Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes

Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes

Gabriella Schoier (University of Trieste, Italy) and Giuseppe Borruso (University of Trieste, Italy)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/jdwm.2012070103
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Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors’ analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of ’dense’ spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. Different tests are performed over the urban area of Trieste (Italy), considering both multiple users and different origin/destination journeys.
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Spatial Data Mining

In recent years geographic data collection devices linked to location-aware technologies such as the global positioning system allow researchers to collect huge amounts of data. Other devices such as cell phones, in-vehicle navigation systems and wireless Internet clients can capture data on individual movement patterns. This explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge.

The process of extracting information and knowledge from these massive geo-referenced databases is known as Geographic Knowledge Discovery (GKD) or Spatial Data Mining. It may be useful to understand spatial data, to discover relationships between spatial and non spatial data, to build knowledge-bases. This has a wide application in Geographic Information Systems (GIS), image analysis and other different areas where spatial data are used.

The nature of geographic entities, their complexity, relationships, and data means that standard Knowledge Discovery in Databases (KDD) or Data Mining techniques are not sufficient (Koperski, 1998) or at least their usefulness is limited. In fact the data inputs of Spatial Data Mining include extended objects such as points, lines, and polygons.

Specific reasons are the nature of geographic space, the complexity of spatial objects and relationships as well as their transformations over time, the heterogeneous and sometimes ill-structured nature of geo-referenced data, and the nature of geographic knowledge.

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