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用于分析移动对象群体动态的基于空间的相对地理信息系统数据模型。

Relative space-based GIS data model to analyze the group dynamics of moving objects.

作者信息

Feng Mingxiang, Shaw Shih-Lung, Fang Zhixiang, Cheng Hao

机构信息

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, Hubei, PR China.

Collaborative Innovation Center of Geospatial Technology, 129 LuoyuRoad, Wuhan 430079, PR China.

出版信息

ISPRS J Photogramm Remote Sens. 2019 Jul;153:74-95. doi: 10.1016/j.isprsjprs.2019.05.002. Epub 2019 May 15.

DOI:10.1016/j.isprsjprs.2019.05.002
PMID:32288231
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7111340/
Abstract

The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

摘要

移动对象的相对运动是地理信息科学（GIScience）中的一个重要研究课题，它支持地理数据库、空间索引和地理空间服务的创新。这种分析在城市治理、交通工程、物流以及个人或行业的地理空间信息服务领域非常流行。重要的是，即使在大数据和云计算时代，移动对象的数据模型也是支持分析移动对象之间动态相对运动的最关键方法之一。传统地理信息系统（GIS）通常将移动对象组织为绝对坐标空间中的点对象。由于绝对空间和相对空间之间转换的额外地理计算，移动对象之间相对运动的推导效率不高。因此，当前的GIS需要一种创新方法来直接存储、分析和解释移动对象的相对关系，以支持其高效分析。本文提出了一种基于相对空间的移动对象GIS数据模型（RSMO）来构建、操作和分析移动对象的关系，并引入了两种算法（关系查询和相对关系动态模式匹配）来推导和分析移动对象的动态关系。实现了三个场景（疫情传播、追踪器查找和附近人群的运动趋势推导）来证明所提出模型的可行性。实验结果表明，对于这三个场景的相同功能，所提出模型的执行时间约为绝对GIS方法的5%-50%。基于此实验结果，在所知名商业GIS软件中分析移动对象的相对关系时，所提出模型的计算性能优于绝对方法。所提出的方法填补了传统GIS的空白，并显示出基于相对空间的地理计算、分析和服务的前景。

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