Practitioners’ Model of Community Resilience: Implications and Applications

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…clad his philosophy in mail and mask by means of mathematical hocus pocus. – Nietzsche

Functionality = Initial Functionality + Direct Impacts + Indirect Impacts + Competence • Resources,
for each part of the community.

John Plodinec of CARRI

John Plodinec of CARRI

In previous posts in this series (Part One and Part Two), I’ve laid out the bases for a model of community resilience and then fleshed out the model (with appropriate mathematical hocus pocus, I hope!).  In this post, I want to look at some of the implications of the model and how I’ve used it in my resilience work.

All of us are bombarded with data on a daily basis.  Making sense of it all can be an almost impossible task.  The model is my tool for not only organizing the data flowing in but also for drawing inferences and conclusions.  Although I’ll save us all from going into the differentials here, each of the following comes directly from the model’s mathematical formulation.

  • Although I’ve developed the model in terms of a shock to one or more parts of a community, it remains useful to me even in the absence of a shock (i.e., no direct or indirect impacts). In this case, functionality changes by investment (competent application of resources) or its lack, i.e., a community’s trajectory prior to a shock is determined by how much it invests in itself.  For those of you who’ve read my screed before, this is one of the reasons I’ve harped on resilience requiring investment.  In my experience, one of the indirect impacts of a positive trajectory is an increase in confidence that will be felt in many other parts of the community; that increase in confidence leads to an increase in competence.
  • Each part of the community has its own resilience to a given shock; some will recover faster, some slower, and some may never reach pre-shock levels.  Compare NOLA’s Garden District, the Broadmoor neighborhood, and the Lower Ninth Ward.
  • Further, the resilience of any part of the community may well be different depending on the type of shock.  An economic shock is different from a natural disaster which is different still from a health crisis. The direct impacts are different leading to different indirect impacts.  This is inherent in Butler and Sayre’s work on the Gulf Coast looking at recovery from Katrina and from the BP oil spill.

    New Orleans, LA–Aerial views of damage caused from Hurricane Katrina the day after the hurricane hit August 30, 2005.
    Photo by Jocelyn Augustino/FEMA

  • An important implication of the model is that it “allows” a community’s New Normal to be more functional than its Old Normal (compared to the Bruneau’s 1 – q(t) formulation).  The model implies this improvement happens through the competent application of community capital.
  • While I have talked about “shocks” in terms of loss, they may actually be an abrupt improvement in one part of the community (e.g., see Not all disasters are the color of doom) that results in negative impacts on other parts (i.e., no direct impact, but significant indirect impacts).  Or the “shock” may not strike the community directly at all.  Think of the Great Blackout – setting a protective relay in Ontario led to a loss of power for much of the northeastern US (which in turn led to a spike in births nine months later – talk about your cascading consequences and interdependencies!).
  • While I’ve discussed the model in terms of shocks, mathematically it also works for those chronic stresses that are slow processes.  Insufficient maintenance of roads and bridges is an example.
  • As I’ve indicated before, “resources” includes all forms of community capital.  This helps to explain Rick Weil’s observations about well-connected lower income groups recovering almost as rapidly as those with higher incomes.  As a corollary, competence above all implies connectedness – connections to capital and to people.  Thus, the (competence x recovery) product implies a learning organization that either has experience or has planned for the shock.
  • In the first of this series, I threatened promised to deal with stresses.  I don’t want to go into too much detail but – based on the model – I’ve developed a sort of taxonomy of community stresses.
    • As I indicated above, some stresses can be considered slow processes that can be dealt with as if they were shocks.  They will have both direct and indirect impacts, although the indirect impacts will be incrementally small though eventually significant.  As an example, poor road maintenance eventually degrades transportation performance.  These are often manifested as a negative trending trajectory.
    • Some stresses can be considered as resource constraints.  Poverty is a good example.  A small community that does not have personnel trained in grant-writing will not be able to access available resources, indicating a lack of human capital.  This often will be seen as a low initial capacity or functionality.
    • Others can be considered as constraints on competence.  A community with great internal disconnects and conflicts – for example, Ferguson, MO – may have all the resources needed for recovery from a shock, but may not be able to use them effectively.
    • The model also leads to my somewhat unconventional view of metrics.  Perhaps the most important aspect of the model is its explicit recognition of the time element.  As a result, I am more interested in a community’s trajectory and its structure than in a snapshot of attributes.  I find that the community’s trajectory and some idea of who is connected to whom provides more practical indicators of the ability to recover than single instant statistical data.  Further, many of the commonly used “resilience metrics” change so slowly that they have little value as measures of progress. Thus, in CARRI’s Community Resilience System (and its Campus Resilience Enhancement System) we try to understand the structure of the community (or campus) and to judge its competence to deal with the shocks it may face.

I don’t pretend that this model is right or even that it’s unique, only that it’s useful (at least to me) for drinking from the fire hose of information which inundates us all.  Thus, it has played an important role in my work to help communities become more resilient.

Republished with permission of: Dr. John Plodinec of CARRI

About the Author

John Plodinec Community Resiliency ExpertJohn Plodinec, Ph.D is the Associate Director for the Community and Regional Resilience Institute (CARRI) at Meridian Institute.

In this role, he is responsible for identifying and evaluating technologies useful for enhancing community resilience.

CARRIHe also is playing a leading role in development of CARRI’s Community Resilience System. He has also been heavily involved with CARRI’s engagement with the Charleston, SC, region. John recently retired from the US Department of Energy’s Savannah River National Laboratory (SRNL), as its Science Advisor. In this position, he led SRNL’s Laboratory-Directed Research and Development program, as well as developing strategic partnerships in areas aligned with the laboratory’s primary thrust areas.

Read additional insights by Dr. Plodinec.


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