Similarities and Anomalies

Both similarities and anomalies play crucial roles in recognizing and identifying crime patterns. 

Similarities, such as consistent modus operandi, geographic location, and victim profiles, often help in linking multiple incidents, suggesting that they are the work of a single offender or criminal group. On the other hand, anomalies in crime data—unusual spikes or patterns that deviate from statistical norms—can serve as red flags that a serial crime pattern may emerge. These could be temporal, spatial, or even cross-jurisdictional anomalies that, when detected, prompt further investigation to either confirm or disprove the existence of a serial pattern. Analyzing similarities and anomalies provides a comprehensive approach to identifying and understanding crime patterns.

Similarities or anomalies may first be noticed in qualitative data rather than through statistical analysis. Review of crime and investigative data by officers or analysts with subject matter expertise may trigger further investigation. The similarities of anomalies may exist across non-integrated datasets or have a basis in unwritten historical, tacit knowledge.

Recognition and Identification of Crime Patterns via Similarities

Modus Operandi (MO): The method used to commit the crime can be a critical factor in identifying a pattern. Law enforcement often looks for similarities in how a crime is executed, such as the tools used, time of day, or specific actions carried out during the crime.

Example: If a series of burglaries occur where the perpetrator enters through the back door and disables the alarm system similarly, it may indicate a single offender or group using a consistent MO.

Geographic Location: Crimes occurring in a concentrated area often indicate a pattern. Offenders may be more comfortable or knowledgeable about a particular locale.

Example: Multiple car thefts happening in the same neighborhood within a short time frame may indicate a pattern.

Victimology: Certain characteristics of the victims, such as age, gender, profession, or lifestyle, can indicate a pattern.

Example: If assaults have occurred against women jogging in parks in the evening, a pattern may be forming around this specific victim profile.

Time Frame: Crimes committed during similar times of the day, days of the week, or even seasons can indicate a pattern.

Example: A series of robberies occurring on Friday evenings may suggest that the perpetrator exploits a particular vulnerability associated with that time frame.

Recognition and Identification of Crime Patterns via Anomalies

Temporal Anomalies: Sudden spikes or dips in crime rates within a specific time frame may indicate a serial pattern. For example, a sequence of related crimes occurring in a narrow time window can create a noticeable "peak" in the data.

Example: If a jurisdiction typically reports one or two burglaries per week but suddenly experiences five burglaries over two days, this temporal anomaly could indicate a serial burglar is active.

Spatial Anomalies: This cluster may indicate a serial pattern when a specific geographic area experiences an unusual concentration of similar crimes. Such a spatial anomaly often appears as a 'hotspot' on crime mapping software.

Example: If car thefts are usually spread throughout a city but suddenly concentrate in a specific neighborhood, this spatial anomaly could be the work of a serial car thief operating in that area.

Victimology Anomalies: If data reveal an atypical concentration of victims sharing specific characteristics (such as age, occupation, and gender), this may signify a targeted pattern by a serial offender.

Example: A series of attacks on women who work late shifts in a specific industry within a particular area may represent an anomaly in victimology, suggesting a serial offender.

Cross-jurisdictional Anomalies: Sometimes, an anomaly can appear when data from multiple jurisdictions are analyzed collectively. Crimes that appear unrelated in isolated datasets may show a pattern when combined.

Example: If multiple nearby towns each experience a minor uptick in home invasions, the anomaly may only become apparent when these datasets are combined, potentially revealing a serial offender operating across jurisdictions.

Anomalies in Crime Type: An unusual increase in a specific type of crime or modus operandi within a short period could indicate a serial pattern.

Example: A sudden increase in arson incidents using a specific incendiary device could indicate a serial arsonist at work.

Mixed Anomalies: Sometimes, an anomaly may be a combination of spatial, temporal, and other factors.

Example: A spike in robberies occurring near ATMs late at night over a weekend could be a mixed anomaly indicating a serial pattern.