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

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About: Relative space-based GIS data model to analyze the group dynamics of moving objects Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Relative space-based GIS data model to analyze the group dynamics of moving objects
Creator	Cheng, Hao Fang, Zhixiang Feng, Mingxiang Shaw, Shih-Lung
topic	covid:f7b02969cbba5c043ee62b09248abd5c540e40e7#this
Source	Elsevier; Medline; PMC
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.
has issue date	2019-05-15(xsd:dateTime)
bibo:doi	10.1016/j.isprsjprs.2019.05.002
bibo:pmid	32288231
has license	no-cc
sha1sum (hex)	f7b02969cbba5c043ee62b09248abd5c540e40e7
schema:url	https://doi.org/10.1016/j.isprsjprs.2019.05.002
resource representing a document's title	Relative space-based GIS data model to analyze the group dynamics of moving objects
has PubMed Central identifier	PMC7111340
has PubMed identifier	32288231
schema:publication	ISPRS J Photogramm Remote Sens
resource representing a document's body	covid:f7b02969cbba5c043ee62b09248abd5c540e40e7#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/f7b02969cbba5c043ee62b09248abd5c540e40e7
is schema:about of	named entity 'relative' named entity 'dynamic' named entity 'Three' named entity 'space' named entity 'introduces' named entity 'relative' named entity 'support' named entity 'objects' named entity 'REMOTE SENSING' named entity 'MODELS' named entity 'ANALYSIS' named entity 'SERVICES' named entity 'PAPER' named entity 'MODEL OF' named entity '27S' named entity 'OBJECTS' named entity 'USUALLY' named entity 'ALGORITHMS' named entity 'APPROACHES' named entity 'COMPUTATION' named entity 'THESE' named entity 'EXECUTION' named entity 'INDIVIDUALS' named entity 'DATA MODEL' named entity 'EFFICIENT' named entity 'ONE OF' named entity 'ANALYZING' named entity 'TREND' named entity 'INTERPRET' named entity 'INDEXING' named entity 'INNOVATION' named entity 'EXPERIMENTAL RESULTS' named entity 'ENGINEERING' named entity 'MOTIONS' named entity 'SPACE' named entity 'PHOTOGRAMMETRY' named entity 'MOVING' named entity 'MODEL' named entity 'SPATIAL' named entity 'FINDING' named entity 'CONSTRUCT' named entity 'SCENARIOS' named entity 'NEARBY' named entity 'INFORMATION SERVICES' named entity 'SERVICE' named entity 'SOFTWARE' covid:arg/f7b02969cbba5c043ee62b09248abd5c540e40e7 named entity 'COORDINATED' named entity 'PATTERN' named entity 'IMPLEMENTED' named entity 'FEASIBILITY' named entity 'METHOD' named entity 'PROPOSED' named entity 'JOURNAL ' named entity 'BASED' named entity 'GIS' named entity 'RELATIONSHIPS' named entity 'RESEARCH' named entity 'METHODS' named entity 'PROMISE' named entity 'DYNAMIC' named entity 'TRANSPORTATION' named entity 'SPREADING' named entity 'TOPIC' named entity 'URBAN' named entity 'PERFORMANCE' named entity 'FUNCTION' named entity 'ABSOLUTE' named entity 'MATCHING' named entity 'GEOGRAPHIC INFORMATION SYSTEMS' named entity 'DERIVATION' named entity 'INTRODUCES' named entity 'CURRENT' named entity 'TIMES' named entity 'RELATIONSHIP' named entity 'GIS'

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