Visual exploration of urban functional zones based on augmented nonnegative tensor factorization

Liu, Liyan; Liu, Jiaxin; Liu, Shangching; Chen, Wei; Jia-ju, Man

doi:10.1007/s12650-020-00713-3

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…Additionally, Shi et al (2019b) leveraged multi-source mobility datasets and Point of Interest (POI) data to construct two region-feature-time tensors for mobility intentions extraction, unveiling insights into people’s travel intentions and region functionalities. Liu et al (2021) propose an augment nonnegative tensor factorization-based model that combine mobility semantics and inherent location information for mobility intention identifying. TPFlow ( Liu, Xu & Ren, 2019 ) introduced a pioneering piecewise rank-one tensor decomposition, facilitating automated partitioning and the extraction of multi-dimensional mobility intentions from spatiotemporal data.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…It effectively decomposes a tensor into a summation of rank-one components, allowing it to reveal latent structures within spatiotemporal datasets. Researchers have harnessed this method to analyze various types of spatiotemporal data, such as location-based social network (LBSN) data ( Luan et al, 2017 ; Shi et al, 2019b ), bikesharing data ( Yan et al, 2018 ) traffic flow data ( Han & Moutarde, 2016 ; Liu et al, 2021 ; Liu, Xu & Ren, 2019 ). For instance, Takeuchi, Kawahara & Iwata (2017) modeled traffic flow data as a three-dimensional tensor, where each dimension corresponded to days, hours, and geographical locations, respectively.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

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Mining human periodic behaviors via tensor factorization and entropy

Yi,

Su,

et al. 2024

PeerJ Computer Science

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Understanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges. We employ tensor decomposition to extract mobility intentions from spatiotemporal datasets, thereby revealing hidden structures in users’ historical records. Subsequently, we utilize subsequences associated with the same mobility intention to mine human periodic behaviors. Furthermore, we introduce a novel periodicity detection algorithm based on relative entropy. Our experimental results, conducted on real-world datasets, demonstrate the effectiveness of the MIRE model in accurately uncovering human periodic behaviors. Comparative analysis further reveals that the MIRE model significantly outperforms baseline periodicity detection algorithms.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: Preliminaries and Related Workmentioning

confidence: 99%

Mining human periodic behaviors via tensor factorization and entropy

Yi,

Su,

et al. 2024

PeerJ Computer Science

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“…Concerning trajectory points, Zhou et al [27] and Liu et al [1] implemented sampling strategies to reduce visual clutter, facilitating the discovery of trajectory patterns. Recognizing the large-scale nature of trajectory data, Zhou et al [3] designed visual representations of Origin-Destination (OD) flows, iteratively probing OD movement patterns.…”

Section: Visual Analysis Of Trajectory Topicmentioning

confidence: 99%

“…The most intuitive method to help the visual exploration of traffic trajectory is to reduce the visual density by computing the trajectory features. Liu et al [1] effectively reduce the visual density of Origin to Destinatin (OD) flow by dividing and exploring urban functional areas. Liu et al [2] employ a tensor decomposition algorithm to segment multi-dimensional spatiotemporal data and reduce the number of visualization elements on the same screen.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process

Chen,

Wu,

Tang

et al. 2024

Electronics

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The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.

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Progressive visual analysis of traffic data based on hierarchical topic refinement and detail analysis

Tao

Tang

2022

J Vis

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Visual exploration of urban functional zones based on augmented nonnegative tensor factorization

Cited by 10 publications

References 40 publications

Mining human periodic behaviors via tensor factorization and entropy

Mining human periodic behaviors via tensor factorization and entropy

Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process

Progressive visual analysis of traffic data based on hierarchical topic refinement and detail analysis

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