Name two data sources commonly used by FLSEs to identify high-risk populations.

• A. Satellite imagery data and grocery store data
• B. Hospital admission data and weather data
• C. School enrollment data and tax records
• D. Local fire department incident data and census/community demographic data

Answer: (D)

Using data that link risk events to who is at risk helps FLSEs identify high-risk populations. The best data combination for this is local fire department incident data paired with census or community demographic data. Fire department incident data show where fires and other emergencies happen, how often they occur, and what kinds of incidents are most common. This reveals real hotspots and patterns in the built environment, occupancy types, and times when risk is higher.

Census or community demographic data add the people side: age distributions, housing types, income levels, occupancy density, and other characteristics that affect vulnerability and response. By combining where incidents happen with who lives there, you can pinpoint neighborhoods or groups that are more likely to experience fires or injuries and tailor education, prevention messaging, and resource allocation accordingly.

Other data sources don’t provide the same actionable mix. Satellite imagery and grocery store data can show environment and consumer patterns but don’t directly measure incident risk tied to specific populations. Hospital admission data and weather data offer outcomes or environmental context, but they don’t map risk to the characteristic makeup of a community as effectively. School enrollment data and tax records aren’t as directly useful for identifying current high-risk populations or for guiding targeted prevention efforts.

In short, pairing actual incident patterns with demographic context gives a practical, targeted view of where prevention and outreach will have the most impact.