Deliberate fires: From data to intelligence

Area of Science:

• * Criminology and Fire Science
• * Data Analysis and Intelligence Studies

Background:

• * Deliberate fires pose a global challenge, causing insecurity and resource depletion.
• * Current case-by-case responses to deliberate fires are largely ineffective.
• * Repetitive patterns in deliberate fires suggest an intelligence-led approach is needed.

Purpose of the Study:

• * To develop and present a procedure for generating intelligence from fire event data.
• * To demonstrate the effectiveness of an intelligence-based approach in understanding and reducing deliberate fires.
• * To lay the groundwork for an integrated, real-time intelligence process for fire monitoring.

Main Methods:

• * Creation of a specific dataset for fire events of deliberate or unknown origin.
• * Application of tailored analytical methods to the fire event dataset.
• * Implementation of the procedure on a nine-year dataset from Geneva, Switzerland.

Main Results:

• * Rudimentary data and simple processing yield valuable, previously unsuspected intelligence.
• * The intelligence gained enhances responders' understanding of deliberate fire incidents.
• * The approach supports improved decision-making for agencies dealing with fires.

Conclusions:

• * An intelligence-based strategy is promising for tackling and reducing repetitive deliberate fires.
• * The study provides a foundation for a systematic, real-time fire intelligence process.
• * Collaboration, data sharing, and coordinated actions among institutions are encouraged to mitigate fire-related harm.