2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 377
incorporate spatial effects (Anselin 1988). Event point data are frequently
applied in crime analysis and epidemiology (Waller and Gotway 2004).
The use of continuous spatial data primarily characterizes applications
in the natural sciences, such as geology, biology, or forestry, and is based
on geophysical data and geo-statistical methods (Bailey and Gatrell 1995;
Cressie 1991; Cressie and Wikle 2011).
Although the focus in this essay is on geographic space, spatial connectivity
can be conceptualized nongeographically (e.g., in terms of social
connectivity). Examples include Hanssen and Durland’s 5 application of
social network analysis to relate denser math teacher communication
networks to improved student test scores or the assessment by Radil et al.
(2010) of gang violence in geographic and social network space. Perceived
distances and neighborhood boundaries are another example of
non-Euclidean space (Coulton et al. 2001).
Spatial Methods and Tools
This section highlights some standard and new spatial analysis methods
that I believe are particularly relevant for the evaluation of place-based
initiatives in the context of this essay. Among the myriad of spatial methods
that exist, I selected a few that can generate interesting insights without
being too complicated to explain, at least conceptually, to stakeholders
without statistical backgrounds. 6 Selected tools to implement these methods
are also mentioned, with an emphasis on programs that are free and/
or open source, user friendly, under active development, and with larger
user bases. However, these selections are necessarily subjective and biased
toward some of the free and open-source software development efforts I
am affiliated with. 7 For a broader spatial analysis software overview, see
reviews by Fischer and Getis (2010) and Anselin (2012). 8
Cluster Detection Methods
The most common techniques for identifying spatial concentrations of
values beyond eyeballing map patterns are methods that detect statistically
significant clusters (called hotspots in some fields). These methods
can operationalize the concepts of spatial dependence (spatial autocorrelation)
discussed above. One of the popular groups of contiguitybased
cluster methods are so-called local indicators of spatial association
(LISAs) (Anselin 1995), which identify whether an area has statistically