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Proceedings ArticleDOI

The Research on the Compression Algorithms for Vector Data

Huifeng Ji, +1 more
- pp 1-4
TLDR
The compression algorithms of the line element are researched and the result is that the Douglas - Peucker method is best, followed by the vertical distance method, the offset angle method and choosing interval points method.
Abstract
Vector data compression system carries important significance in studies such as geography environment simulation, map database establishment and GIS. But there are lots of redundant points in the digital topographic map. In order to reduce storage space and improve the speed, vector data must be compressed. The compression algorithms of the line element are researched in the paper. The line element is tracked by the digital software. The four algorithms are researched such as choosing interval points, limiting vertical distance, limiting angle, Douglas-Peucker. It is better that the number of nodes and the error are less in line elements by the compression algorithms. The result is that the Douglas - Peucker method is best, followed by the vertical distance method, the offset angle method and choosing interval points method.

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Citations
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Proceedings ArticleDOI

FogGIS: Fog Computing for geospatial big data analytics

TL;DR: FogGIS as mentioned in this paper is a framework based on fog computing for mining analytics from geospatial data, it has been built a prototype using Intel Edison, an embedded microprocessor and has validated by doing preliminary analysis including compression and overlay analysis.
Journal ArticleDOI

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

TL;DR: This study developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data and compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time.
Book ChapterDOI

Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications

TL;DR: This book chapter discusses the concept of edge-assisted cloud computing and its relation to the emerging domain of “Fog-of-things (FoT)”, and proposes application-specific architectures GeoFog and Fog2Fog that are flexible and user-orientated.
Journal ArticleDOI

A dynamic adaptive grating algorithm for AIS-based ship trajectory compression

TL;DR: A dynamic adaptive threshold grating compression algorithm that has improved advantages in the ease of use, the applicability to different trajectories and compression performance, all of which can better support relevant applications, such as ship trajectory data storage and rapid cartographic display.
Journal ArticleDOI

Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis

Le Qi, +1 more
TL;DR: The pseudo-code of five typical vector data compression algorithms for ship trajectory data compression is introduced and the performances of these algorithms were tested by the compression experiments of actual ship trajectories in the Qiongzhou Strait, showing that ships’ speeds and rate of turns, the requirement of real time processing can affect the option of the most appropriate algorithm, and the algorithm selection in different applications is suggested.
References
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Journal ArticleDOI

Near-Linear Time Approximation Algorithms for Curve Simplification

TL;DR: This work presents near-linear time approximation algorithms that, given a parameter ε > 0, compute a simplified polygonal curve P’ whose error is less than ε and size at most the size of an optimal simplified Polygonal Curve with error ε/2.
Proceedings ArticleDOI

An O(nlogn) implementation of the Douglas-Peucker algorithm for line simplification

TL;DR: An important task of the cartographer’s art is to extract features from detailed data and represent them on a simple and readable map, and the line simplification problem is an important part of representing linear features.
Journal Article

Research in WebGIS Vector Spatial Data Compression Methods

TL;DR: This paper introduces author's work in compress to vector spatial data, which will be transported in Internet/Intranet, and proposes next four innovation on the basis ofvector spatial data compression method.
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