“Community risk reduction” (CRR) as a fire prevention strategy has existed in the United States for a long time—longer than me, and that’s saying a lot! But there’s one aspect of CRR that we continue to struggle with, and it affects all the other aspects of this strategy: We must be able to accurately identify what’s at risk before developing or purchasing prevention programs intended to mitigate that risk. We can make the process as easy or as complicated as we like, depending on what kind of resources we have, but it still boils down to figuring out where we should place our efforts to accomplish the most.
The opposite approach is to develop prevention programs that we like, or strategies we think will work. But when we do that, we may end up creating a program that no one really needs. I could have an excellent program that uses a clown to teach fire safety, but it’s likely to be ineffective on elderly or non-English speaking audiences—who may be the most at-risk audience in my response area.
Performing a risk assessment can be as simple as asking firefighters where they most frequently respond, because what we’ve faced in the past is what we’ll likely face in the future—and so we should be doing something proactive to prevent it. Done at the station level, this kind of risk assessment can differentiate between the risks in various places in our communities. One area may respond frequently to assisted-living centers, while another may have industrial property to protect. A blanket approach to prevention strategy development often won’t cover all our bases.
Even stipulating that simple risk assessments are better than nothing, fire prevention personnel should be aware that there are far more sophisticated risk-assessment tools available if we have the resources to take advantage of them.
Imagine for a moment that I’m sitting in my fire prevention office thinking I know all about my community. Armed with national data, I know that my high-risk audiences are elderly or very young, and they have low income levels in common. I even know that minority populations tend to have more fire losses, perhaps in part because of their income levels.
But there could be more involved. If I can obtain information about shopping preferences and cultural differences, I can refine my prevention strategies to appeal to the highest-risk audiences much more specifically, and reach them in places that they tend to go on a regular basis. A simple form of this type of risk analysis was conducted when I worked in Portland, Ore., many years ago, and it revealed that we should target places of worship to reach African American audiences, because they congregated there frequently and had high levels of trust in their religious institutions.
Today, there are more sophisticated options available. Two examples I know of come from ESRI and the Buxton Company.
ESRI (www.esri.com) is a software company that offers a range of data, tools and services that can refine risk assessments well beyond our traditional method of looking at previous fire incident data. ESRI’s technology provides “layers” of information that create a “tapestry” of neighborhoods using 65 unique market segments. Information about the people in each of these neighborhood groups includes demographics, lifestyles, and consumer and business segments. These groupings provide a great deal of information that not only identifies who is at risk, but how best to reach them.
ESRI offers a range of products and consulting services for getting the most out of these data (learn more at www.esri.com/data/esri_data/index.html). The options range from simple and free to very sophisticated and costly. (You can start by accessing these layers for free: www.arcgis.com/home/item.html?id=f5c23594330d431aa5d9a27abb90296d.)
Another company that does this kind of work is Buxton (www.buxtonco.com). The company has a history in the private sector, but has more recently been branching out into the public safety world. I’ve seen how Buxton assisted Philadelphia and Cleveland, Ohio, with their risk assessments—and it’s impressive. The results are incorporated into a “predictive analytics” package that can tell the fire department, address by address, which homes in their jurisdiction exhibit the psychographics (lifestyles, attitudes, behaviors and financial attributes) of those households that, based on past incidents, are most likely to have a fire in the future. These analytics can be used for prevention purposes, such as the distribution of smoke detectors or outreach and educational campaigns.
Buxton was able to take previous fire incident data, append household-level data and identify those households where fires were most likely to occur in the future. The correlation goes well beyond our previous knowledge levels about income and ethnicity, and leverages the same type of household-level psychographics being used by large, private-sector entities for the purposes of better understanding their customers and patients.
In the Right Direction
It’s clear that we can be far more sophisticated in our efforts to understand who our audiences really are, and how to reach them. It takes more resources, to be sure, but if we’re going to be successful with our prevention strategies, we might consider spending more of our time and money up front where it will do more good. Because unless we’re doing a good job of assessing our real risks, we might just be headed full speed in the wrong direction.