Dear Colleagues,

Traditional spatial analyses grew up in an era of sparse data and very weak computational power. Today, both of those circumstances are reversed and many of the old solutions are no longer suitable. The title of this Special Issue, "Spatial Analysis and Data Mining", reflects this change and combines two things which, until recently, engaged quite different groups of researchers and practitioners. Together, they require particular techniques and a sophisticated understanding of the special problems associated with spatial data. This geographic data mining, or Geographic Knowledge Discovery (GKD), is not new, but is developing and changing rapidly as both more, and different, data becomes available, and people see new applications. The days of ‘Big Data’ require fresh thinking.

The aim of geographic data mining (GKD) is to assist in the generation of hypotheses, which can be tested, about interesting or anomalous spatial patterns which may be discovered in very large databases. It is important that the patterns discovered should not be statistical or sampling artifacts, and should be nontrivial and useful. The intent is not to build a system that makes decisions or interpretations automatically, but supports humans in these tasks. Also GKD is not synonymous with statistical analyses, such tools have a role in the testing of hypotheses generated by GKD but not in GKD itself.

We seek original and innovative papers which address this fusion of “Spatial Analysis and Data Mining” and present research which advances theory, demonstrates application and evaluates the approach taken.

Professor Brian Lees
Guest Editor

• geographic data mining
• geographic knowledge discovery
• spatio-temporal data mining
• spatial analysis
• knowledge discovery