2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 383
and Vaidyanathan (2009) on shared measurement systems and RW Ventures’s
Dynamic Neighborhood Taxonomy (Weissbourd et al. 2009)]. An
example of such an (open-source) framework is Pahle’s (2012) so-called
complex systems framework, which combines analytic models with scenario
planning, collaboration, and decisionmaking in complex settings
in a cyber framework that can use advanced computational resources to
reduce model run times. The data needs of such frameworks are aided
by current initiatives to make more public data accessible and integrate
technology with public services (such as Government 2.0 and Code for
America efforts or Chicago’s big data initiative), as well as by innovative
initiatives that collect data through crowdsourcing (e.g., Ushahidi).
The goal of this essay has been to inspire the use of a broader spatial
perspective in the research and practice of place-based evaluation and
illustrate with a few examples how spatial concepts and methods can
add value in this context. After this avid endorsement of spatial analysis,
it is imperative to note the importance of contextualizing such analysis
within the broader questions and frameworks that evaluation research
and practice have been engaged in (Kubisch et al. 2010), similarly to how
quantitative impact measurement and evaluation for accountability have
recently been extended to include more qualitative evaluation for learning
approaches. This contextualization is especially relevant because the
focus on space can reveal and account for spatial patterns, but it often
does not explain the underlying mechanism (the spatial process) that
generated these patterns (this is especially true when techniques such
as spatial fixed effects or trend surfaces are used). In these cases, spatial
patterns point to other dynamics (such as social processes) that are driving
observable outcomes. Understanding the mechanisms of how and
why communities change and how they can be altered to improve community
residents’ lives is something that goes beyond spatial analysis,
but I hope to have persuaded readers of the value of an explicit spatial
perspective in contributing to this larger endeavor.
Notes
1. See N. Verbitsky, Associational and Causal Inference in Spatial Hierarchical Settings:
Theory and Applications. PhD dissertation, Department of Statistics, University of Michigan,
Ann Arbor, MI, 2007.
2. MDRC’s description of its evaluation goals of the Neighborhood Change Program
(NCP) illustrate this point: “For methodological reasons, the study will not permit
a formal assessment of the impacts of NCP across Chicago. Nonetheless, the research