Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2019

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 2018 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. 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.

版本6更新说明：本次仅更新更新说明文本，不改动数据集本身。
版本5更新说明：新增2019年数据集。请注意，自2018年起，「报告月数」变量出现显著变化。这可能源于统一犯罪报告（Uniform Crime Reporting, UCR）程序中「状态」变量的报告规则调整——该变量用于计算缺失县份的月数（本次所用代码未发生变更）。因此，该变量在2018年前后的数据不具备可比性。
版本4更新说明：新增2018年数据集。
版本3更新说明：新增Excel格式数据集；同时修改项目名称，避免将本数据集与美国国家刑事司法数据档案馆（National Archive of Criminal Justice Data, NACJD）出品的数据集混淆。
版本2更新说明：新增2017年数据集；新增「报告月数」变量，用于说明该机构全年上报数据的月份数量。

「被盗与追回财物」数据集属于统一犯罪报告（Uniform Crime Reporting, UCR）项目数据集，涵盖犯罪数量（包含谋杀、强奸、抢劫、入室盗窃、盗窃/偷窃及机动车盗窃）、涉案价值以及犯罪子类别的相关信息。例如，抢劫可细分为公路抢劫、银行抢劫、加油站抢劫等子类。
数据集主体内容围绕盗窃犯罪展开。盗窃犯罪进一步细分为入店行窃、自行车盗窃、建筑内盗窃及抢夺钱包等子类。对于部分被盗物品（如现金、珠宝及贵金属、枪支），数据集同时提供被盗财物价值与追回财物价值两项数据。
该数据集也被称为「A表补充件（已知与上报犯罪）」。本数据集所有数据均直接从美国联邦调查局（Federal Bureau of Investigation, FBI）获取，格式为文本文件或.DTA格式文件。

数据集可能存在数据不准确的情况，尤其是以「auto」为前缀的列组。为减少（但无法完全消除）数据错误，针对以「offenses」或「auto」为前缀的列组，将以下常见数据录入错误值替换为NA（例如数值超过对应机构的人口规模，或远高于同一机构其他犯罪类型或月份的数值）：1000、2000、3000、4000、5000、6000、7000、8000、9000、10000、20000、30000、40000、50000、60000、70000、80000、90000、100000、99942。上述清洗操作未应用于以「value」为前缀的列。

对于所有数值型列，将负向标识值（例如用「j」代表-1）替换为其对应的实际负数值。这类负向标识值并未出现在美国联邦调查局针对本数据集的代码手册中，但实际存在于数据中。本次数据处理采用了美国联邦调查局《已知犯罪与逮捕结案数据》的代码手册中的相关规则。

为便于与其他数据集合并，本数据集已与执法机构标识符对照（Law Enforcement Agency Identifiers Crosswalk, LEAIC）数据集进行了合并。LEAIC数据集可为数据补充联邦信息处理标准（Federal Information Processing Standards, FIPS）编码（州、县及区域）以及机构类型/子类型信息。若某机构过往曾使用过不同的FIPS编码，请核实本数据中的编码是否为最新版本。