Unique ID: 2015107

Division: Office of Information and Regulatory Affairs
Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Computerized Criminal History Records

Using computerized Criminal History Records

The project obtains, parses and standardizes computerized criminal history records (CCHR) and transforms the relatively unstructured record of arrest and prosecution, which varies widely among the 50 State, into a standardized statistical research database. The project has created a comprehensive set of rules that reshape the data files in each State into a common format, and it also has compiled a set of rules for translating information contained in the CCHR into a common format for variables such as type of offense, disposition, and other elements in the CCHR.

Project Objective:

Scientific / research

Project Outcomes:

Statistical reports on recidivism; methodological reports on the creation of the database, estimation, and validity checks.

Statistical Area

Demographic and social

Project Sources
Project Sources
Type Of Institution: National statistical office
Big Data Source: Other
Region: North America
Country Area: United States
Id Country Regional: country
Partnerships
Partnerships
Data Providers: Intermediary Big Data provider
Other Partners: Research or academic institute
Accessing Data
Accessing Data
Data Access Rights: Only for this project
Intermediary Comments: We use two intermediaries: One of which parses the State records into a common file format, the other of which standardizes fields into a research database.
Data Coverage
Data Coverage
Data Coverage: All available data
Coverage Geo Pop: Whole country / high % of market
Coverage Period: Varies with the subprojects, but in general from 2004 to 2015.
Data Quality
Data Quality
Quality Framework: Quality of source/input
Quality Aspects Evaluated: Privacy and Security, Completeness, Usability, Time Factors, Validity, Accuracy, including selectivity
Validation Comments: We use internal and external validity criteria. Internally, we review coding and classifications of values into the common research format; externally, we compare estimates with other sources of data.
Data Quality Concerns Comments: Some records have incomplete dispositions and this could affect estimates of certain measures of recidivism, such as convictions. We are employing external validity checks to compare estimates derived from the CCHR with other statistics.
Methodology
Methodology
Methods Used: Supervised learning
Technologies
Technologies
Technologies: Column store database
Other
Other
Income Level: High-income
Iso: US
Timeframe To Produce Indicator: NA
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