Transforming Fire Reports into Actionable Data: A Polish Case Study

Fire service reports are a treasure trove of information, but much of it remains hidden in unstructured text. In a recent study, we tackled this challenge head-on. The work focuses on extracting valuable insights from the narrative portions of fire reports, which are often overlooked due to the complexity of processing unstructured data.

The Challenge

Firefighters document every emergency intervention, creating both electronic and paper reports. While structured data like the type of accident or the number of victims is easily analyzed, the unstructured, narrative sections—such as descriptions of the event, weather conditions, and rescue activities—are rarely used. This is because traditional tools struggle to make sense of free-text descriptions, leaving valuable information untapped.

The Solution: Information Extraction System

The research introduces a novel Information Extraction System designed to transform unstructured text from fire reports into structured, analyzable data. The system processes the narrative portions of reports, converting them into a format that can be used for detailed analysis and decision-making.

Key Steps in the Process

1. Text Pre-processing: The system first segments the text into sentences and classifies them into categories like “operation,” “equipment,” “damage,” and “meteorological conditions.” This step ensures that each sentence is labeled correctly, making it easier to analyze.
2. Taxonomy Induction: The system then builds a taxonomy—a structured classification of terms and phrases—based on the content of the reports. For example, it identifies key attributes like “hydrant location” or “type of vehicle involved in an accident.”
3. Schema Construction: Using the taxonomy, the system creates a structured database schema. This schema defines how the extracted information will be stored, making it easier to query and analyze.
4. Information Extraction: The system uses predefined rules to extract specific pieces of information from the text. For instance, it can identify the location of a hydrant or the types of vehicles involved in a car accident.
5. Analysis: Finally, the structured data is used to generate useful statistics and insights. For example, the system can determine the most common types of vehicles involved in accidents or the efficiency of hydrants in different locations.

Real-World Applications

The system was tested on real data from the Polish Fire Service, and the results were impressive:

• Text Segmentation: Achieved an F-measure of 95.5%, meaning the system accurately identified sentence boundaries.
• Text Classification: Achieved an F-measure of 90%, accurately categorizing sentences into relevant classes.

These results demonstrate that the system can effectively extract and structure information from fire reports, enabling fire service analysts to perform more detailed and accurate analyses.

Why This Matters

By transforming unstructured text into structured data, this system unlocks valuable insights that were previously hidden. Fire service analysts can now:

• Identify patterns in rescue operations.
• Improve resource allocation.
• Enhance emergency response planning.

For example, the system can reveal which types of equipment are most frequently used in car accidents or which areas have the most efficient hydrants. This information can help fire departments make data-driven decisions, ultimately improving their ability to save lives and protect property.

Conclusion

The work represents a significant step forward in the analysis of fire service reports. By leveraging advanced information extraction techniques, his system transforms raw, unstructured text into actionable data. This not only enhances the efficiency of fire services but also provides a model for other fields where unstructured data is a challenge.

For more details, you can read the full study here.