(This is part two of a two part series.)
After establishing Standards of Coverage for the most probable and consequential routine risks, it is time to begin looking at existing and future locations for fire stations that can facilitate achievement of performance objectives and achieving desired outcomes.
Station placement is mostly based upon the element of time and distance as a method of deploying resources in the best positions when an emergency does arise. There are guidelines to help us determine appropriate times and distances in planning fire stations. Again, NFPA 1710 provides travel time guidelines for both first due unit travel time and time for assembling the effective response force at the scene. According to this standard, fire stations should be planned for based upon a four minute travel time which equates to generally two mile travel distances if calculated using the ISO travel time formula (1.7 * Distance + .65 = Travel Time Minutes). NFPA 1720 provides guidance for response times for volunteer fire departments recognizing that the population density of areas served is generally lower and available resources are also generally lower. NFPA 1720 sets a nine minute response time (which includes any calculation for turnout time, but not including alarm handling time) in areas with a population density greater than 1000 persons per square mile. Response times are longer in areas with a lower population density. If we apply the ISO response time formula to determine the distance that can be covered in nine minutes, we find that it equates to five miles. The ISO rating schedule has generally recognized areas as unprotected if located greater than five miles from a fire station. Some states have modified that maximum distance. Generally five miles should be the greatest travel distance planned for if the desire is to achieve a protected insurance rating and hopefully reduce the impact of fire on the public and the economy. NIST provides a really well documented study considering the differences in performance for fire crews responding to residential fires based upon the variables of crew size, travel time, and arrival time spread.
(Results of this study can be found in the NIST publication, Report on Residential Fireground Field Experiments and can be found at this link: https://firereporting.org/pdfs/NIST%20Report_with%20Append%20HIRES.pdf.)
An example of how to display the assumptions upon which a fire station location study and plan can be based, and the findings justified, is provided in the Basis for Resource Deployment chart. This graphic represents the relationships of time to fire propagation, generally expected property loss and cardiac survival. Although there may be some who will challenge the absolute accuracy of this tool, many of the current standards and service organizations which routinely deal with these common hazards and risk use the same general benchmarks. This model assumes that the primary hazards to plan for in determining the deployment of resources is the cardiac EMS event and the residential structure fire event.
The American Heart Association still teaches that brain cells begin to die between four and six minutes when suffering from a lack of oxygen. Much research now points to a combination of timely CPR and defibrillation for survival of cardiac arrest to hospital discharge. A paper published by the University of Washington Press, and based upon work done at the University of Washington School of Medicine, documents in an article titled, “Sudden Cardiac Arrest: A Cardiac Arrest Survival Formula,” suggests that CPR begun within four minutes of cardiac arrest and defibrillation begun within six minutes should result in a sustainable survival rate of 20 percent to hospital discharge.
The time/temperature curve for fires, better stated today as the fire propagation curve, is undergoing considerable scrutiny and may be changing somewhat due to very valuable and recent research on differences between modern and traditional residential fire interior configuration, construction, furnishings and air flow paths. A generalization would state that there has not been significant data that changes the survivability and economic loss projections for fires confined to room of origin and fires that extend beyond room of origin. There is evidence that in modern structures, the time/temperature curve looks very different and that flashover can occur much earlier in the event.
The biggest changes so far based on recent research are not substantially changing the recommended times for response or even the critical tasking and staffing requirements, but rather in the order of critical tasks and the tactical decisions made during the initial attack on the fire. Generally, we can still apply the guidance from charts like the Fire Propagation Curve identified by Gerard and Jacobsen. Charts very similar appear in many other guidance documents, such as NFPA 1710.
So now we can begin analyzing and planning station locations based upon data acquired in analysis of hazard and risk, performance studies, standards of cover service level objectives and desired outcomes. Utilizing Geographic Information System (GIS) tools for analysis and planning provides the agency the greatest capability to analyze many different but geospatially-related factors and also provides a means of displaying findings in a visual and easily understood manner. The author’s recommended model utilizes GIS tools to create a matrix that represents fire station service areas layered over existing stations and future station areas. From this matrix many different types of analysis can be conducted to answer all three of the original questions posed in the article title; Fire Stations – Where, When and Why?
Where to Place Fire Stations
Fire stations should be located to position appropriate assets — staffing, apparatus, etc. — so that they can be deployed with a reasonable expectation of consistent performance in achieving service level objectives and desired outcomes for the most probable and consequential events facing the community. Building upon the previous information discussed in this article, it could be reasonable for a community to desire to deploy assets with the expectation that CPR and defibrillation can begin within four to six minutes in a high percentage of cardiac arrest events with the desired outcome of survivability to hospital discharge for 20 percent of cardiac arrest victims. It is also reasonable to plan fire stations so that a first due unit arrives within four minutes travel time and an initial effective response force can be deployed and arrive within eight minutes travel time in a high percentage of residential structure fire events with the desired outcome of confining fire to the room of origin. The matrix size and shape for service areas are based upon travel times that facilitate your desired outcomes. The following example is of a matrix laid over an urban to metropolitan community in North Carolina.
The diamond shaped service area is used purposefully. It provides an overlay for a station area that, based upon the distances between points, reflects distance and travel time. Unlike old models of drawing circles on maps, it eliminates overlap and/or unserved open space. This results in greater efficiency in planning locations, which should result in more efficient workloads, unit and station utilization.
The diamond shaped service areas represented in the following example from a modest sized but urban and growing community are based upon a desired four minute travel time.
The use of this type of matrix helps evaluate existing station service areas, look for improvement opportunities (like relocations) and is one of the few tools available to accurately predict future station locations with a direct relationship to performance and outcomes. The value of the service area matrix for future predictions of station locations is that the matrix is not dependent upon an existing road network to make generally accurate predictions. There is a simple relationship to distance, thereby time, inside the diamond shaped matrix. If a station were located in the center of the matrix, the distance to any point along any border or axis following right angles is the same as the distance from the center of the service area to the farthest points at the diamond tips.
In a developed service area with an existing road network, actual performance in achieving travel time objectives will be in the 80 to 90 percentiles, absent some other critical barrier. Utilizing this matrix/grid pattern as part of geospatial analysis helps to capture and process data for standard station response areas — such as incidents, response times and unit utilization — along with other critical community planning and analysis data such as census data, parcel data, building inventory and inspections data, land use and zoning data.
Using this matrix you can also see some simple relationships between travel time performance and number of resources deployed. When considering fire stations you can easily see that it takes four times as many fire stations to reduce travel time in half. In reverse, it only takes one-quarter the number of stations if you were to double the response time goal. Since the grids boundaries match, there are no overlaps and there are no gaps in service area planning as there would be if you used the old “circles on a map” method.
The initial orientation and anchoring of the matrix over a jurisdiction should be based on the knowledge gained from the hazard and risk assessment, from the adopted standards of cover and an analysis of the existing stations service demand and response performance. Once the matrix is oriented, then it can be used, as part of a routine geospatial analysis using GIS tools, to follow development and service demand changes dynamically. One large North Carolina department utilizes the matrix and updates it on an annual basis.
When and Why to Build Stations
Conducting periodic analysis utilizing the matrix provides the information and knowledge needed to answer the second and third questions related to stations, “When? and Why?” You will be able to collect lots of data from many sources using the geospatial tools. As long as there is a common address or coordinates in the various data bases, then the information can be shared, joined, compared, etc. The “Service Area Evaluation Factors” chart provides an example of one method for analyzing existing and unserved station service areas using data that is generally readily available to a fire department.
In this example, the factors are separated into three categories. Each category has a set of factors. These factors are weighted since they are all considerations in making deployment decisions but are not generally equal in terms of impact. Decisions about weighting are local decisions and should be made deliberately and based upon analysis and data, not to drive a specific outcome.
Growth factors are directly related to increasing or decreasing service demand. You can use other growth factors but population and development related factors are easily understood by community decision makers. A relationship to the districts ISO rating is appropriate since there is an economic impact upon the property owners who also typically fund the fire department.
Service demand factors are the results of growth factors. Growth factors are predictive of service demand, while actual service demand factors relate directly to the communities changing need and requirements for service delivery. As growth and service demand factors increase, the need for new resources, or additional resources, inside the station service area increase.
Performance and capability factors reflect our ability to respond to increasing or decreasing demand based upon growth or other changes. As growth and service demand increase, our ability to meet the demand becomes more challenging. When all of the categories are scored and combined, the service area score is determined. The higher the service area score is, then the greater the need for resources.
Results of the service area analysis can be used for many purposes. Obviously, you can produce a visual representation of the most efficient locations for fire stations. But the data, information and knowledge developed from the analysis are extremely valuable. We can extrapolate threshold values that can become the basis for when resources are deployed. As an example, we can determine the values for population, developed land area, property valuations or calls for service (or a number of other values) from the data to determine and communicate at what values the community has historically decided to invest in additional resources. We have all been taught that a community sets its own level of acceptable risk. But historically, that acceptable level of risk has been difficult to define. Using this matrix and methods, we can now define that level and put solid values to its dimensions. As an example we can communicate that our community may have decided to build stations when factors reached these sample levels; 1,000 persons/square mile, or 10,000 population in the service area. Other threshold values may be when total calls for service in a service area exceeds 1,000 calls per year, or property valuation in a service area exceeds $50 Million. All of these values can be found by analyzing each service area and extracting the values from the lowest scoring area where the community has decided to invest in resources, like building a fire station. That establishes the baseline to compare other service areas that do not have a fire station.
The results of service area evaluations can easily be mapped using GIS tools. When mapping, you can display the results laid over existing station locations and unserved areas, you can also lay the results over other maps of stations and incidents. There are many numbers of ways that your analysis can be mapped or otherwise displayed. The choice depends upon the audience and the recommendations which you find are defensible.
In this example, the darker service areas reflect higher service area scores. If this model is maintained dynamically over time, then the agency can communicate changes to the community, whether it is increasing calls for service, population growth, developed properties and the total property evaluation. This model also serves as a good tool for analyzing resources inside existing service areas. As an example, you can answer questions like, would the relocation of a station improve response time within this service area? Are there other nearby assets that can be deployed rather than the expensive solution of building and staffing a fire station? Is the service area demand for service beginning to exceed the capacity of the assets located in existing stations? Sometimes, the answer to increasing demand is not additional stations but may be additional units, or other more effective Community Risk Reduction strategies.
The process of determining the need for new fire stations is becoming much more complex. The investment in deploying combat forces for fire fighting is becoming more and more expensive, sometimes exceeding the ability of the community to fund. The fire chief and community should expect that stakeholders from very diverse areas will ask hard questions and the expansion of services and expenditures are becoming harder and harder to defend. However, the results of not having adequate solutions, whether they depend upon community risk reduction strategies or response capability, have significant consequences.
It is no longer sufficient just to point to a location on a map and say we need a fire station here. We have to be able to defend the location and be able to place assets based upon measurable service level objectives and desired outcomes. We must be able to plan well into the future when recommending additional deployment of resources. Property that might be perfect at a point in the future may not be available if we wait until we need it to begin property acquisition.
As an area grows and develops, other stakeholders will be looking for prime properties as well, many with the same issues of distribution and service that we consider when choosing a site. So we need to be able to answer the question of “when.” When do we purchase land? When do we begin to recruit personnel? When do we make the fleet purchases necessary? When do we begin construction and when do we need to begin operations?
The biggest responsibility we have as public servants and stewards of public trust and assets is to be able to explain “why” when recommending deployment strategies. We should have the knowledge and information to answer questions and defend recommendations. If we cannot answer “why,” then we are very unlikely to make good recommendations or get support for our requests.
Stephen would you make this a table
ISO Hydrant Count10.00%
Service Demand Factors
Commercial Sq. Footage5:00%
Distance to Fire Station5.00%
Over 4 Minute Calls10.00%
Percent Covered in 4 Minutes5.00%