The development of information technology has enabled many different technologies to
collect large amounts of spatial data every day. It is of very great significance to discover …

Detecting regional co-location patterns on urban road networks is challenging because it is
computationally prohibitive to search all potential co-location patterns and their localities …

Spatial co-location pattern (SCP) mining discovers subsets of spatial feature types whose
objects frequently co-locate in a geographic space. Many existing methods treat the space …

The presence of global spatial autocorrelation usually leads to the spurious identification of
spatial hotspots and hinders the identification of local hotspots. Despite the use of statistical …

Neighborhood-based and density-based clustering methods are applied in various data
analysis applications. However, most of them have low performance in mixed …

Spatial data often exhibit a tendency highly similar to spatial objects located close to each
other. Thus, prevalent co-location pattern (PCP) mining has been studied extensively to …

Regional co-location pattern mining is a branch of spatial co-location pattern mining, which
is used to discover co-location patterns that prevalently co-occur in local regions. The …

In multivariate spatial interpolation, the accuracy of a variable of interest can be improved
using ancillary variables. Although geostatistical methods are widely used for multivariate …

The spatial co-location pattern is a collection of spatial features in which instances of
features prevalently appear in neighboring spatial regions. Due to the heterogeneity of …

The analysis of association rules within ecosystems is crucial for monitoring, managing, and
conserving natural resources. As widely adopted approaches for this task, geospatial …