Learning Guide
Law Enforcement Agency Identifiers Crosswalk

Description of the LEAIC

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 other types of data, such as socio-economic data. It does this by including a record for each 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 agency-reported crime data and other government 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.

Let's look at the example below. Table 1 shows the population of five cities which are identified by letters. Table 2 shows the unemployment rate in the same five cities now identified by numbers. We would like to combine these tables and see how population and unemployment are related. However, none of the variables in our data sets match. This is where the crosswalk comes in. Table 3 is our crosswalk (the LEAIC for this example). It contains a column with our identifiers from Table 1, a column with our identifiers from Table 2, and a (supplementary) column giving us the name of the city. Now we have a data set that lets us match the information from Table 1 with Table 2. We can merge Table 1 with Table 3 because the column City_symbol is present in both. From this merged file, it is possible to merge with Table 2 because now the column City_number is present in both. Table 4 shows what the final data set looks like.

Table 1

City_symbol Population
A 681,170
B 8,538,000
C 1,568,000
D 864,816
E 120,782

Table 2

City_number Unemployment Rate
1 5.9%
2 3.9%
3 5.9%
4 2.7%
5 1.8%

Table 3

City_symbol City_number City
A 1 Washington D.C.
B 2 New York City
C 3 Philadelphia
D 4 San Francisco
E 5 Ann Arbor

Table 4

City_symbol City_number City Population Unemployment Rate
A 1 Washington D.C. 681,170 5.9%
B 2 New York City 8,538,000 3.9%
C 3 Philadelphia 1,568,000 5.9%
D 4 San Francisco 864,816 2.7%
E 5 Ann Arbor 120,782 1.8%

The original motivation for creating the LEAIC file was to enable linking Uniform Crime Reporting (UCR) Program data and/or National Incident-Based Reporting System (NIBRS) data, produced by the Federal Bureau of Investigation (FBI), with socio-economic data produced by the Census Bureau (e.g., to examine crime rates and poverty information at city level). A file such as the LEAIC is necessary to facilitate this type of research because of the different coding systems used by the data sources.

While FBI crime data contains a wealth of information, it has a number of limitations that restrict its use - limitations alleviated by the LEAIC. The FBI's codes are not used by other agencies. For example, a researcher who would like to examine the relationship between poverty and crime would be unable to do so using only FBI data. Government poverty data (available through the Census) uses Federal Information Processing Standards (FIPS) codes to specify locations whereas FBI data uses Originating Agency Identifier (ORI) codes to indicate its agencies. This disconnect prevents pairing poverty data (or almost any other government data set) with FBI crime data because the identifying codes do not match. The LEAIC solves this problem because for each ORI, it contains the matching FIPS code. LEAIC thus makes it possible to merge with both FBI and other government data sets.

If the above researcher wanted to examine city level poverty and crime, they would encounter another problem. The FBI does not have codes for places - cities, townships, etc. The smallest geographical area available is the county level, meaning that analyses at smaller units are not possible. In addition, some cities have multiple reporting agencies. For example, the City of Philadelphia has multiple police agencies (e.g. Philadelphia Police, Philadelphia Sheriff, University of Pennsylvania Police). Using FBI data alone would allow neither analysis at a city level nor assurance that all crime in the city is properly aggregated, as we would not know which agencies are in which cities.

The LEAIC solves these problems by allowing a match between an individual agency from FBI data and a city from another data source (e.g. the Census). The agencies of Philadelphia Police, Philadelphia Sheriff, and University of Pennsylvania Police, for example, could be matched with poverty data for the City of Philadelphia and aggregated to the city level. At this city level, it is possible to analyze how city poverty affects city crime. The key to these analyses is the LEAIC's ability to match FBI data with other government data. The Census Bureau typically uses FIPS codes to geographically identify counties and states. In addition, the FIPS system has codes for places (county subdivisions, cities, census-designated places). The LEAIC file "crosswalks" the UCR/NIBRS and FIPS state and county codes; it also adds FIPS place codes to law enforcement agency records. Consequently, a city-level analysis of crime and poverty could be done by merging UCR crime data to the LEAIC file by ORI code (contained in both the UCR/NIBRS and LEAIC files) and then merging the result to Census data using FIPS state and place codes (contained in both the Census and LEAIC).

In this learning guide, we will examine crime rates of different age and gender groups across states. There are many other uses of the LEAIC. For example, it has information about congressional and judicial districts, allowing you to examine crime in those jurisdictions. As a collection of reporting law enforcement agencies, it serves as a census of police departments in the United States. Other uses are limited only by the availability of proper match keys on the data you're interested in. When considering whether to use the LEAIC, check to see that both of your data sets have match keys in the LEAIC. If they do, then the LEAIC is the right choice for your project.