The SHAPE team has worked with a fire and rescue service to help them understand their local population, supporting them to model options to optimise their resources, enhance response, strengthen resilience and reduce community risk.
The fire and rescue service we partnered with covers a diverse population of almost one million people, spread across cities, major towns and rural areas, with the population expected to rise unequally across the region over the next few years.
The organisation wanted to assess their current resources in relation to the intervention standards for emergency response times and mobilisations for major incident coverage and COMAH sites, along with producing options to optimise their stations and resources to provide increased coverage of areas of the community particularly at risk.
To do this, they needed a deep understanding of the population makeup and community risk, and how it differed across the region.
The SHAPE team mapped historic response data across the region to visualise trends in responses by type of call out, the season and time of day.
The first step in the process was data collection. We collated historical data on incident responses from the past five years, community risk plans and population and demographic data from SHAPE to provide a broad picture of the existing community risk, and an idea of future risk based on population growth or decline.
We then processed and mapped this data, revealing hot spots of activity and visualising trends in responses which could be analysed against community risk. Our mapping was also able to set out demographic and population data in regard to potential recruitment of on-call firefighters living within a certain radius of relevant fire stations.
We then visualised all the data, breaking it down into sub-locations and overlaying resource locations and travel times, therefore providing a detailed overview of the percentage of the population currently served, and analysis of incident trends by year, season, time of day and type of incident. Analysis of the historical incident data revealed patterns in the data, including seasonal variations and emerging risks.
Travel time analysis had to be predicated on blue light travel times, which meant adapting traditional travel time tools to take into account the quicker response times of emergency vehicles.
Once the data was processed, the project could move on to scenario planning – running the incident response data against distinct scenarios. This forecasted if changes to the population or community risk would enable the service to maintain coverage of all high-risk areas.
Scenario planning was broken down into several geographic areas, enabling variations in projected population growth for individual areas to be considered, as well as variances within demographics of different communities.
The final report gave analysis and recommendations on the various scenarios. Some scenarios were able to offer enhanced cover due to optimisation of the service in areas expected to increase in population, whereas other plans could be ruled out as they would not maintain sufficient coverage to match population growth and future predicted community risk.
If you’re interested in working with us to understand the impact of your blue light scenario planning, get in touch.
