2000115-Strengthening-Communities-with-Neighborhood-Data

378 Strengthening Communities with Neighborhood Data
similar values compared with its neighbors and, when summarized
across all areas, is proportionate to a global indicator of spatial association.
Popular LISA statistics are local Moran’s I and Gi/Gi*, which can
be used to map hotspots, coldspots, and/or spatial outliers (areas with
values that are inversely related to those of their neighbors, as explained
above) (Anselin, Sridharan, and Gholston 2007). Programs in which
these maps can be created include OpenGeoDa, R (R-Geo packages, such
as GeoXP), the Python-based Spatial Analysis Library PySAL (all free
and open source), and Esri’s spatial statistics toolbox.
As mentioned above, street segments have frequently been used as units
of analysis to which points are aggregated. Several recent methods extend
existing cluster techniques to networks, including kernel density mapping
(Okabe, Satoh, and Sugihara 2009); local Ripley’s K function (Okabe and
Yamada 2001); and LISAs (local Moran’s I and Gi/I*) (Yamada and Thill
2010). These improved methods allow analysts to generate cluster maps
at the street segment level that can be visualized through varying street
colors, widths, or heights. Existing software to apply some or all of these
methods include SANET (for ArcGIS), GeoDaNet, and PySAL 1.4. These
programs also allow users to compute street network distances between
origins and destinations; 9 an analyst could determine, for example, if services
are within reach of target area residents. GeoDaNet also generates
spatial weights based on network distances. Another program, the Urban
Network Analysis Toolbox for ArcGIS (recently released by the Massachusetts
Institute of Technology’s City Form Lab), enables the analysis of
point patterns using graph methods with street networks.
Several recent exploratory methods add a time dimension to the traditional
cross-sectional spatial cluster methods to detect local clusters across
space and time. A popular local test for detecting space–time hotspots of
event points is the space–time permutation scan by Kulldorff et al. (2005)
implemented in the SaTScan software (Kulldorff 2010). This method
detects events (such as crimes) that occurred in a similar location at a
comparable time and tests the significance of these space–time hotspots
by using a Monte Carlo permutation approach [for a comprehensive overview
of statistical analysis of spatial points patterns, see Diggle (2003)].
Rey (2001) integrated LISAs with classic Markov transition matrices
to test if joint transitions of an area and its neighbors remain significant
for a given set of time periods. One of the questions this method can
address is if area-based hotspots (e.g., of poverty, crime, or house prices)
persist over time. The LISA Markov is implemented in PySAL. 10