On Discovery of Spatiotemporal Influence-Based Moving Clusters

Patel, Dhaval

doi:10.1145/2631926

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…The profile discovery of moving vehicles and animals can be used to detect road design flaws in urban planning and understand the habitual behaviors of animal migration under different geographical conditions, respectively. Unlike moving clusters, objects are "static" in spatiotemporal influence-based moving clusters proposed in the literature [22]. A spatiotemporal influencebased moving cluster refers to a sequence of spatial clusters that can exhibit the influence of their "spread" over a set of nearby objects; such clusters can be applied in the study of infectious diseases, ideas, etc.…”

Section: Related Workmentioning

confidence: 99%

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Chen

2021

IJGI

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The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

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Section: Related Workmentioning

confidence: 99%

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Chen

2021

IJGI

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“…The discovery of movement patterns has an important place in the domains of trajectory data mining as these patterns can exhibit the rules of individuals' movements and their interactions. Typical movement patterns include the flock pattern [22][23][24][25][26], convoy pattern [27][28][29], leadership pattern [30][31][32], moving cluster [33,34], and crew [35]. Among these, the flock pattern has been paid much attention and it commonly plays an important role in many application fields.…”

Section: Introductionmentioning

confidence: 99%

Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques

Zhang

Miao

Wang

et al. 2023

IJERPH

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Carbon dioxide (CO2) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high–low attribute values criterion and the extreme number–duration values criterion. A case study is conducted based on the CO2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

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“…Some complementary statistical methods were used, namely, the correlation matrix (Kazmier, 2004), ANOVA and the sliding window model (Datar et al, 2002). In addition, clusters analysis for pattern mining applied on spatio-temporal data were reviewed to consider by future implementation (Patel, 2005;Xiao, 2014).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories

Corrêa

Oliveira

Gama

et al. 2016

Computers and Electronics in Agriculture

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Agribusiness is an activity that generates huge amounts of temporal data. There are research centers that collect, store and create indexes of agricultural activities, providing multidimensional time series composed by years of data. In this paper, we are interested in studying the behavior of these time series, especially in what regards the evolution of agricultural price indexes over the years. We explore data mining techniques tailored to analyze temporal data, aiming to generate spatio-temporal trajectories of grains price indexes for six years of data. We propose the use of Tucker decomposition to both analyze the temporal patterns of these price indexes and map trajectories that represent their behavior over time in a concise and representative low-dimensional subspace. The case study presents an application of this methodology to real databases of price indexes of corn and soybeans in Brazil and the United States.

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On Discovery of Spatiotemporal Influence-Based Moving Clusters

Cited by 5 publications

References 36 publications

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques

Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories

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