Clustering constitutes one of the most popular and important tasks in data analysis. This is true for any type of data, and geographic data is no exception. In fact, in geographic knowledge discovery the aim is, more often than not, to explore and let spatial patterns surface rather than develop predictive models. The size and dimensionality of the existing and future databases stress the need for efficient and robust clustering algorithms. This need has been successfully addressed in the context of general-purpose knowledge discovery. Geographic knowledge discovery, nonetheless can still benefit from better tools, especially if these tools are able to integrate geographic information and aspatial variables in order to assist the geographic analyst's objectives and needs. Typically, the objectives are related with finding spatial patterns based on the interaction between location and aspatial variables. When performing cluster-based analysis of geographic data, user interaction is essential to understand and explore the emerging patterns, and the lack of appropriate tools for this task hinders a lot of otherwise very good work.In this paper, we present the GeoSOM suite as a tool designed to bridge the gap between clustering and the typical geographic information science objectives and needs. The GeoSOM suite implements the GeoSOM algorithm, which changes the traditional Self-Organizing Map algorithm to explicitly take into account geographic information. We present a case study, based on census data from Lisbon, exploring the GeoSOM suite features and exemplifying its use in the context of exploratory data analysis.
|Number of pages||15|
|Journal||Computers, Environment and Urban Systems|
|Publication status||Published - 1 May 2012|
- Geographic knowledge discovery
- Self-Organizing Maps
- Spatial clustering