Theories And Methods Of Spatio-temporal Outliers Detection

Spatio-temporal outliers (STOs for short) may contain some kind of potential and unknown knowledge about geographical phenomena. The detection of spatio-temporal outlier (STO for short) is very significant and necessary for better understanding spatio-temporal data, discovering the spatial relationships among spatio-temporal entities. Currently, many approaches have been proposed for the detection of STOs, and have been applied to many fields, such as weather, forest fires, geological disasters, environmental protection, public safety, and so on. This thesis focuses on the development of theories and methods about the detection of STOs, and all are summarized as follows:(1) After overview of existing research results about the detection of STOs, the characteristics of spatio-temporal data (STD for short) are summed up and the STD classification method is presented. And then, the characteristics and classification method of the STOs are studied. The framework of the STOs detection is explored, which includes the STOs candidates detecting step and the STOs evaluating step.(2) To solve the problems in the traditional statistical method for the detection of outliers, the method is expanded up to spatio-temporal domain and the nearest-neighbors and statistical-criteria based spatial outliers (SOs for short) detection (NNSCBSOD for short) is developed. The NNSCBSOD employs the k-times-standard-deviation rule in the each nearest neighbor to discriminate the outlying-ness of the spatial object.(3) For using cluster-analysis to detect STOs, since the existing spatial and spatio-temporal clustering methods only considers the spatial or spatio-temporal distance, while ignoring the thematic attributes, the dual-distance based spatial clustering methods (DDBSC for short) is proposed, which form all the adjacent and attributes similar spatial objects into a spatial cluster. Then, in the clustering results, all the isolating objects emerge and compose the SOs set. In view of the existing spatial clustering methods not adapting the uneven distribution of spatial data, an adaptive density-change based spatial cluster (ADBSC for short) method is proposed, too. The ADBSC and the concept-lattice approach are combined and utilized to detect SOs.(4) In order to use Clustering method to detect STOs, a concept-lattices based spatio-temporal Cluster (CLBSTC for short) is raised in this dissertation. The CLBSTC synthetically uses the ADBSC and concept-lattices approach to discovery the spatio-temporal clusters, and then, form all the spatio-temporal adjacent objects within the same concept lattice into a spatio-temporal cluster (STC for short). In the clustering results, all objects, not belonging to any STC, are STOs.(5) To employ the intelligent computing technology to detect STOs, this dissertation introduces the back propagation (BP for short) neural network into STO-detection procession, and STO detection neural network (STODNN for short) is described. Then, the constructions, learning samples design, learning rules about STODNN are discussed. After that, a STO measure is put forward via using the distance between the network output and the original data. Depending on the STO measure, the STOs are detected. Many experiments showed that the BP neural network output errors are smallest when using the input vector including the STC number and related attributes items, and STOs detection results are most stable.(6) Because of uncertainty in the STDs and the calculating process, it is required to evaluate the reliability STOs. In this dissertation, the reliability evaluation process is divided into two steps: STO filter and evaluation. Furthermore, the association-rules (ARs for short) based STO filtering methods (ARBF for short) is proposed. The ARBF prunes all the STOs candidates, which consistent with the ARs. In order to evaluate the reliability of the STOs, basing on the mining table for ARs, the outlying support and the outlying confidence are defined to measure the reliability of the STOs. In the end, Voronoi-diagram based spatial ARs mining method (VDSAR for short) and event-coverage based spatio-temporal ARs mining method (ECSTAR for short) are proposed so as to discovery space and spatio-temporal ARs.Finally, after concluding all the research work in this dissertation, some deficiencies and the following work are discussed, which include (1) employing fuzzy sets, decision trees, etc. to further study evaluation the uncertainty methods for the STOs, and (2) integrating the 3-dimensional visualization and chart technologies to visualize the STOs.