. Moreover, governments around the world are increasingly investing in smart city initiatives, which incorporate sophisticated policing technologies, thereby fueling market expansion.

Another significant growth driver is the growing concern for public safety, coupled with the surge in criminal activities across urban and rural landscapes. Law enforcement agencies face continuous pressure to enhance their operational efficiency and responsiveness. Police analytics software equips these agencies with tools to conduct thorough crime analysis and predictive policing, which helps in identifying crime hotspots and potential threats ahead of time. These capabilities are not only improving operational efficiency but also fostering a proactive approach to crime prevention, which is highly valued by both agencies and the communities they serve.

The advancement of artificial intelligence and machine learning technologies is also propelling the growth of the police analytics software market. These technologies allow for more sophisticated data analysis, including pattern recognition and predictive modeling, which can foresee criminal activities before they occur. By employing AI-driven analytics, law enforcement agencies can significantly enhance their crime-fighting capabilities, improving accuracy in suspect identification and crime linkage analysis. This technological advancement is creating new opportunities for innovation within the market and compelling more agencies to adopt these solutions for a competitive edge.

Regionally, North America is currently leading the market, attributable to its technologically advanced law enforcement infrastructure and significant investments in public safety technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. This growth is fueled by rapid urbanization, rising crime rates, and increasing government investments in smart policing technologies across developing nations such as India and China. Meanwhile, Europe is also expected to experience substantial market growth, driven by stringent regulations related to public safety and the adoption of advanced crime-fighting solutions across various countries in the region.

Component Analysis

The police analytics software market by component is segmented into software and services, both of which play a pivotal role in enhancing the capabilities of law enforcement agencies. The software segment is at the forefront of this market, comprising various solutions designed to aid in crime analysis, predictive policing, and incident management. These software tools are essential in processing vast datasets generated from various sources, allowing agencies to derive actionable insights that aid in decision-making processes. The continuous evolution of software capabilities, driven by artificial intelligence and machine learning advancements, has further enhanced their analytical power, making them indispensable tools for modern policing.

Within the software segment, there are applications tailored for specific functions such as predictive policing and real-time crime mapping. Predictive policing software has gained significant traction as it enables law enforcement to anticipate potential crime hotspots and deploy resources effectively. Real-time crime mapping, on the other hand, provides law enforcement with a dynamic view of crime patterns

Innocent victims of crime are often blamed for what happened to them. In this article, we examine the hypothesis that victim blaming can be significantly reduced when people mimic the behavior of the victim or even a person unrelated to the crime. Participants watched a person on a video after which we assessed the extent of their spontaneous mimicry reactions (Study 1) or participants were instructed to mimic or not to mimic the movements of this person (Study 2). Then, they were informed about a rape and criminal assault and judged the degree to which they thought the victims were responsible for the crime. One of the crimes happened to the same person as the person they previously did or did not mimic. The other crime happened to a person unrelated to the mimicry situation. Results of both studies revealed that previously mimicking the victim or an unrelated person reduced the degree to which victims were being blamed.

China crime rate per 100K population for 2020 was 0.50, a 3.22% decline from 2019.

<ul style='margin-top:20px;'>

<li>China crime rate per 100K population for 2019 was <strong>0.52</strong>, a <strong>2.27% decline</strong> from 2018.</li>
<li>China crime rate per 100K population for 2018 was <strong>0.53</strong>, a <strong>6.27% decline</strong> from 2017.</li>
<li>China crime rate per 100K population for 2017 was <strong>0.57</strong>, a <strong>8.01% decline</strong> from 2016.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

In January 2024, the most usual type of crime reported in Mexico was domestic violence. In that month, the cases of this type of violence amounted to around 20,814 cases. Regarding property crimes, the highest number of occurrences were vehicle thefts, with nearly 11,887 reported crimes.

Domestic violence Domestic violence stands out as the crime with the highest incidence and, paradoxically, one of the least attended to by the government. Public spending allocated to combat domestic violence has been dismally low, with a value only around 25 percent of the spending on the military. Adding to the concern, this budget has witnessed a consistent decrease each year since 2015. This decline in resources has had severe consequences, leading to a surge in domestic violence crimes, with many resulting in homicides. It's noteworthy that the majority of registered femicides occur within the confines of closed domestic spaces and are often committed by the partners of the victims. This paints a concerning picture of the challenges faced in addressing and preventing domestic violence.

Mexico and the most violent cities in the world
Mexico hosts seven of the most dangerous cities globally, with Celaya ranking as the number one in terms of murder rate, registering a staggering 109.39 homicides per 100,000 inhabitants and the most of these other cities are concentrated in the upper region of the country, highlighting the significant regional variations in safety and security. On the other hand, the capital, Mexico City, has experienced a decreasing trend in crime incidence, with a notable decrease from 2018 to 2022, nonetheless, the crime rate is still high. As a result, crime and insecurity have become the primary concern for nearly half of the country's population, underscoring the pressing need for addressing these issues.

Historical chart and dataset showing Switzerland crime rate per 100K population by year from 1990 to 2021.

India Cyber Crime: IPC Section: Number of Cases Registered data was reported at 33,798.000 Unit in 2022. This records an increase from the previous number of 25,384.000 Unit for 2021. India Cyber Crime: IPC Section: Number of Cases Registered data is updated yearly, averaging 738.000 Unit from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 33,798.000 Unit in 2022 and a record low of 176.000 Unit in 2008. India Cyber Crime: IPC Section: Number of Cases Registered data remains active status in CEIC and is reported by National Crime Records Bureau. The data is categorized under India Premium Database’s Crime – Table IN.CRA001: Crime Statistics.