2000115-Strengthening-Communities-with-Neighborhood-Data

140 Strengthening Communities with Neighborhood Data
in light of what has been learned and priorities that have been identified.
In other words, what courses of action might we follow to respond
to threats and opportunities in light of a better understanding of our
strengths and weaknesses?
This process virtually always entails some type of mental testing of
alternative scenarios, and even the best of these work by trial and error.
The process may start with clarifying the theory of change or logic model
(the social systems model in Land’s diagram)—that is, clarifying and
revising ideas about how the relevant patterns of cause and effect actually
work. After this clarification, alternative sets of promising interventions
are formulated and, consistent with the theory, expectations about
the advantages and disadvantages of each alternative are made explicit.
Thinking through the implications may lead to recognizing new problems
and opportunities, which may then suggest directions for adjustments
to the first plan (a new scenario).
In local policy and community work, the pressure from funders and
leaders in the field for more data-driven decisionmaking in this process
(weighing the options and deciding what actions to take) is now
substantial. This pressure implies increasing efforts to take advantage of
community information to quantify expected implications of alternative
scenarios. Instead of saying in general that one option is likely to be
less risky, less expensive, or more effective than another, the team tries
to use model relationships to project trends under varying assumptions
and to estimate costs. Given the complexity of local socioeconomic systems,
no one has yet come close to developing measures for a system as a
whole, but it is now reasonable to try to measure much more than in the
past. These efforts will be wise to employ the framework of cost–benefit
analysis [see, for example, Boardman et al. (2001)], even if all the desired
parameters cannot be estimated reliably.
The practice of formulating and testing alternative scenarios has been
a recommended part of strategic planning for half a century. 2 The practice
has evolved slowly because the work is complex and there has been
an unfulfilled expectation that the art of predicting the future would
improve enough so institutions would not have to think through very
many alternatives. Now there seems to be more acceptance that we live
in an uncertain world. The future of complex systems (like cities and
neighborhoods) will remain very difficult to predict reliably. However,
computer-based tools are being developed to simplify the task of scenario
testing, so more work of this type seems probable (Avin 2012). The