Practitioner’s Model of Community Resilience: Bases
The arithmophobic may experience palpitations, panic attacks, or even pass out.
I will try to be as gentle as I can!
As I threatened promised in my last post, in the next few posts I’m going to develop and discuss the model of community resilience that I’m using in my work. In this post, I’m going to present the bases for the model. These include not only the assumptions behind the model but also the reasons for wanting a model. As you’ll see, these are interwoven.
What drove me to develop a model of community resilience? Quite simply, I wanted – actually needed – some means to systematically look at how (and how fast and how far) communities come back from severe disruptions. I wanted a tool that was fairly intuitive, aligned with experience and that was useful for all parts of a community and any type of disruption (i.e., practical). More recently, I’ve begun to use the model to better understand the impacts of chronic stresses.
One of the biggest problems those of us “doing” community resilience face is that each part of a community has its own language / jargon, particularly when it comes to “resilience.” If you’re looking at a community as a system of systems, these systems frequently “are separated by a common language.” It often seems to the layman that specialists in health care, for example, are speaking in tongues! As a layman in almost everything, I needed a tool that I could use to translate the electrical dialect, the financial dialect, the flood plain manager dialect and all of the others into a common language that I could understand.
I early on recognized that an important gap in understanding community resilience was that too often “community” was left undefined (I’ve written about this previously.). Thus a definition of “community” was needed. I like this one.
A community is a collection of individuals and organizations bound together by geography and perceived self-interest to efficiently carry out essential common functions.
The “geography” part is almost trivial, but the rest of the definition packs a lot of meaning. “Perceived self-interest” implies that communities are open systems – people join or leave based on their perceptions of how well the community is working for them. Thus, while it is common to consider “White Flight” from central cities to be a product of racism, the migration of educated blacks to the suburbs requires a different rationale. This definition implies that they found that essential common functions (e.g., law enforcement) were not being provided at an acceptable level.
“Common functions” implies the idea of the Whole Community as being important for its resilience. In other words, each of the systems carrying out an essential common function will have its own resilience. A community’s economy, its water system, its social services and all of the other community systems will have their own distinctive responses to and recoveries from disruption – their own resilience strategies. The distinctive strategy will depend on the type and magnitude of disruption, the structure of both the system and the rest of the community (the community’s interconnectedness) and the resources the system can call on.
The resilience strategies of infrastructural systems (e.g., water, electricity, transportation) most often will focus on assets. The resilience strategies of social service systems will focus more on service and people. The community’s resilience will encompass the resilience of each of these community systems. As a practical matter, we know that these systems interact and often are interdependent. Thus, a community’s water system requires electricity; and generation of electricity requires water. Health care requires both.
Experience shows that there is a characteristic loss-recovery curve (below) for each community system impacted directly by a shock or disruption. It is important to note that this curve is seen for every type of disruption – social unrest, economic shocks such as the Great Recession, health crises, and disasters that inflict physical damage on community systems.
As the graphic indicates, there are four parts that make up the curve:
- The trajectory of the system before the event. As I’ve written before, there is a sort of “Law of Conservation of Community Momentum” that indicates that communities will tend to stay on the same trajectory after a disruption as they were on before unless they take action.
- The pre-shock capacity (level of service or functionality) of the system. If there are parts of the community that are underserved by the system (e.g., the community’s economy) then it is unlikely that those parts will be better served after the shock.
- The loss due to the shock. This will depend on the type of shock and the system’s ability to resist compared to the shock’s magnitude. Thus, new construction in Moore, OK – now mandated to be able to resist up to 135 mph winds – should not be significantly damaged by 90+% of tornadoes while existing structures may be completely destroyed. Similarly, a community’s electrical system may be out of service for days or weeks after a hurricane but may suffer few (if any effects) from a pandemic.
- The system’s recovery from the shock. Communities and the systems that they encompass will respond and recover differently depending on the type of shock. In particular, the path to recovery of Gulf Coast communities after Hurricane Katrina was quite different than after the BP oil spill. The time frame for recovery of each system is also quite likely to vary. For example, the recovery of a health care system – depending on the recovery of community water, electricity and perhaps transportation systems – is likely to lag behind these other systems.
The loss-recovery curve thus can be encapsulated in an equation:
Capacity* of the system after a shock =
(Pre-shock capacity + Trajectory* + Loss* + Recovery*)
Please note that to this point there is really no math wizardry involved; this is simply a mathematical identity equation. The loss-recovery curve is broken down into its constituent parts. This will have only some claim on originality once we begin to dig deeper into each of these parts. In the following posts, I’m going to do just that – channeling my inner Scott Miles to flesh out the model and actually do a (very) little math. As I go along, I’ll consider the nature and impacts of stresses. I will also use the model to consider potential resilience strategies.
* Each of these are time-dependent.