Abstract:
In some standard applications of spatial point pattern analysis, window selection
for spatial point pattern data is complex. Often, the point pattern window is given a
priori. Otherwise, the region is chosen using some objective means reflecting a view
that the window may be representative of a larger region. The typical approaches
used are the smallest rectangular bounding window and convex windows. The
chosen window should however cover the true domain of the point process since it
defines the domain for point pattern analysis and supports estimation and inference.
Choosing too large a window results in spurious estimation and inference in regions
of the window where points cannot occur. We propose a new algorithm for selecting
the point pattern domain based on spatial covariate information and without the
restriction of convexity, allowing for better estimation of the true domain. Amodified
kernel smoothed intensity estimate that uses the Euclidean shortest path distance
is proposed as validation of the algorithm. The proposed algorithm is applied in
the setting of rural villages in Tanzania. As a spatial covariate, remotely sensed
elevation data is used. The algorithm is able to detect and filter out high relief areas
and steep slopes; observed characteristics that make the occurrence of a household
in these regions improbable.
