Research Of The OEP-Based Spatiotemporal Data Model And Its Analytic Model

Spatiotemporal (ST) data model (STDM) is the core of spatiotemporal geographic information systems (GIS). It is not only a foundation for more effective storage and management of spatiotemporal data, but also for more advanced spatial and temporal analysis and exploration of geographical phenomena and their temporal variations. Currently, the STDMs are mainly used for effective storage and retrieval, but lack of considerations for advanced spatiotemporal analysis (ASTA) applications. This leads to a gap between data models and spatiotemporal analysis applications:i.e, only a few among the most developed models are suitable for analysis. Moreover, existing STDM lacks of ways to describe and represent its internal associations. Spatiotemporal analysis is the ultimate goal of spatiotemporal data modeling, and exploring temporal variations is just the most advanced practice.Therefore, modeling the STDM should integrate with the description and expression of this ST-varying response mechanism, which focus on the expression of temporal relations and the causal chains; and focus on consideration of the organic whole and internal individuals. In this thesis, considered the ST-varying characteristics of geographical objects and phenomena as a research objective, it presents a more basic STDM to describe the internal mechanism of ST-varying, uses this mechanism to build its semantic conceptual model and logical model. In this model, we widen its the capacity of high-level analysis, which enhance and expand the model application capabilities.Some works and contributions have been made as follows:?. This paper reviewed the research of temporal GIS and the mainstream STDMs, and also discussed the problems of these current models, and so presented the tasks of this study (three-category with seven task) and research framework.?. Traditionally, the ST-varying characteristics are based on the description of computer-based semantic information, but lacks of geographical semantics. It is difficult to achieve "true" semantic interoperability, and proposed the "intrinsic theory-variation theory-ontology theory" of the "three of theory" for the new ST-varying semantic framework. This framework combines information theory with cognitive theory:the intrinsic theory emphasizes the computer-based semantic representation for the spatial and temporal data; variation theory interprets the S-T variation for geographical semantic meaning; the ontology theory upgrade S-T variation into the semantic description of the reality of the world.?. The mainstream STDM lack of the description and expression of the internal mechanisms, we proposed and constructed an object-oriented S-T data model based on the'object-event-process':the OEP model. This model is different from other STDM. It pays more attention to describing and expressing the overall and internal individuals relations for the geographical dynamic phenomena, by virtue of the occurrence and development of the internal association. More importantly, given the focus of description and expression of three different parts of this model, it can be integrated or split into other STDM. After all, it is a general-purpose ST data model.IV. To expand the OEP model in S-T analysis, we applied it into S-T-varying characteristics for sea-ice, and constructed a logical model of sea-ice ontology, including semantic query. Meanwhile, we also developed the sea-ice ontology (as a part of this logical model) into the OEP-based factor model.V. To achieve interoperability between computer-based information semantics and geographic semantics, we explore the associations between "spatial relationships" and "geographical events" or "geographical process". The former is derived from the "intrinsic theory", while the latter is derived from "variation Theory". Concretely, the associations are characterized by implying the spatial geometry of the "geographic object" such as topology and orientation with the ST-varying geographical or geographical processes associated with the event. Consequently, we proposed the regional qualitative analysis of continuous ST-varying model:RAE model.VI. The relationships between geographic features in the computer-based information demonstrate spatial relationships between geo-objects. At this point, spatial relations have shown a'diversity', and it itself is 'multi-Types'. There is an temporal association between spatial associations, namely'multivariate'association pattern (MVAP). From the S-T features, mining MVAP is a powerful way to explore the variation laws. Here, we discussed the MVAP associated with the definition, construct and mining algorithms.