III: Small: A Theory of Topological Relations for Compound Spatial Objects

Spatial data collections with an incomplete coverage yield regions with holes and separations that often cannot be filled by interpolation. Geosensor networks typically generate such configurations, and with the proliferation of sensor colonies, there is now an urgent need to provide users with better information technologies of cognitively plausible methods to search for or compare available spatial data sets that may be incomplete. The objective of the investigations is to advance knowledge about qualitative spatial relations for spatial regions with holes and/or separations. The core activity is the study of the interplay between topological spatial relations with holed regions and topological spatial relations with separated regions to address the potentially complex configurations that feature both holes and separations. Three characteristics of such a set of topological relations are addressed: the formalization of a sound set of relations at a granularity that allows for the distinction of the salient features of holed and separated regions, while offering the opportunity to generalize to coarser relations in a meaningful and consistent way; the relaxation of such relations so that the determination of the most similar relations follows immediately from the applied methodology; and the qualitative inference of new information from the composition of such relations to identify inconsistencies and to drawn information that is not immediately available from individual relations. The hypothesis is that combining the relation formalization with sound similarity and composition reasoning yields critical insights for a sufficiently expressive, common approach to modeling topological relations for holed regions and regions with separations. The resulting theory of topological spatial relations highlights a parallelism between relations with holed regions and regions with separations, which is most apparent when these regions are embedded on the surface of the sphere, while some parts of these regularities are often hidden in the usual planar embedding. Since topological relations are qualitative spatial descriptions, they come close to people\u27s own reasoning, so that a better understanding of the relations for compound spatial objects will have ramifications for qualitative spatial reasoning, without a need for drawing graphical depictions to make inferences. It also lays the foundation for linguistic constructs to communicate in natural language spatial configurations, ultimately leading to talking maps. An immediate impact of this theory of topological relations between holed and separated regions is on the querying and reasoning about dataset that are gathered by geosensor networks. Additional information available online: http://www.spatial.maine.edu/~max/holesAndParts.htm

Algebra of core concept transformations: Procedural meta-data for geographic information

Transformations are essential for dealing with geographic information. They are involved not only in converting between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relations, and a set of basic transformations described in terms of function signatures that use such types. Type inference allows us to enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions. This allows us to automatically infer goal concepts across workflows and to query over such concepts across raster and vector implementations. We evaluate the algebra over a set of expert GIS workflows taken from online tutorials

Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

Improving the accuracy and the efficiency of geo-processing through a combinative geo-computation approach

Geographical Information Systems (GIS) have become widely used for applications ranging from web mapping services to environmental modelling, as they provide a rich set of functions to solve different types of spatial problems. In the meantime, implementing GIS functions in an accurate and efficient manner has received attention, throughout the development of GIS technologies. This thesis describes the development and implementation of a novel geo-processing approach, namely Combinative Geoprocessing (CG), which is used to address data processing problems in GIS. The main purpose of the CG approach is to improve the data quality and efficiency of processing complex geo-processing models. Inspired by the concept of Map Calculus (Haklay, 2004), in the CG approach GIS layers are stored as functions and new layers are created through a combination of existing functions. The functional programming environment (Scheme programming language) is used in this research to implement the function-based layers in the CG approach. Furthermore, a set of computation rules is introduced in the new approach to enhance the performance of the function-based layers, such as the CG computation priority, which provides a way to improve the overall computation time of geo-processing. Three case studies, which involve different sizes of spatial data and different types of functions are investigated in this research in order to develop and implement the CG approach. The first case study compares Map Algebra and our approach for manipulating two different raster layers. The second case study focuses on the investigation of a combinative function through the implementation of the IDW and Slope functions. The final case is a study of computational efficiency using a complex chain processing model. Through designing the conceptual model of the CG approach and implementing the CG approach in the number of case studies, it was shown that the new approach provides many advantages for improving the data quality of geo-processing. Furthermore, the overall computation time of geo-processing could be reduced by using the CG approach as it provides a way to use computer resources efficiently and avoid redundant computations. Last but not least, this thesis identifies a new research direction for GIS computations and GIS software development, such as how a robust geo-processing tool with higher performance (i.e. data quality and efficiency) could be created using the CG approach