An efficient visual exploration approach of geospatial vector big data on the web map

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2023-12-09 DOI:10.1016/j.is.2023.102333

Zebang Liu , Luo Chen , Mengyu Ma , Anran Yang , Zhinong Zhong , Ning Jing

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引用次数: 0

Abstract

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD.

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网络地图上地理空间矢量大数据的高效可视化探索方法

地理空间矢量数据的可视化探索已日益成为地理空间矢量大数据（GVBD）管理和分析的重要组成部分。随着数据规模的快速增长，即使采用并行分布式计算技术，现有的可视化技术也难以实现对地理空间矢量大数据的高效可视化探索。为了填补这一空白，本文提出了一种在网络地图上对 GVBD 进行可视化探索的方法。在这种方法中，我们提出了显示驱动计算模型，并结合传统的数据驱动计算方法，设计了一种自适应实时可视化算法。同时，我们还设计了一种像素四R树形空间索引结构。最后，我们通过离线构建索引来支持可视化的在线计算，在单机上实现了GVBD的多层次实时交互式可视化探索，所有可视化结果均可实时计算，无需占用外部缓存。实验结果表明，该方法优于目前主流的可视化方法，可在0.5 s内获得任意缩放级别的可视化结果，可以很好地应用于亿级尺度GVBD的多级实时交互可视化探索。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.