This work introduces a graph-based approach to the representation of evolving entities in space and time. At an abstract level, the model makes a distinction between filiation and spatial relationships between entities, while at the database level, it stores derivation relationships and determines continuation and spatial relationships in time. An extended relational database specification implements the spatio-temporal graph model. A formal language models integrity constraints that are inherent to the model and those that are semantic and application dependent. The satisfiability of these constraints is studied and an algorithm for checking consistency of spatio-temporal graph is provided. An experimental evaluation shows the potential of the model.

Modeling consistency of spatio-temporal graphs

Computing increasingly happens somewhere, with that geographic location important to the computational process itself. Many new and evolving spatial technologies, such as geosensor networks and smartphones, embody this trend. Conventional approaches to spatial computing are centralized, and do not account for the inherently decentralized nature of "computing somewhere": the limited, local knowledge of individual system components, and the interaction between those components at different locations. On the other hand, despite being an established topic in distributed systems, decentralized computing is not concerned with geographical constraints to the generation and movement of information. In this context, of (centralized) spatial computing and decentralized (non-spatial) computing, the key question becomes: "What makes decentralized spatial computing special?" In Part I of the book the author covers the foundational concepts, structures, and design techniques for decentralized computing with spatial and spatiotemporal information. In Part II he applies those concepts and techniques to the development of algorithms for decentralized spatial computing, stepping through a suite of increasingly sophisticated algorithms: from algorithms with minimal spatial information about their neighborhoods; to algorithms with access to more detailed spatial information, such as direction, distance, or coordinate location; to truly spatiotemporal algorithms that monitor environments that are dynamic, even using networks that are mobile or volatile. Finally, in Part III the author shows how decentralized spatial and spatiotemporal algorithms designed using the techniques explored in Part II can be simulated and tested. In particular, he investigates empirically the important properties of a decentralized spatial algorithm: its computational efficiency and its robustness to unavoidable uncertainty. Part III concludes with a survey of the opportunities for connecting decentralized spatial computing to ongoing research and emerging hot topics in related fields, such as biologically inspired computing, geovisualization, and stream computing. The book is written for students and researchers of computer science and geographic information science. Throughout the book the author's style is characterized by a focus on the broader message, explaining the process of decentralized spatial algorithm design rather than the technical details. Each chapter ends with review questions designed to test the reader's understanding of the material and to point to further work or research. The book includes short appendices on discrete mathematics and SQL. Simulation models written in NetLogo and associated source code for all the algorithms presented in the book can be found on the author's accompanying website.

Decentralized Spatial Computing: Foundations of Geosensor Networks

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. 
We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. 
Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systems

pdf/geospatial-narratives-and-their-spatio-temporal-dynamics-4s7cxe22ze.pdf

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

Topology is the most commonly used spatial construct to bridge the gap between formal spatial information theory and systems on the one side and (human) spatial cognition and thinking on the other. To this end, we find topological calculi in virtually all research areas pertinent to spatial information science such as ontological modeling, geographic information retrieval, or image analysis and classification. Manifold behavioral experiments have been conducted to assess the cognitive adequacy of topological calculi with varying results. Our contribution here is unique for two reasons: first, we are addressing, behaviorally, the role of topology in the crucial area of spatio-temporal information; second, we are evaluating the role of topology across different semantic domains. We report five experiments that were conducted in the framework we developed (Klippel and Li 2009), which combines critical constructs from spatial information theory and cognitive science. Topologically equivalent movement patterns...

Spatial Information Theory Meets Spatial Thinking: Is Topology the Rosetta Stone of Spatio-temporal Cognition?

The modeling, analysis and visualization of dynamic geospatial phenomenahas been identified as a key developmental challenge for next-generation GeographicInformation Systems (GIS). In this context, the envisaged paradigmatic extensions tocontemporary foundational GIS technology raises fundamental questions concerning theontological, formal representational and (analytical) computational methods that wouldunderlie their spatial information theoretic underpinnings. We present the conceptualoverview and architecture for the development of high-level semantic and qualitativeanalytical capabilities for dynamic geospatial domains. Building on formal methods in theareas of commonsense reasoning, qualitative reasoning, spatial and temporal representationand reasoning, reasoning about actions and change and computational models of narrative,we identify concrete theoretical and practical challenges that accrue in the context offormal reasoning about space, events, actions and change. With this as a basis and withinthe backdrop of an illustrated scenario involving the spatio-temporal dynamics of urbannarratives, we address specific problems and solution techniques chiefly involving qualitativeabstraction, data integration and spatial consistency and practical geospatial abduction.

Geospatial Narratives and Their Spatio-Temporal Dynamics: Commonsense Reasoning for High-Level Analyses in Geographic Information Systems

Representation and reasoning about dynamic spatial phenomena requires at its foundation a formalism of spatial change. This paper extends our understanding of topological change, by providing a classification and analysis of events associated with changes in topological structures of spatial areal objects as they evolve through time. Tree structures are employed to represent topological relationships between regions and holes of areal objects. Basic and complex changes are specified using structure-preserving mappings between trees. Furthermore, the paper constructs a normal form, and proves that it is the 'simplest' form that can represent all the changes under consideration.

Event-based topology for dynamic planar areal objects

Wireless sensor networks (WSNs) can provide real-time information about geospatial environments, and so have the potential to play an important role in the monitoring of geographic phenomena. The research reported in this paper uses WSNs to provide salient information about spatially distributed dynamic fields, such as regional variations in temperature or concentration of a toxic gas. The focus is on topological changes to areas of high-activity that occur during the evolution of the field. Topological changes investigated include region merging and splitting, and hole formation or elimination. Such changes are formally characterized, and an algorithm is developed that detects such changes by means purely of in-network processing. The efficiency of this algorithm is investigated both theoretically and using simulation experiments.

/pdf/detecting-basic-topological-changes-in-sensor-networks-by-1gzkoahhy4.pdf

Detecting basic topological changes in sensor networks by local aggregation

The research reported in this paper uses wireless sensor networks to provide salient information about spatially distributed dynamic fields, such as regional variations in temperature or concentration of a toxic gas. The focus is on deriving qualitative descriptions of salient changes to areas of high-activity that occur during the temporal evolution of the field. The changes reported include region merging or splitting, and hole formation or elimination. Such changes are formally characterized, and a distributed qualitative change reporting (QCR) approach is developed that detects the qualitative changes simply based on the connectivity between the sensor nodes without location information. The efficiency of the QCR approach is investigated using simulation experiments. The results show that the communication cost of the QCR approach in monitoring large-scale phenomena is an order of magnitude lower than that using the standard boundary-based data collection approach, where each node is assumed to have its location information.

/pdf/qualitative-change-detection-using-sensor-networks-based-on-4o36iplnhg.pdf

Qualitative change detection using sensor networks based on connectivity information

Topological changes to regions, such as merging/splitting and hole formation/elimination, are significant events in their evolution. Information about such salient changes is useful in many applications. The research reported in this paper provides theoretical foundations for such topological change detection in sensor networks. A local tree model is proposed in the spatial domain, based on which a set of types of topological changes is specified. We also present a sensor network framework which captures the necessary information required by the tree model. Both the local tree model and the sensor network framework form the foundations for detection approaches that allow sensor networks to report topological changes.

Preliminaries for Topological Change Detection Using Sensor Networks

Topological changes to regions, such as merging, splitting, hole formation and elimination, are significant events in the evolution of regions. Wireless sensor network technology, which provides real-time information about the environment, can play an important role in detecting and reporting such topological changes. 
This thesis provides theoretical foundations and algorithmic solutions to topological change detection using sensor networks. Two models, the morphism-based model and the local tree model, are developed, providing formal semantics of topological changes. The morphism-based model represents dynamic topological properties of continuously evolving areal objects, in which basic and complex topological changes are represented and classified using trees and structure-preserving mappings between them. Based on this model, this work constructs a normal form and proves that it is the simplest form that could represent all the changes under consideration. The local tree model represents discrete and incremental changes of the areal objects based on selected components and relations between them. It allows us to specify different kinds of topological changes using information within the locality of the change. Based on the local tree model, we develop two decentralized and energy-efficient approaches, the transient group-based (TG-based) and the adaptive group-based (AG-based) approaches, to topological change detection using sensor networks. The TG-based approach employs the boundary group framework, which reduces the communication cost by reporting only the group level data instead of data from each individual node. The AG-based approach further reduces the communication cost by reusing the time-invariant information. 
Experimental results show that when the configurations of sensor networks satisfy certain density and communication constraints, the proposed approaches are able to generate correct reports on the topological changes, and at the same time reduce the communication cost to a level much lower than that of a basic boundary-based data collection approach.

/pdf/specifying-and-detecting-topological-changes-to-an-areal-57iogh31zy.pdf

Specifying and detecting topological changes to an areal object

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