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About 10,000 papers for “spatial data structures”

SPATIAL DATA STRUCTURES

Sotvoldieva, Dildora, Mekhmonaliyev, Yakhyobek · Zenodo (CERN European Organization for Nuclear Research) · 2024

This article describes the types of data used in the process of data processing in geographic information systems and their use, as well as the structure of this data.

Cited by 0Open access

Spatial Data Infra Structures

Brande-Lavridsen, Hanne, Daugbjerg, Poul, Jensen, Bent Hulegaard · VBN Forskningsportal (Aalborg Universitet) · 2000

Cited by 0

and Spatial Data Structures

Dimitris Papadias, Yannis Theodoridis · 2008

5 are based on (Papadias et al., 1994c). The ideas of this paper were refined by our collaboration with

Cited by 0

Data Structures for Spatial Data Mining

Fi Mu, Petr Kuba, Petr Kuba · 2001

This report deals with spatial data structures for indexing and with their usability for knowledge discovery in spatial data. Huge amount of data processed in spatial data mining (or in data mining generally) requires using some indexing structures to speed up the mining process. Typical data types and operations used in geographic information systems are described in this paper. Then basic spatial data mining tasks and some spatial data mining systems are introduced. Finally, indexing spatial structures for both vector and metric spaces are described and structures used in some spatial data mining systems are presented.

Cited by 0

Compact data structures to represent spatial hierarchical structures

Fuentes, Jose, Seco, Diego, Rodriguez, Andrea et al. · Figshare · 2022

Implementation of three compact data structures to represent spatial hierarchical structures, with applications on the topological model.<br>For replication of the experiments, please, check the file README.md

Cited by 0Open access

Compact data structures to represent spatial hierarchical structures

José Fuentes, Diego Seco, M. Andrea Rodríguez et al. · Figshare · 2022

Implementation of three compact data structures to represent spatial hierarchical structures, with applications on the topological model.<br>For replication of the experiments, please, check the file README.md

Cited by 0Open access

Compact data structures to represent spatial hierarchical structures

José Fuentes, Diego Seco, M. Andrea Rodríguez et al. · Figshare · 2022

Implementation of three compact data structures to represent spatial hierarchical structures, with applications on the topological model.<br>For replication of the experiments, please, check the file README.md

Cited by 0Open access

Heterogeneous construction of spatial data structures

Butts, Robert O.

Linear spatial trees are typically constructed in two discrete, consecutive stages: calculating location codes, and sorting the spatial data according to the codes. Additionally, a GPU R-tree construction algorithm exists which likewise consists of sorting the spatial data and calculating nodes' bounding boxes. Current GPUs are approximately three orders of magnitude faster than CPUs for perfectly vectorizable problems. However, the best known GPU sorting algorithms only achieve 10-20 times speedup over sequential CPU algorithms. Both calculating location codes and bounding boxes are perfectly vectorizable problems. We thus investigate the construction of linear quadtrees, R-trees, and linear k-d trees using the GPU for location code and bounding box calculation, and parallel CPU algorithms for sorting. In this endeavor, we show how existing GPU linear quadtree and R-tree construction algorithms may be modified to be heterogeneous, and we develop a novel linear k-d tree construction algorithm which uses an existing parallel CPU quicksort partition algorithm. We implement these heterogeneous construction algorithms, and we show that heterogeneous construction of spatial data structures can approach the speeds of homogeneous GPU algorithms, while freeing the GPU to be used for better vectorizable problems.

Cited by 0

Data Structures for Spatial Database Systems

Octavian Procopiuc · 1997

This paper surveys the data structures that were proposed for efficient management of spatial data. These data structures were designed having in mind the types of queries asked on spatial data, which are usually proximity-based. It is thus essential to store together, in one sense or another, &amp;quot;nearby&amp;quot; objects. This condition is crucial for performance when the databases are very large---which is almost always the case---and data accesses require lengthy I/O operations.

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Using Hierarchical Spatial Data Structures for Hierarchical Spatial Reasoning

A. U. Frank Berlin-heidelberg, Sabine Timpf, Andrew U. Frank · 2008

This paper gives a definition of Hierarchical Spatial Reasoning, which computes increasingly better results in a hierarchical fashion and stops the computation when a result is achieved which is ‘good enough’. This is different from standard hierarchical algorithms, which use hierarchical data structures to improve efficiency in computing the correct result. An algorithm on hierarchical spatial data structure explores all details where such exist. An hierarchical reasoning algorithm stops processing if additional detail does not effectively contribute to the result and is thus more efficient. Hierarchical spatial data structures, especially quadtrees, are used in many implementations of GIS and have proved their efficiency. Operations on hierarchical spatial data structures are effective to compute spatial relations. They can be used for hierarchical spatial reasoning. A formal definition of hierarchical spatial reasoning requires • a coarsening function c, which produces a series of less detailed representations from a most detailed data set, • a function of interest f which is applicable to these representations, and • a function f ’ which computes for each representation the quality of the result. The computation starts with the least detailed representation and continues till a result with sufficient quality is found. This is demonstrated with a simplified example, based on a raster computation for area computation and overlap. Examples from the literature demonstrate that the same hierarchical aggregation and similar coarsening functions can be used for a wide variety of spatial reasoning tasks. 1

Cited by 0

Spatial Random Graphs: Studying dependence structures in spatial data

Leoni Carla Wirth · 2025

Spatial random graphs provide a powerful framework for analyzing the relations and interactions governed by an underlying spatial structure. Such graphs can be used not only to model and analyze observed networks, but also to represent complex data via synthetic networks, i.e. networks in which the edge structure is not observed but in which edges are introduced to provide a representation of additional information. In both cases, spatial graph models can be very useful for describing and highlighting the characteristics of the underlying dependence structures. A wide range of sophisticated spatial graph models is important for capturing some key structures of complicated real-world networks. This thesis makes substantial contributions to the theoretical study of dependence structures in spatial (random) graphs. It is based on three research articles (currently available as preprints on arXiv) and one work in progress. The thesis summarizes the approaches and main results of these papers, which are included in full in the appendix. A fundamental ingredient for understanding and analyzing the graph structure is a metric that measures the (dis)similarity between spatial random graphs in a natural way. The first research article constructs such a spatial graph metric by building on an optimal assignment of the vertices with costs based on the dissimilarity of both, vertex and edge structures. The paper demonstrates that the new graph metric can be used as a statistical tool to infer important graph properties and further serves as a foundation for deriving a metric for spatial random graph distributions. Due to the complexity of the underlying dependence structure, a direct analysis of spatial random graphs is often not feasible. Instead, the second paper studies the approximation of possibly more complicated spatial random graphs with simpler spatial random graphs. These simpler spatial random graphs are based on suitable independence assumptions and are a generalization of the random geometric graph consisting of vertices given by a Gibbs process and (conditionally) independent edges generated from a connection function. The results are derived by an application of Stein's method which allows to state explicit convergence rates with respect to the metric for spatial random graph distributions introduced in the first paper. An application of these results yields interesting convergence statements, for example, in the context of thinnings and discretizations of spatial random graphs. Another application is considered in the third paper, where synthetic networks are studied under a privacy constraint. In this context, a private synthetic graph generator is introduced that takes observed attributes as an input and jointly generates a network based on the true data and private data, respectively. Finally, as part of ongoing research, it is outlined how kernel Stein discrepancies can be used to perform goodness-of-fit tests for point processes.

Cited by 0

Satellite Collision Detection using Spatial Data Structures

Christian T. Hellwig, Fabian Czappa, Martin C. Michel et al. · 2023

In recent years, the number of artificial objects in Earth orbit has increased rapidly due to lower launch costs and new applications for satellites. More and more governments and private companies are discovering space for their own purposes. Private companies are using space as a new business field, launching thousands of satellites into orbit to offer services like worldwide Internet access. Consequently, the probability of collisions and, thus, the degradation of the orbital environment is rapidly increasing. To avoid devastating collisions at an early stage, efficient algorithms are required to identify satellites approaching each other. Traditional deterministic filter-based conjunction detection algorithms compare each satellite to every other satellite and pass them through a chain of orbital filters. Unfortunately, this leads to a runtime complexity of O(n<sup>2</sup>). In this paper, we propose two alternative approaches that rely on spatial data structures and thus allow us to exploit modern hardware’s parallelism efficiently. Firstly, we introduce a purely grid-based variant that relies on non-blocking atomic hash maps to identify conjunctions. Secondly, we present a hybrid method that combines this approach with traditional filter chains. Both implementations make it possible to identify conjunctions in a large population with millions of satellites with high precision in a comparatively short time. While the grid-based variant is characterized by lower memory consumption, the hybrid variant is faster if enough memory is available.

Cited by 0

Spatial data structures for geographic information systems

Green, N.P., Rhind, D.W, Birkbeck Coll., London (United Kingdom) · OpenGrey (Institut de l'Information Scientifique et Technique) · 1988

NERC Remote Sensing Special Topic; Conceptual design of a geographic information system for the NERC

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