Intelligence Gathering and Crime Analysis

The following excerpt from Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, written by Colleen McCue, is reprinted with permission from Butterworth-Heinemann, a division of Elsevier; copyright 2007. Download the complete chapter for free: "Law enforcement data mining and predictive analysis: Techniques and tools."

Revealing its origins and widespread use in business, data mining goes by many names, including knowledge management, knowledge discovery, and sense making. Data mining is "[a]n information extraction activity whose goal is to discover hidden facts contained in databases." In other words, data mining involves the systematic analysis of large data sets using automated methods. By probing data in this manner, it is possible to prove or disprove existing hypotheses or ideas regarding data or information, while discovering new or previously unknown information. In particular, unique or valuable relationships between and within the data can be identified and used proactively to categorize or anticipate additional data. Through the use of exploratory graphics in combination with advanced statistics, machine learning tools, and artificial intelligence, critical "nuggets" of information can be mined from large repositories of data.

Discovery and prediction

When examining drug-related homicide data several years ago, we decided to experiment with different approaches to the analysis and depiction of the information. By drilling down into the data and deploying the information in a mapping environment, we found that the victims of drug-related homicides generally did not cross town to get killed. While it makes sense in retrospect, this was a very surprising finding at the time. This type of analysis of homicide data had not been considered previously, although after it had been completed it seemed like a logical way to view the information.

After further analysis of the data, we were able to generate a prediction regarding the likely location and victim characteristics of one of the next incidents. Within the next twelve hours, a murder was committed with characteristics that were strikingly similar to those included in the prediction, even down to the fact that the victim had not crossed town to get killed.

This embodies the use of data mining and predictive analytics in law enforcement and intelligence analysis. First, the behavior was characterized and, through this process, new information was "discovered." The idea of looking at the information in this fashion to determine the relationship between the victim's residence and subsequent murder location made sense, but had not been done before. Adding value to crime information in this manner deviates significantly from the traditional emphasis on counting crime and creating summary reports. By looking at the data in a different way, we were able to discover new facets of information that had significant operational value.

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Second, by characterizing the behavior, it could be modeled and used to anticipate or predict the nature of future events. The ability to anticipate or predict events brings a whole new range of operational opportunities to law enforcement personnel. Much as in the movie Minority Report, once we can anticipate or predict crime, we will have the ability to prevent it. Unlike the movie, however, crime prevention can be effected through the use of proactive deployment strategies or other operational initiatives, rather than proactive incarceration of potential offenders. On the other hand, the ability to characterize risk in potential victims provides an opportunity for targeted, risk-based interventions that ultimately can save lives and provide safer neighborhoods for all, a topic that will be covered in Chapter 11.

This example, although a somewhat odd and inelegant use of "brute force" analytics, embodies the essence of data mining and predictive analytics within the public safety arena. Through the use of these powerful tools, we can understand crime and criminal behavior in a way that facilitates the generation of actionable models that can be deployed directly into the operational environment.

Confirmation and discovery

At a very simple level, data mining can be divided into confirmation and discovery. Criminal investigation training is similar to case-based reasoning. In case-based reasoning, each new case or incident is compared to previous knowledge in an effort to increase understanding or add informational value to the new incident. In addition, each new incident is added to this internal knowledge base. Before long, an investigator has developed an internal set of rules and norms based on accumulated experience. These rules and norms are then used, modified, and refined during the investigation of subsequent cases. Analysts and investigators will look for similarities and known patterns to identify possible motives and likely suspect characteristics when confronted with a new case. This information is then used to understand the new case and investigate it.

These internal rule sets also allow an investigator to select suspects, guide interviews and interrogations, and ultimately solve a case. These existing rule sets can be evaluated, quantified, or "confirmed" using data mining. In addition, internal rule sets can be modified and enhanced as additional information is added and integrated into the models. Finally, as predictive algorithms are developed, we can extend beyond the use of data mining for simple characterization of crime and begin to anticipate, predict, and even prevent crime in some cases.

Many seasoned homicide investigators can identify a motive as the call comes in, based on the nature of the call, geographic and social characteristics of the incident location, and preliminary information pertaining to the victim and injury patterns. For example, a young male killed in a drive-by shooting in an area known for open-air drug markets is probably the victim of a drug related homicide. Additional

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