In 2024, the crime rate in Russia reached approximately ******* offenses per 100,000 population, marking a decrease compared to the previous year. Over the period from 2015 to 2024, the rate saw a decline of ***** registered crimes per 100,000 people. What are the most common offenses in Russia? Fraud and theft are the most frequent types of crime in Russia. New types of fraud emerged with the development of digital banking, including crimes related to personal data thefts, access to bank accounts, and unauthorized payment card transactions. Approximately ** percent of crimes in Russia are of little gravity. Criminals’ profile The largest share of people who committed offenses in Russia are aged between 30 and 49 years, among both genders. While most criminals in the country finished up to nine school classes, more than ********* of all perpetrators received a secondary professional education. Furthermore, among female criminals, over ** percent are university graduates.

The homicide rate in Russia reached *** homicides per 100,000 population in 2023. The indicator has declined steadily since 2002, when it was measured at **** murders per 100,000 inhabitants. Over the years observed, the highest rate was recorded at **** homicides per 100,000 persons in 1994. Homicide in Russia The significant decrease in intentional homicides characterizes the general reduction in crime in Russia since the 1990s. However, murder remains one of the major causes of death in the country. In total, over 7,600 homicides and attempted homicides were recorded in Russia in 2022. Victims of homicide The number of male victims of homicide per 100,000 inhabitants in Russia was approximately ***** times higher than the murder rate of women. A United Nations study showed that while most people who were killed around the globe were men, women were most likely to be murdered by their partners, ex-partners, and family members.

Approximately 98 percent of female criminals in Russia in 2021 possessed a Russian passport. Among men who committed a crime in the country, nearly four percent had a nationality of another state or were classified as stateless persons.

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

In 2021, approximately 3.45 thousand rape and attempted rape crimes were registered in Russia, marking a slight decrease in number compared to the previous year. In 2005, the highest amount of such offenses in the country was recorded at 9.2 thousand cases.

FBI National Incident-Based Reporting System (FBI NIBRS) crime data for Russia Police Department (City) in Ohio, including incidents, statistics, demographics, and detailed incident information.

In 2024, around ******* crimes were recorded in Moscow, the Russian capital. The number of crimes registered by the city's authorities decreased by approximately ***** between 2023 and 2024. Among other federal subjects of the country, Moscow had the highest number of crimes registered each year.

This dataset contains information about the state of crime in Russia since 2003. The table shows the number of crimes of various types for each month.

Your task (or desire) is to analyze the data and forecast the crimes number for next month or year. Good luck and peace🙌

Этот датасет содержит информацию о состоянии преступности в России за каждый месяц, начиная с 2003 года.

Ваша задача - проанализировать данные и попробовать предсказать число преступлений на следующий месяц или год.

Import: python import pandas as pd data = pd.read_csv('crime.csv', parse_dates=['month'], index_col=['month'], dayfirst=True)

This data is provided on a one-time basis from time to time, and is public data that provides crime statistics by region compiled by the National Police Agency as of 2023. Various crime types such as violent crimes, intelligent crimes, theft, and violence are subdivided into city/county/district levels, and for foreign criminals, crime occurrence figures by nationality (such as China, Vietnam, and Russia) are also included. This data can be used to analyze regional crime concentration, crime patterns related to foreigners, and spatial distribution by crime type. This data is used for establishing local security strategies by the police, crime prevention plans in areas with a high concentration of foreigners, space-based crime research by research institutes, and establishment of regional prevention measures by public institutions.

Most crimes in Russia were registered in Moscow, where law enforcement authorities recorded approximately ******* offenses in 2024. In the Krasnodar Krai, around ****** criminal acts were observed.

Around *** million crimes were registered by authorities in Russia in 2024, marking a slight decrease compared to the previous year. Over the observed period, the number of offenses recorded in Russia was significantly high at approximately **** million cases in 2006, after which this figure saw a gradual decline.

The average for 2017 based on 35 countries was 1.7 homicides per 100,000 people. The highest value was in Russia: 9.2 homicides per 100,000 people and the lowest value was in Luxembourg: 0.3 homicides per 100,000 people. The indicator is available from 1990 to 2017. Below is a chart for all countries where data are available.

In 2023, nearly 142,500 especially grave crimes recorded in Russia, for which the punishment involved over 10 years in custody or a more severe measure. Compared to the previous year, the number of such crimes in the country increased by 14 percent.

The Jewish Autonomous Oblast and the Republic of Karelia in Russia were the country's federal subjects with the largest share of crimes committed by underage persons or where they acted as co-perpetrators in 2024. Furthermore, in the Tyva Republic, criminal cases involving youth accounted for 5.6 percent of the total.

Context

This is official open data from The Ministry of Internal Affairs of the Russian Federation on missing and wanted people, identified and unindentified corpses. Original data available here source.

Content

File meta.csv - contain information about data source and contact information of original owners in Russian.

File structure-20140727.csv - describe datastructure in Russian. Main things that you need to know about data columns are here:

1. "Subject" - The official name of the subject of statistical reporting. That's Russian regions, note that Crimean Federal District and city ​​of Sevastopol are included.
2. "Point FPSR" - Item of the Federal Statistical Work Plan. You don't need to know this.

3. "Name of the statistical factor" - this one speaks for itself. Available factors:

   -- Identified persons from among those who were wanted, including those who disappeared from the bodies of inquiry, investigation, court.

   -- Total cases on the identification of citizens on unidentified corpses that were on the register.

   -- Total wanted persons, including those who disappeared from the bodies of inquiry, investigation, court.

   -- Identified persons from among the wanted persons, including those missing.

   -- Total wanted persons.

   -- Number (balance) of unreturned missing persons in relation to 2011 (%)

   -- Number (balance) of unresolved criminals against 2011 (%)

   -- Total discontinued cases in connection with the identification of the person

   -- Total wanted persons, including those missing

   -- Identified persons from the number of wanted persons

4. "Importance of the statistical factor" - value of correspondent statistical factor.

Files data-%Y%m%d-structure-20140727.csv contain actual data. Names of the files contain release date. Data aggregated by quarters of each year, for example data-20150127-structure-20140727.csv - data for whole 2014 year data-20150627-structure-20140727.csv - data for Q1 and Q2 of 2015

File translate.csv is used to simplify translation from Russian to English. See usage in the kernel.

Acknowledgements

Thanks to newspaper Komsomolskaya Pravda for bringing up the issue of missing kids in Russia.

Thanks to Liza Alert - Volunteer Search and Rescue Squad for efforts in rescue of missing people in Russia.

Photo by Alessio Lin on Unsplash

Inspiration

Missing people, especially kids, is a serious problem. However there is not much detailed information about it. Russian officials provide overall information without detalisation of victim's age. As a result many speculations appear in media on this topic:

• "https://www.rbth.com/multimedia/pictures/2017/07/26/when-police-are-powerless-how-volunteers-search-and-find-missing-persons_811700">Last year about 200,000 reports of missing people were filed with police in Russia.
• 45,000 kids lost every year
• More than 15,000 kids lost every year
• Radio interview - starting from minute 7:55 main point: "More than 15K kids lost completely, i.e. was not ever found"

Some insights to official data can be found here interview, year 2012: "Annually in Russia about 20 thousand minors disappear, in 90% of cases the police find children".

Still there is no information about kids in recent years. If you have any reliable sources, please share.

The dataset “Swedish Crime Fiction in Russian Translation: Editions, Corpus and Reviews” contains information about research data related to two different articles about Swedish crime fiction in Russia.

The goal of this study was to assess the nature and scope of Soviet emigre crime in the United States, with a special focus on the question of whether and how well this crime was organized. The research project was designed to overcome the lack of reliable and valid knowledge on Soviet emigre crime networks through the systematic collection, evaluation, and analysis of information. In Part 1, the researchers conducted a national survey of 750 law enforcement specialists and prosecutors to get a general overview of Soviet emigre crime in the United States. For Parts 2-14, the researchers wanted to look particularly at the character, operations, and structure, as well as the criminal ventures and enterprises, of Soviet emigre crime networks in the New York-New Jersey-Pennsylvania region. They were also interested in any international criminal connections to these networks, especially with the former Soviet Union. The investigators focused particularly on identifying whether these particular networks met the following criteria commonly used to define organized crime: (1) some degree of hierarchical structure within the network, (2) continuity of that structure over time, (3) use of corruption and violence to facilitate and protect criminal activities, (4) internal discipline within the network structure, (5) involvement in multiple criminal enterprises that are carried out with a degree of criminal sophistication, (6) involvement in legitimate businesses, and (7) bonding among participants based upon shared ethnicity. Data for Parts 2-14 were collected from a collaborative effort with the Tri-State Joint Project on Soviet Emigre Organized Crime. From 1992 through 1995 every investigative report or other document produced by the project was entered into a computer file that became the database for the network analysis. Documents included undercover observation and surveillance reports, informant interviews, newspaper articles, telephone records, intelligence files from other law enforcement agencies, indictments, and various materials from the former Soviet Union. Every individual, organization, and other entity mentioned in a document was considered an actor, given a code number, and entered into the database. The investigators then used network analysis to measure ties among individuals and organizations and to examine the structure of the relationships among the entries in the database. In Part 1, National Survey of Law Enforcement and Prosecutors Data, law enforcement officials and prosecutors were asked if their agency had any contact with criminals from the former Soviet Union, the types of criminal activity these people were involved in, whether they thought these suspects were part of a criminal organization, whether this type of crime was a problem for the agency, whether the agency had any contact with governmental agencies in the former Soviet Union, and whether anyone on the staff spoke Russian. Part 2, Actor Identification Data, contains the network identification of each actor coded from the documents in Part 3 and identified in the network data in Parts 4-14. An actor could be an individual, organization, concept, or location. Information in Part 2 includes the unique actor identification number, the type of actor, and whether the actor was a "big player." Part 3, Sources of Data, contains data on the documents that were the sources of the network data in Parts 4-14. Variables include the title and date of document, the type of document, and whether the following dimensions of organized crime were mentioned: sources of capital, locational decisions, advertising, price setting, financial arrangements, recruitment, internal structure, corruption, or overlapping partnerships. Parts 4-14 contain the coding of the ties among actors in particular types of documents, and are named for them: indictments, tips, investigative reports, incident reports, search reports, interview reports, arrest reports, intelligence reports, criminal acts reports, confidential informant reports, newspaper reports, social surveillance reports, other surveillance reports, and company reports.

In 2024, over ***** criminal offenses committed under drug intoxication were recorded by Russian law enforcement authorities. The number of such incidents has continuously declined since 2016, when offenders in over ****** crimes were under the influence of narcotic substances.

In 2024, the number of crimes that were committed using the internet, digital devices, or other telecommunication technologies increased in Russia. The number of technology crimes was the highest over the observed period.

Russia's female homicide rate was slightly below four crimes per 100 thousand population in 2019, marking a decline from 2015. That was more than three times lower than the number of male homicides per 100 thousand inhabitants. The total homicide rate in the country reached 4.7 per 100 thousand people in 2020.