Preprint #97-3
The analysis of a spatial point pattern is often involved with looking for
structure, such as clustering or regularity. This can be done
through (kernel density)
estimates of the K-function or its derivative, the product density function. In this
article, we define a local version of the product density function for each event
derived under Anselin's (1995) definition of a local indicator of
spatial association (LISA). These product density LISA functions are
then grouped
by a standard
hierarchical clustering algorithm into bundles of functions
with
similar behavior.
Events corresponding to LISA functions within the same bundle are similar with
respect to their distance to other nearby events. This grouping of events is very
different from the usual clustering notion in spatial point patterns. Our research
provides a new quantification of structure in the analysis of
spatial point
patterns.
Copies of preprints are available from the author upon request. Use
the preprint number (located at the top of the page) and make
the request directly to the author, Iowa State University,
Department of Statistics, Snedecor Hall, Ames, IA 50011-1210.