In 2024, there were 301,623 cases filed by the National Crime Information Center (NCIC) where the race of the reported missing person was white. In the same year, 17,097 people whose race was unknown were also reported missing in the United States. What is the NCIC? The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide. Missing people in the United States A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

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.

As of June 2021, the cumulative number of cases of missing people amounted to 267 thousand. More specifically, the chart displays the total number of reports recorded by the Police between 1974 and June 2021. In 1974, a database recording the number of missing people cases was started.

Context

Missing 411 is a series of books that describe people who go missing in unusual circumstances. The books contain a plethora of very precise, and very comprehensive data about each victim, and the circumstances surrounding each particular disappearance.

Content

Everything that is stated directly or can be inferred about a victim. For example, if people go missing in various parts of Arizona, "tablelands" will often be true, since this is a prominent geological feature of Arizona. However, it will not be marked true unless there are tableland features apparent in the case itself, or from images of the area in which the case happened.

Acknowledgements

Thanks to David Paulides for serious empirical rigor. His data is fun to catalogue because there is very little speculation to sort through. He focuses on facts in the form of statements taken from newspapers, police reports, park service reports, and eye-witness accounts. David does not muddle his books with theories and speculation about unrelated phenomena.

Inspiration

I want to see machine learning and human inference used to identify patterns in this data that can help draw an increasingly clear profile of who the attacker is, what causes people to go missing, what it means for society more broadly. Possible questions to answer:

1. What factors link together to form a satisfying explanation about our attacker?
2. What unidentified patterns are in the data?
3. How does this data correlate with larger datasets of generally related phenomena?

De facto states—polities, such as Abkhazia (Georgia) or the Donetsk People’s Republic (Ukraine), that appropriate many trappings of statehood without securing the status of full states—have been a constant presence in the postwar international order. Some de facto states, such as Northern Cyprus, survive for a long period of time. Others, including Tamil Eelam in Sri Lanka, are forcefully reintegrated into their parent states. Still others, such as Aceh in Indonesia, disappear as a result of peacemaking. A few, such as Eritrea, successfully transition to full statehood. What explains these very different outcomes? I argue that four factors account for much of this variation: the extent of military assistance that separatists receive from outside actors, the governance activities conducted by separatist insurgents, the fragmentation of the rebel movement, and the influence of government veto players. My analysis relies on an original dataset that includes all breakaway enclaves from 1945 to 2011. The findings enhance our understanding of separatist institutional outcomes, rebel governance, and the conditions that sustain nonstate territorial actors.