The County Longitudinal Template is a tool that enables researchers to allow for temporal changes in the geographic boundaries of counties in the United States. These data provide a decade-by-decade account of the administrative status of each county, starting in 1990 and tracing each census period back through 1840. The first four variables are the county name, ICPSR state code, FIPS code, and ICPSR county code. These four variables allow the researcher to select the counties for the state in question. The next 16 variables are ID variables for each census year, 1990 back to 1840. The last 13 variables are boundary change flags for each census year from 1960 to 1840.

Criminal justice research may require merging disparate data sources that have no common match keys. The Law Enforcement Agency Identifiers Crosswalk (LEAIC) file facilitates linking reported crime data with socio-economic data. It does this by having a record for each law enforcement agency, law enforcement reporting entity, and access identifier for the National Crime Information Center (NCIC). Essentially, if an entity (law enforcement agency or section of a law enforcement agency) is capable of reporting crime information, it is included in the file. The LEAIC records contain common match keys for merging reported crime data and Census Bureau data. These linkage variables include the Originating Agency Identifier (ORI) code, Federal Information Processing Standards (FIPS) state, county and place codes, and Governments Integrated Directory government identifier codes.

Researchers have long been able to analyze crime and law enforcement data at the individual agency level (see UNIFORM CRIME REPORTING PROGRAM DATA: [UNITED STATES] [ICPSR 9028]) and at the county level (see, for example, UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1997 [ICPSR 2764]). However, analyzing crime data at the intermediate level, the city or place, has been difficult. To facilitate the creation and analysis of place-level data, the Bureau of Justice Statistics (BJS) and the National Archive of Criminal Justice Data (NACJD) created the Law Enforcement Agency Identifiers Crosswalk. The crosswalk file was designed to provide geographic and other identification information for each record included in either the Federal Bureau of Investigation's Uniform Crime Reports (UCR) files or BJS's Directory of Law Enforcement Agencies. The main variables for each record are the UCR originating agency identifier number, agency name, mailing address, Census Bureau's government identification number, UCR state and county codes, and Federal Information Processing Standards (FIPS) state, county, and place codes. These variables make it possible for researchers to take police agency-level data, combine them with Bureau of the Census and BJS data, and perform place-level, jurisdiction-level, and government-level analyses.

The aim of this study was to provide a systematic empirical assessment of three basic organizational premises of Community-Oriented Policing (COP). This study constructed a comprehensive data set by synthesizing data available in separate national data sets on police agencies and communities. The base data source used was the 1999 Law Enforcement Management and Administrative Statistics (LEMAS) survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1999 (ICPSR 3079)], which contained data on police organizational characteristics and on adoption of community-oriented policing procedures. The 1999 survey was supplemented with additional organizational variables from the 1997 LEMAS survey [LAW ENFORCEMENT MANAGEMENT AND ADMINISTRATIVE STATISTICS (LEMAS), 1997 (ICPSR 2700)] and from the 1996 Directory of Law Enforcement Agencies [DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: UNITED STATES]. Data on community characteristics were extracted from the 1994 County and City Data Book, from the 1996 to 1999 Uniform Crime Reports [UNIFORM CRIME REPORTING PROGRAM DATA. [UNITED STATES]: OFFENSES KNOWN AND CLEARANCES BY ARREST (1996-1997: ICPSR 9028, 1998: ICPSR 2904, 1999: ICPSR 3158)], from the 1990 and 2000 Census Gazetteer files, and from Rural-Urban Community classifications. The merging of the separate data sources was accomplished by using the Law Enforcement Agency Identifiers Crosswalk file [LAW ENFORCEMENT AGENCY IDENTIFIERS CROSSWALK [UNITED STATES], 1996 (ICPSR 2876)]. In all, 23 data files from eight separate sources collected by four different governmental agencies were used to create the merged data set. The entire merging process resulted in a combined final sample of 3,005 local general jurisdiction policing agencies. Variables for this study provide information regarding police organizational structure include type of government, type of agency, and number and various types of employees. Several indices from the LEMAS surveys are also provided. Community-oriented policing variables are the percent of full-time sworn employees assigned to COP positions, if the agency had a COP plan, and several indices from the 1999 LEMAS survey. Community context variables include various Census population categories, rural-urban continuum (Beale) codes, urban influence codes, and total serious crime rate for different year ranges. Geographic variables include FIPS State, county, and place codes, and region.

The crosswalk file is designed to provide geographic and other identification information for each record included in either the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program files or in the Bureau of Justice Statistics' Census of State and Local Law Enforcement Agencies (CSLLEA). The main variables each record contains are the alpha state code, county name, place name, government agency name, police agency name, government identification number, Federal Information Processing Standards (FIPS) state, county, and place codes, and Originating Agency Identifier (ORI) code. These variables allow a researcher to take agency-level data, combine it with Bureau of the Census and BJS data, and perform place-level and government-level analyses.