A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

Milaghardan, Amin Hosseinpoor; Abbaspour, Rahim Ali; Claramunt, Christophe

doi:10.3390/e20070490

Cited by 7 publications

(8 citation statements)

References 55 publications

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“…In the next step, a framework proposed in a previous study is applied to separately calculate the spatial entropy of each descriptor (i.e., curvature, turning, and self‐intersection points) according to the spatial distribution of the mentioned critical points (Hosseinpoor Milaghardan, Ali Abbaspour, & Claramunt, 2018b). Geometric trajectory similarities of the aforementioned descriptors were also evaluated by spatial entropy measures introduced in previous work (Hosseinpoor Milaghardan et al., 2018b). For this purpose, the diversity of the spatial distribution of each extracted critical point for a given trajectory is first evaluated using a measure of spatial entropy as given by Equations (1) and (2).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The objective is to extract trajectory data patterns by applying a K ‐means clustering method that minimizes distance variances between critical points. The geometric trajectory similarities of the descriptors mentioned are evaluated by spatial entropy measures (Hosseinpoor Milaghardan et al., 2018b). To this end, the spatial distribution for the critical points of each descriptor is calculated using the spatial entropy, and finally, a measure of entropy for each trajectory is calculated.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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An activity‐based framework for detecting human movement patterns in an urban environment

Milaghardan

Abbaspour

Claramunt

et al. 2021

Transactions in GIS

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The continuous development of positioning technologies and computing solutions for the integration of large trajectory data sets offers many novel research opportunities. Among various research domains, the extraction of users' movement patterns is an important issue that is yet to be addressed. While many previous studies have analyzed human and animal movements from a predominantly geometrical point of view, additional semantics are still required to provide a better understanding of the patterns that emerge. User activity data provide important information resources to analyze and predict movement patterns in urban environments. This study introduces a computational framework that combines the geometric and activity‐based dimensions of human trajectories. First, the geometrical dimension considers a series of parameters (i.e., turning points, curvature, and self‐intersection) that are extracted by a convex‐hull algorithm and characterizes a given trajectory. Second, user activity transitions are modeled and then denote some recurrent patterns. Finally, geometric and activity patterns are integrated into a unified trajectory modeling framework. This favors the analysis of human movement patterns by taking into account the geometric and activity dimensions. The entire approach and framework have experimented with the LifeMap Korean trajectory data set commonly considered as a reference benchmark. The experiments showed how the integration of geometrical and activity‐based dimensions could provide a better understanding of the patterns and trends that emerge from a large trajectory data set.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

An activity‐based framework for detecting human movement patterns in an urban environment

Milaghardan

Abbaspour

Claramunt

et al. 2021

Transactions in GIS

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“…The data collected in this way reflect the actual movement of real-world individuals and can be applied to a wide-range of use cases. However, due to privacy concerns and the high cost of large-scale data collection, we have seen a rising trend of using synthetic trajectory datasets in recent years [10,18,22]. In this section, we review several prior efforts to modeling human mobility and generating synthetic trajectories.…”

Section: Related Workmentioning

confidence: 99%

Generating contextual trajectories from user profiles

Yang

Poellabauer

Mitra

et al. 2020

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation

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The trajectory of traffic participants is an essential source for pattern mining and knowledge discovery in urban mobility. However, real-world trajectory data are often not publicly available due to privacy concerns or intellectual property constraints. Although some simulators or synthetic trajectory datasets have been proposed, many of them only consider the spatial-temporal aspects of the trajectory data, but ignore other contextual information that could impact trajectories. On one hand, trajectories are usually associated with and affected by user profiles (e.g., a person's daily routines and preferred modes of transportation). On the other hand, an individual's movements are also affected by environmental conditions and interactions with other traffic participants, particularly in urban scenarios (e.g., routing choices due to congestion or road conditions). Such contextual trajectories provide a more realistic representation of the mobility patterns of traffic participants. Due to the lack of such datasets or trace generators, this work presents ConTraSim (Contextual Trajectory Simulation), a novel approach for generating contextual trajectories based on the Simulation of Urban MObility (SUMO) traffic simulator. More specifically, the proposed approach is designed to produce GPS traces annotated by contextual information that mimic the movements of multiple types of traffic participants in urban areas. As a case study, we also generate a sample dataset using the proposed method and compare it to real-world data to demonstrate how well the synthetic data reflects real-world data characteristics. CCS CONCEPTS • Information systems → Spatial-temporal systems.

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“…Similarly, the local distance-ratio weight definition is asymmetric by essence but S or T can be focused on, not just C. A fully symmetric version, looking at categories defined as stc, leads to indicators that can take various forms depending on the choice of distances, e.g., closer to its definition as global indice [5], or to its spatio-temporal version [25,26]:…”

Section: With a Symmetric Or Non-symmetric Spatio-temporal Approachmentioning

confidence: 99%

On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework

Leibovici

Claramunt

2019

Entropy

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Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the processes involved, in relation to one or more phenomena, is often the first step before elaborating spatial functioning theories and specific planning actions, e.g., epidemiological modelling, urban planning. To do so, the spatio-temporal distributions of meaningful variables from a decision-making viewpoint, can be explored, analysed separately or jointly from an information viewpoint. Using metrics based on the measure of entropy has a long practice in these domains with the aim of quantification of how uniform the distributions are. However, the level of embedding of the spatio-temporal dimension in the metrics used is often minimal. This paper borrows from the landscape ecology concept of patch size distribution and the approach of permutation entropy used in biomedical signal processing to derive a spatio-temporal entropy analysis framework for categorical variables. The framework is based on a spatio-temporal structuration of the information allowing to use a decomposition of the Shannon entropy which can also embrace some existing spatial or temporal entropy indices to reinforce the spatio-temporal structuration. Multiway correspondence analysis is coupled to the decomposition entropy to propose further decomposition and entropy quantification of the spatio-temporal structuring information. The flexibility from these different choices, including geographic scales, allows for a range of domains to take into account domain specifics of the data; some of which are explored on a dataset linked to climate change and evolution of land cover types in Nordic areas.

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A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

Cited by 7 publications

References 55 publications

An activity‐based framework for detecting human movement patterns in an urban environment

An activity‐based framework for detecting human movement patterns in an urban environment

Generating contextual trajectories from user profiles

On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework

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