By Shuyao Qi, Panagiotis Bouros, Nikos Mamoulis (auth.), Mario A. Nascimento, Timos Sellis, Reynold Cheng, Jörg Sander, Yu Zheng, Hans-Peter Kriegel, Matthias Renz, Christian Sengstock (eds.)
This e-book constitutes the refereed court cases of the thirteenth foreign Symposium on Spatial and Temporal Databases, SSTD 2013, held in Munich, Germany, in August 2013. The 24 revised complete papers provided have been rigorously reviewed and chosen from fifty eight submissions. The papers are geared up in topical sections on joins and algorithms; mining and discovery; indexing; trajectories and highway community info; nearest neighbours queries; uncertainty; and demonstrations.
Read Online or Download Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings PDF
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Additional info for Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings
The alternative hypotheses H1 (S) represent a higher or lower level of co-location inside S comparing to that outside S. We are interested in ﬁnding S of arbitrary shapes and not conﬁned to rectangular (including squared) shapes. Because we can handle stronger and weaker regional co-location similarly in the same framework, we will focus on discussing stronger co-location hereafter and the discussion of the opposite is straightforward. Fig. 1. Regional Co-locations of a Rectangle and an Arbitrary Shape: An Example Figure 1 illustrates a regional co-location of a rectangle on the left and a regional co-location of an arbitrary shape on the right.
Finding regional co-location patterns for sets of continuous variables in spatial datasets. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2008, pp. 30:1–30:10. ACM, New York (2008) 5. : Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006) 6. : Discovering colocation patterns from spatial data sets: A general approach. IEEE Trans. on Knowl. and Data Eng. 16(12), 1472–1485 (2004) 7.
Bayesian Spatial Scan Statistic. Spatial scan statistics have been studied extensively. The purpose of spatial scan statistics was to ﬁnd spatial clusters where certain quantity of interest occurs signiﬁcantly higher than expected. The state-of-art is based on Kulldorﬀ’s spatial scan statistic (). An extended discussion of spatial scan statistic was presented in . In , Kulldorﬀ deﬁned a general model for the multidimensional spatial scan statistic. There are three basic properties of the scan statistic: the geometry of the area being scanned, the underlying probability distribution generating the observed data under the null hypothesis and shapes of the scanning window.
Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings by Shuyao Qi, Panagiotis Bouros, Nikos Mamoulis (auth.), Mario A. Nascimento, Timos Sellis, Reynold Cheng, Jörg Sander, Yu Zheng, Hans-Peter Kriegel, Matthias Renz, Christian Sengstock (eds.)