The study of spatial entities needs a model that is not only fully observable and controllable, but also computable. Euclidean topology on R-2 is a usually used tool for this study, but it has the following two weakne...
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The study of spatial entities needs a model that is not only fully observable and controllable, but also computable. Euclidean topology on R-2 is a usually used tool for this study, but it has the following two weaknesses. First, there exists some phenomenon of human perception of the spatial entity that cannot be simulated by it. Second, its observation of the basic geometric properties (interior, exterior, boundary) of the spatial entity lacks computability so that the model based on it lacks computability and cannot be directly used to practical systems. Consequent, in this paper, we present another tool for studying spatial entities - raster quasi-topology on R-2 and then compare the two tools.
We demonstrate that a resolution-r PR quadtree containing n points has, in the worst case, at most 8n(r - [log4(n/2)]) + 8n/3 - 1/3 nodes. This captures the fact that as n tends towards 4r, the number of nodes in a PR...
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We demonstrate that a resolution-r PR quadtree containing n points has, in the worst case, at most 8n(r - [log4(n/2)]) + 8n/3 - 1/3 nodes. This captures the fact that as n tends towards 4r, the number of nodes in a PR quadtree quickly approaches O(n). This is a more precise estimation of the worst case space requirement of a PR quadtree then has been attempted before.
Peer-to-peer (P2P) networks have become a powerful means for online data exchange. Currently, users are primarily utilizing these networks to perform exact-match queries and retrieve complete files. However, future mo...
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Peer-to-peer (P2P) networks have become a powerful means for online data exchange. Currently, users are primarily utilizing these networks to perform exact-match queries and retrieve complete files. However, future more data intensive applications, such as P2P auction networks, P2P job-search networks, P2P multiplayer games, will require the capability to respond to more complex queries such as range queries involving numerous data types including those that have a spatial component. In this paper, a distributed quadtree index that adapts the MX-CIF quadtree is described that enables more powerful accesses to data in P2P networks. This index has been implemented for various prototype P2P applications and results of experiments are presented. Our index is easy to use, scalable, and exhibits good load-balancing properties. Similar indices can be constructed for various multidimensional data types with both spatial and non-spatial components.
In this paper, we first present a variation of the 2-dimensional run-encoding, called the run-length Morton code encoding scheme, for compressing binary images, then we present efficient algorithms for manipulating se...
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In this paper, we first present a variation of the 2-dimensional run-encoding, called the run-length Morton code encoding scheme, for compressing binary images, then we present efficient algorithms for manipulating set operations and performing conversions between the proposed encoding scheme and some well-known spatial data structures. The time complexities of set operations are linearly proportional to the size (number) of the run-length Morton codes and the time complexities of conversions are linearly proportional to the number of the nodes in the corresponding quadtree/bintree with respect to the run-length Morton codes.
If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation co...
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If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out-of-core spatialdata structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point-based rendering technique adapted for attributed 3D point clouds, to enable effective out-of-core real-time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.
A space-filling curve in 2,3,or higher dimensions can be thought as a path of a continuously moving *** its main goal is to preserve spatial proximity,this type of curves has been widely used in the design and impleme...
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A space-filling curve in 2,3,or higher dimensions can be thought as a path of a continuously moving *** its main goal is to preserve spatial proximity,this type of curves has been widely used in the design and implementation of spatial data structures and nearest neighbor-finding *** paper is essentially focused on the efficient representation of Digital Ele-vation Models(DEM) that entirely fit into the main *** propose a new hierarchical quadtree-like data structure to be built over domains of unrestricted size,and a representation of a quadtree and a binary triangles tree by means of the Hilbert and the Sierpinski space-filling curves,respectively,taking into account the hierarchical nature and the clustering properties of this kind of *** triangulation schemes are described for the space-filling-curves-based approaches to efficiently visualize multiresolu-tion surfaces.
This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that invo...
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This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that involve complex spatial and chemical-structural relations. Using a case study from molecular biology, I show why causal graph models fail to adequately represent and explain biological phenomena of this kind. The inadequacy of these models is due to their failure to include relevant spatial-structural information in a way that does not render the models nonexplanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory.
spatial autocorrelation is a well-recognized concern for observational data in general, and more specifically for spatialdata in ecology. Generalized linear mixed models (GLMMs) with spatially autocorrelated random e...
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spatial autocorrelation is a well-recognized concern for observational data in general, and more specifically for spatialdata in ecology. Generalized linear mixed models (GLMMs) with spatially autocorrelated random effects are a potential general framework for handling these spatial correlations. However, as the result of statistical and practical issues, such GLMMs have been fitted through the undocumented use of procedures based on penalized quasi-likelihood approximations (PQL), and under restrictive models of spatial correlation. Alternatively, they are often neglected in favor of simpler but more questionable approaches. In this work we aim to provide practical and validated means of inference under spatial GLMMs, that overcome these limitations. For this purpose, a new software is developed to fit spatial GLMMs. We use it to assess the performance of likelihood ratio tests for fixed effects under spatial autocorrelation, based on Laplace or PQL approximations of the likelihood. Expectedly, the Laplace approximation performs generally slightly better, although a variant of PQL was better in the binary case. We show that a previous implementation of PQL methods in the R language, glmmPQL, is not appropriate for such applications. Finally, we illustrate the efficiency of a bootstrap procedure for correcting the small sample bias of the tests, which applies also to non-spatial models.
Due to high data volume, massive spatialdata requires considerable computing power for real-time processing. Currently, high performance clusters are the only economically viable solution given the development of mul...
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Due to high data volume, massive spatialdata requires considerable computing power for real-time processing. Currently, high performance clusters are the only economically viable solution given the development of multicore technology and computer component cost reduction in recent years. Massive spatialdata processing demands heavy I/O operations, however, and should be characterized as a data-intensive application. data-intensive application parallelization strategies, such as decomposition, scheduling and load-balance, are much different from that of traditional compute-intensive applications. In this article we introduce a Split-and-Merge paradigm for spatialdata processing and also propose a robust parallel framework in a cluster environment to support this paradigm. The Split-and-Merge paradigm efficiently exploits data parallelism for massive data processing. The proposed framework is based on the open-source TORQUE project and hosted on a multicore-enabled Linux cluster. A specific data-aware scheduling algorithm was designed to exploit data sharing between tasks and decrease the data communication time. Two LiDAR point cloud algorithms, IDW interpolation and Delaunay triangulation, were implemented on the proposed framework to evaluate its efficiency and scalability. Experimental results demonstrate that the system provides efficient performance speedup.
Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocaw, SW Poland. Seven UHI cases, measured during various meteorological ...
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Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocaw, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran's I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR-MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging;GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.
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