The goals of this data collection were to provide information on robbery-related security measures employed by financial institutions, to identify factors that contribute to robbery, and to study the correlates of case disposition and sentence length of convicted robbers. The collection compares banking institutions that have been robbed with those bank offices that have not been robbed to provide information on factors that contribute to these robberies. The office-based file includes variables designed to measure general office characteristics, staff preparation and training, security measures, characteristics of the area in which the banking institution is located, and the robbery history of each institution. The incident-based file includes variables such as the robber's method of operation and behavior, the employee's reaction, the characteristics of the office at the time of the robbery, and the apprehension of the offender. Also included is information on the status of the investigation, reasons involved in solving the robbery, status of prosecution, ultimate prosecution, and sentence in length.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 4 release notes:Adds data for 2018Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, cite it.Version 5 release notes:Adds data for 2019Note that the number of months reported variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the "status" variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 4 release notes:Adds data for 2018Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 10 release notes:Adds 2022 dataVersion 9 release notes:Adds 2021 data.Version 8 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Property Stolen and Recovered data they release. Changes .rda file to .rds.Version 7 release notes:Adds data for 2006.Version 6 release notesChanges release notes description, does not change data.Version 5 release notes:Adds data for 2019Note that the number of months reported variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the "status" variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 4 release notes:Adds data for 2018Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

Context

Maritime piracy and armed robbery against ship are one of the contemporary challenges of the maritime industry. These two phenomena have a global impact on maritime trade and security. Nowadays, the Gulf of Aden and the Indian Ocean are considered high risk areas in terms of piracy and armed robbery against ships activities. In this regard, both the international community and the coastal States of the region have deployed every effort to try to find ways to address the problem.

Content

This dataset contains information from more than 7,500 maritime pirate attacks that took place between January 1993 and December 2020, as well as country indicator data for the same time period. The pirate attack data was collected from the International Maritime Bureau (IMB), tidied, and augmented with geospatial data. The country indicator data was gathered from a variety of sources, notably The World Bank. The data is contained in Comma Separated Value (CSV) files.

pirate_attacks.csv

• Date [Key] - Date of Attack. Used as a key with the Country Matrix data frame.
• Time - Time the attack took place, either in UTC or Local Time.
• Longitude - Longitude where the attack took place.
• Latitude - Latitude where the attack took place.
• Attack Type - Either NA (Missing), Attempted, Boarding, or Hijacked.
• Location Description - A text description of the location. With attacks taking place at sea, it is not as simple as just naming a city or town.
• Nearest Country [Key] - The country code whose shore is closest to the attack. The resolution is around 1 km, it can be much better depending on how detailed the mapping of the coast is in the vicinity.
• EEZ Country [Key] - The Exclusive Economic Zone country code in which the attack took place, if it took place within an EEZ.
• Shore Distance - Distance in kilometres to the shore from the attack location. This is the true geographic distance over the surface of the earth.
• Shore Longitude - The longitude of the closest point on the shore to the attack.
• Shore Latitude - The latitude of the closest point on the shore to the attack.
• Attack Description - The text description of the attack if it exists.
• Vessel Name - The name of the ship which was attacked if it is known.
• Vessel Type - The type of vessel attacked if known.
• Vessel Status - The status of the ship at the time it was attacked. Either NA (Missing), Berthed (Tied to a berth), Anchored (anchored at sea or in a harbour), or Steaming (ship underway).

country_indicators.csv

• Country [Key] - The country in ISO3 country code format.
• Corruption Index - Corruption Perceptions Index.
• Homicide Rate - Total Intentional Homicides per 100,000 people.
• GPD - Gross Domestic Product (US Dollars).
• Total Fisheries Per Ton - Total Fisheries Production (metric tons).
• Total Military - Total Number of Armed Forces personnel.
• Population - Country Population.
• Unemployment Rate - Percentage of the Country Unemployed.
• Total GR - Total Government Revenue. An indication of how well the country collects taxes.
• Industry - Industrial contribution to total GDP.

country_codes.csv

• Country [Key] - The country in ISO3 country code format.
• Region - The region the country is in.
• Country Name - The English country name.

https://raw.githubusercontent.com/newzealandpaul/Maritime-Pirate-Attacks/main/img/dataset_schema.png" alt="dataset_schema">

Acknowledgements

This data was collected and arranged by: Benden, P., Feng, A., Howell, C. and Dalla Riva, G.V., 2021. Crime at Sea: A Global Database of Maritime Pirate Attacks (1993–2020). Journal of Open Humanities Data, 7, p.19. DOI.

The reuse potential includes its use by anti-piracy organisations and researchers, as well as commercial businesses, in the understanding and prevention of maritime piracy. This dataset is available through Zenodo and Github.

Inspiration

1. Can we predict a probability for target values (attempted, boarded , hijacked) for a sea-going vessel depending on her (ship) route, type of vessel, flag, cargo on board, deadweight tonnage, size, speed...? (FYI there are a few free of charge API AIS Live services in the web where we can get live info on vessels traffic and details: Marine Traffic, VesselFinder, Vesseltracker

2. Since 2015th the feature attack_description contains a text description of the attack. Probably we can extract some valuable information and augment our dataset with new features that have a correlation with the t...

These data contain records of guideline computations and adjustments for each count of conviction for criminal defendants who were sentenced pursuant to provisions of the Sentencing Reform Act (SRA) of 1984 and reported to the United States Sentencing Commission (USSC) during fiscal year 2005. The data are one of two supplementary files that should be used in conjunction with the primary analysis file, which contains records for all defendants sentenced under the guidelines. These data can be linked to the primary analysis file using the unique identifier variable USSCIDN. The number of records for a defendant in the current data corresponds to the total number of guideline computations, which may or may not equal the total counts of conviction for that defendant, dependent upon the grouping rules of the particular guideline in question (see Section 3D1.2 of the guidelines manual). As an example, a defendant with five counts of drug trafficking will only have one guideline computation because each of the drug weights for each count are simply added together and only one calculation is necessary. However, if a defendant has five counts of bank robbery, he or she will have five separate guideline computations because bank robbery is considered to be a nongroupable offense. The data were obtained from the United States Sentencing Commission's Office of Policy Analysis' (OPA) Standardized Research Data File. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.