Using NFORS data to understand coverage.
Written by Tyler Buffington & Tyler Garner — July 22, 2019
Fire Chiefs and other users often ask how the real-time operational data collected in NFORS can help inform decisions regarding the geographic placement of new resources. For example:
Can the NFORS data determine the ideal location for an additional fire station or apparatus placement?
These decisions are quite complicated and depend on a multitude of factors including land availability, incident prevalence, projected population growth and demographic trends, and road networks. Additionally, the impact of these decisions can be considerable.
In his book, “The Fires: How a Computer Formula, Big Ideas, and the Best of Intentions Burned Down New York City — and Determined the Future of Cities,” Joe Flood discusses perhaps the most well known example of misguided resource allocation decisions leading to severe consequences. He describes how the results from computer models led to the closure of key fire stations in New York, which contributed to an epidemic of severe fires in the 1970s. This example highlights the need for decision-makers to rely on their experience and knowledge of their own community in addition to data-driven models to inform resource allocation decisions. Putting the data models in the context of a particular community leads to clear information and enlightenment for decision making.
With the understanding of data in context, the large volume of operational data collected from NFORS can help departments better understand the coverage profile of their community and aid in this type of decision making. In this blog post, we describe several informative visualizations that can help decision-makers better understand their community’s specific needs, as well as how the department is currently serving those needs. Let’s start by reviewing historic travel times to see if certain locations are underperforming.
At the most basic level, we can create a scatter-map of historic incidents and the corresponding first unit travel time. An example is shown below, with the 0 on the X axis corresponding to the longitude of the furthest west incident and the 0 on the Y axis indicating the latitude of the furthest south incident. Each point represents an incident and its color is determined based on the travel time of the first unit. n= 5,410 incidents shown.
Although this visualization conveys a lot of information, it’s difficult to interpret, especially since it lacks geographic features which would provide an important contextual reference. It is also difficult to identify trends in regions with a large number of historic incidents since multiple incidents may be stacked in a single location. Perhaps a more informative visualization is an incident density heat map.
To generate this visualization, we employ a technique called Kernel Density Estimation. The technical details of using this methodology are outside the scope of this article, however the resulting visualization gives an indication of how many emergency calls are distributed throughout this department’s service area. Also shown is the department’s jurisdictional boundary and station locations.
This map is useful for visualizing the geographic demand profile. The areas that are shaded red indicate the area where a large number of incidents have occurred. Compare this visualization to the initial scatter map and although it is possible to see “clusters” of incidents, it is difficult to interpret the actual call density since many incidents were in close proximity.
However, it is important to note that the incident density visualization does not provide any information about travel times and therefore, the user does not have any insight into a critical performance component of these locations.
To help NFORS users understand locations that have historic travel time challenges, we can use a kernel smoother to calculate travel times to each location in the service area. In this visualization, each colorized location represents a weighted average. Incidents closer to one another on the map are assigned larger weights because they are assumed to be more “similar.” The results are somewhat intuitive, with incidents further away from a resource location typically having longer travel times.
It is important to note that we only assign colors in this plot to regions with more than 20 historical incidents within a ½ mile radius. We do this because there are not enough data to estimate average travel times in the regions with fewer incidents. Therefore, some regions within the department’s jurisdiction do not have any color assignment.
Now that we’ve analyzed high travel times and high call volume, how can we combine these visualizations to produce a single visualization that gives the user insights into the locations where additional resources may be needed?
To assess these areas, we can combine the previous visualizations to identify locations where both travel times are slow and call volumes are high as shown in the diagram below.
To accomplish this, we multiply the quantiles of the two previous heat maps together to generate a third visualization. In this map, regions that are red indicate both large call volumes and long travel times. It is important to note that this approach assumes an equal weighting of call volume and travel time. Depending on the situation, a decision maker may wish to place more weight on regions of high call volume over regions of slow travel times or vice versa.
In addition to our current analytics, NFORS is currently developing new visualization tools like this to help departments better understand their operational data. For example, using NFORS department leaders could generate the visualizations outlined in this blog post, filtered by incident type (i.e. fire vs. EMS) or by time of day. We are also experimenting with new visualizations of incidents in which the first arriving unit was not from the first due station so that fire department leaders can better understand unit reliability and the impact of high volume in particular areas of their jurisdiction.
As an NFORS user, we welcome your ideas for new visualizations and tools that could help you better use your data to tell your story. If you would like to learn more, or have us create these visualizations for your department. Reach out to our professional services team at hello@i-psdi.org.
