Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Disaster management is a crucial and urgent research issue. Emergency communication
networks (ECNs) provide fundamental functions for disaster management, because …

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …

Snakes, or active contours, are used extensively in computer vision and image processing
applications, particularly to locate object boundaries. Problems associated with initialization …

It is usually difficult for prevalent negative co-location patterns to be mined and calculated.
This paper proposes a join-based prevalent negative co-location mining algorithm, which …

This paper operationalizes the idea of a local indicator of spatial association for the situation
where the variables of interest are binary. This yields a conditional version of a local join …

Summary Spatial Data Mining is the process of discovering interesting and previously
unknown, but potentially useful patterns from large spatial datasets. Extracting interesting …

A visual word lexicon can be constructed by clustering primitive visual features, and a visual
object can be described by a set of visual words. Such a" bag-of-words" representation has …

Co-location pattern mining refers to the task of discovering the group of features (geographic
object types) whose instances (geographic objects) are frequently located close together in …

Spatial co-location pattern mining is an interesting and important task in spatial data mining
which discovers the subsets of spatial features frequently observed together in nearby …