Journal articles: 'Spatio-temporal interaction'

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Grubesic, Tony H., and Elizabeth A. Mack. "Spatio-Temporal Interaction of Urban Crime." Journal of Quantitative Criminology 24, no. 3 (2008): 285–306. http://dx.doi.org/10.1007/s10940-008-9047-5.

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Sun, Dongchu, Robert K. Tsutakawa, Hoon Kim, and Zhuoqiong He. "Spatio-temporal interaction with disease mapping." Statistics in Medicine 19, no. 15 (2000): 2015–35. http://dx.doi.org/10.1002/1097-0258(20000815)19:15<2015::aid-sim422>3.0.co;2-e.

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Spiegel, Elmar, Thomas Kneib, and Fabian Otto-Sobotka. "Spatio-temporal expectile regression models." Statistical Modelling 20, no. 4 (2019): 386–409. http://dx.doi.org/10.1177/1471082x19829945.

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Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

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HAN, Lei, Jun-Feng LI, and Yun-De JIA. "Human Interaction Recognition Using Spatio-Temporal Words." Chinese Journal of Computers 33, no. 4 (2010): 776–84. http://dx.doi.org/10.3724/sp.j.1016.2010.00776.

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Onuki, Yoshiyuki, Eus J. W. Van Someren, Chris I. De Zeeuw, and Ysbrand D. Van der Werf. "Hippocampal–Cerebellar Interaction During Spatio-Temporal Prediction." Cerebral Cortex 25, no. 2 (2013): 313–21. http://dx.doi.org/10.1093/cercor/bht221.

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Kleinman, Erica, Nikitha Preetham, Zhaoqing Teng, Andy Bryant, and Magy Seif El-Nasr. ""What Happened Here!?" A Taxonomy for User Interaction with Spatio-Temporal Game Data Visualization." Proceedings of the ACM on Human-Computer Interaction 5, CHI PLAY (2021): 1–27. http://dx.doi.org/10.1145/3474687.

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Currently, there is no formal taxonomy for the activities that users engage in when interacting with and making meaning from spatio-temporal game data visualizations. As data visualization, especially spatio-temporal visualization, becomes more popular for game data analytics, it becomes increasingly crucial that we develop a formal understanding of how users, especially players, interact with and extract meaning from game data using these systems. However, existing taxonomies developed for InfoVis are not directly applicable due to domain differences and a lack of consensus within the literature. This paper presents the beginnings of a taxonomy for user interaction with spatio-temporal data specific to the domain of games, developed from the results of a qualitative user study (n=7) in which experienced players were tasked with using a spatio-temporal visualization system to explore and understand telemetry data from Defense of the Ancients 2 (DotA 2). The taxonomy includes seven activities organized into three categories: Data Interaction, Sense Making, and Validation. We discuss the implications of these activities on design and future research.

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Zhong, Hua, Jian Wang, Cai Chen, Jianlong Wang, Dong Li, and Kailin Guo. "Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction." Buildings 14, no. 3 (2024): 647. http://dx.doi.org/10.3390/buildings14030647.

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As the cornerstone of intelligent transportation systems, accurate traffic prediction can reduce the pressure of urban traffic, reduce the cost of residents’ travel time, and provide a reference basis for urban construction planning. Existing traffic prediction methods focus on spatio-temporal dependence modeling, ignoring the influence of weather factors on spatio-temporal characteristics, and the prediction task has complexity and an uneven distribution in different spatio-temporal scenarios and weather changes. In view of this, we propose a weather interaction-aware spatio-temporal attention network (WST-ANet), in which we integrate feature models and dynamic graph modules in the encoder and decoder, and use a spatio-temporal weather interaction perception module for prediction. Firstly, the contextual semantics of the traffic flows are fused using a feature embedding module to improve the adaptability to weather drivers; then, an encoder–decoder is constructed by combining the Dynamic Graph Module and the WSTA Block, to extract spatio-temporal aggregated correlations in the roadway network; finally, the feature information of the encoder was weighted and aggregated using the cross-focusing mechanism, and attention was paid to the hidden state of the encoding. Traffic flow was predicted using the PeMS04 and PeMS08 datasets and compared with multiple typical baseline models. It was learned through extensive experiments that the accuracy evaluation result is the smallest in WST-ANet, which demonstrated the superiority of the proposed model. This can more accurately predict future changes in traffic in different weather conditions, providing decision makers with a basis for optimizing scenarios.

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Balash, O. S. "Econometric Modeling of Spatial Interaction." Izvestiya of Saratov University. Economics. Management. Law 12, no. 3 (2012): 30–35. http://dx.doi.org/10.18500/1994-2540-2012-12-3-30-35.

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Verschueren, N., U. Bortolozzo, M. G. Clerc, and S. Residori. "Chaoticon: localized pattern with permanent dynamics." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2027 (2014): 20140011. http://dx.doi.org/10.1098/rsta.2014.0011.

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An analytical mechanism that support localized spatio-temporal chaos is provided. We consider a simple model— the Nagumo Kuramoto model —which contains the crucial ingredients for observing localized spatio-temporal chaos, namely, the spatio-temporal chaotic pattern and its coexistence with a uniform state. This model allows us to unveil the front dynamics and to show that it can be described by a chaotic motor corresponding to the deterministic counterpart of a Brownian motor. Front interaction is identified as the mechanism at the origin of the localized spatio-temporal chaotic structures.

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Wang, Zhenhua, Sheng Liu, Jianhua Zhang, Shengyong Chen, and Qiu Guan. "A Spatio-Temporal CRF for Human Interaction Understanding." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 8 (2017): 1647–60. http://dx.doi.org/10.1109/tcsvt.2016.2539699.

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Li, Y., K. S. Kim, J. Deschamps, R. Briandet, and A. Trubuil. "Spatio-temporal Interaction of Bacteria Mixture within Biofilms." Procedia Environmental Sciences 26 (2015): 11–18. http://dx.doi.org/10.1016/j.proenv.2015.05.009.

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Minta, Steven C. "Sexual differences in spatio-temporal interaction among badgers." Oecologia 96, no. 3 (1993): 402–9. http://dx.doi.org/10.1007/bf00317511.

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Xia, Qian, and Yanrui Ding. "Thermostability of Lipase A and Dynamic Communication Based on Residue Interaction Network." Protein & Peptide Letters 26, no. 9 (2019): 702–16. http://dx.doi.org/10.2174/0929866526666190617091812.

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Objective: Dynamic communication caused by mutation affects protein stability. The main objective of this study is to explore how mutations affect communication and to provide further insight into the relationship between heat resistance and signal propagation of Bacillus subtilis lipase (Lip A). Methods: The relationship between dynamic communication and Lip A thermostability is studied by long-time MD simulation and residue interaction network. The Dijkstra algorithm is used to get the shortest path of each residue pair. Subsequently, time-series frequent paths and spatio-temporal frequent paths are mined through an Apriori-like algorithm. Results: Time-series frequent paths show that the communication between residue pairs, both in wild-type lipase (WTL) and mutant 6B, becomes chaotic with an increase in temperature; however, more residues in 6B can maintain stable communication at high temperature, which may be associated with the structural rigidity. Furthermore, spatio-temporal frequent paths reflect the interactions among secondary structures. For WTL at 300K, β7, αC, αB, the longest loop, αA and αF contact frequently. The 310-helix between β3 and αA is penetrated by spatio-temporal frequent paths. At 400K, only αC can be frequently transmitted. For 6B, when at 300K, αA and αF are in more tight contact by spatio-temporal frequent paths though I157M and N166Y. Moreover, the rigidity of the active site His156 and the C-terminal of Lip A are increased, as reflected by the spatio-temporal frequent paths. At 400K, αA and αF, 310-helix between β3 and αA, the longest loop, and the loop where the active site Asp133 is located can still maintain stable communication. Conclusion: From the perspective of residue dynamic communication, it is obviously found that mutations cause changes in interactions between secondary structures and enhance the rigidity of the structure, contributing to the thermal stability and functional activity of 6B.

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Cao, Xiang Ping, Zhao Yang Li, and Mei Xing Liu. "Low-Dimensional Approximation for Control of Spatio-Temporal Processes Using Principal Interaction Patterns." Advanced Materials Research 591-593 (November 2012): 1217–20. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1217.

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Although the first-principal models of the spatio-temporal processes can accurately predict nonlinear and distributed dynamical behaviors, their infinite-dimensional nature does not allow their directly use. In this note, low-dimensional approximations for control of spatio-temporal processes using principal interaction patterns are constructed. Advanced model reduction approach based on spatial basis function expansion together with Galerkin method is used to obtain the low-dimensional approximation. Spatial structure called principal interaction patterns are extracted from the system according to a variational principle and used as basis functions in a Galerkin approximation. The simulations of the burgers equations has illustrated that low-dimensional approximation based on principal interaction patterns for spatio-temporal processes has smaller errors than more conventional approaches using Fourier modes or Empirical Eigenfunctions as basis functions.

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GABRIEL, E., D. J. WILSON, A. J. H. LEATHERBARROW, et al. "Spatio-temporal epidemiology of Campylobacter jejuni enteritis, in an area of Northwest England, 2000–2002." Epidemiology and Infection 138, no. 10 (2010): 1384–90. http://dx.doi.org/10.1017/s0950268810000488.

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SUMMARYA total of 969 isolates of Campylobacter jejuni originating in the Preston, Lancashire postcode district over a 3-year period were characterized using multi-locus sequence typing. Recently developed statistical methods and a genetic model were used to investigate temporal, spatial, spatio-temporal and genetic variation in human C. jejuni infections. The analysis of the data showed statistically significant seasonal variation, spatial clustering, small-scale spatio-temporal clustering and spatio-temporal interaction in the overall pattern of incidence, and spatial segregation in cases classified according to their most likely species-of-origin.

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Wang, Hongyu, Xiaotao Liu, Yifan Li, Meng Sun, Dian Yuan, and Jing Liu. "Temporal Adaptive RGBT Tracking with Modality Prompt." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5436–44. http://dx.doi.org/10.1609/aaai.v38i6.28352.

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RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

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Subramanian, Priya, Oliver Brausch, Karen E. Daniels, Eberhard Bodenschatz, Tobias M. Schneider, and Werner Pesch. "Spatio-temporal patterns in inclined layer convection." Journal of Fluid Mechanics 794 (April 6, 2016): 719–45. http://dx.doi.org/10.1017/jfm.2016.186.

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This paper reports on a theoretical analysis of the rich variety of spatio-temporal patterns observed recently in inclined layer convection at medium Prandtl number when varying the inclination angle ${\it\gamma}$ and the Rayleigh number $R$. The present numerical investigation of the inclined layer convection system is based on the standard Oberbeck–Boussinesq equations. The patterns are shown to originate from a complicated competition of buoyancy driven and shear-flow driven pattern forming mechanisms. The former are expressed as longitudinal convection rolls with their axes oriented parallel to the incline, the latter as perpendicular transverse rolls. Along with conventional methods to study roll patterns and their stability, we employ direct numerical simulations in large spatial domains, comparable with the experimental ones. As a result, we determine the phase diagram of the characteristic complex 3-D convection patterns above onset of convection in the ${\it\gamma}{-}R$ plane, and find that it compares very well with the experiments. In particular we demonstrate that interactions of specific Fourier modes, characterized by a resonant interaction of their wavevectors in the layer plane, are key to understanding the pattern morphologies.

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Chen, Jiaxun, Athanasios C. Micheas, and Scott H. Holan. "Hierarchical Bayesian modeling of spatio-temporal area-interaction processes." Computational Statistics & Data Analysis 167 (March 2022): 107349. http://dx.doi.org/10.1016/j.csda.2021.107349.

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Ramos-Robles, Michelle, Orthon Ricardo Vargas-Cardoso, Angélica María Corona-López, Alejandro Flores-Palacios, and Víctor Hugo Toledo-Hernández. "Spatio-temporal variation of Cerambycidae-host tree interaction networks." PLOS ONE 15, no. 2 (2020): e0228880. http://dx.doi.org/10.1371/journal.pone.0228880.

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Schwiedrzik, C. M., A. Alink, A. Kohler, W. Singer, and L. Muckli. "A spatio-temporal interaction on the apparent motion trace." Vision Research 47, no. 28 (2007): 3424–33. http://dx.doi.org/10.1016/j.visres.2007.10.004.

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Liang, Jinglian, Chao Xu, Zhiyong Feng, and Xirong Ma. "Affective interaction recognition using spatio-temporal features and context." Computer Vision and Image Understanding 144 (March 2016): 155–65. http://dx.doi.org/10.1016/j.cviu.2015.10.008.

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Dong, Ni, Helai Huang, Jaeyoung Lee, Mingyun Gao, and Mohamed Abdel-Aty. "Macroscopic hotspots identification: A Bayesian spatio-temporal interaction approach." Accident Analysis & Prevention 92 (July 2016): 256–64. http://dx.doi.org/10.1016/j.aap.2016.04.001.

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Huynh-Thu, Quan, and Mohammed Ghanbari. "Modelling of spatio–temporal interaction for video quality assessment." Signal Processing: Image Communication 25, no. 7 (2010): 535–46. http://dx.doi.org/10.1016/j.image.2010.03.006.

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Moiseev, S. A., and R. M. Gorodnichev. "SPATIO-TEMPORAL PATTERNS OF CORTICAL-MUSCULAR INTERACTION DURING LOCOMOTION." Журнал высшей нервной деятельности им. И.П. Павлова 73, no. 5 (2023): 666–79. http://dx.doi.org/10.31857/s0044467723050088.

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The paper considers the synergetic effects manifested at the cortical and muscular levels during locomotor activity showed in conditions of horizontal hanging of the lower extremities. The analysis of the synergies’ spatio-temporal structure was carried out using the data matrix factorization methods. It was found out the control of the movements’ structure is mainly realized through the three muscle synergies. The activity synchronization of the motor, associative, visual and sensorimotor regions of the cortex bilateral part is due to the specifics of performing locomotion in conditions of gravitational unloading and the associated features of receptor signaling. The identified components indicating synchronization of different areas of the right and left cortex may reflect the control processes associated with the control of alternate activation of the flexor and extensor muscles of the contralateral limb during locomotion. The data on the cortical activity spatial-temporal structuring indicate the separate management of muscle synergies through synchronization of cortical commands and the temporary organization of muscle synergies in the frequency ranges 0.30 to 8.00 Hz. Such patterns may reflect the activity of the rhythm-generating mechanism involved in the management of cyclic locomotor activity.

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Sailer, Christian, Peter Kiefer, Joram Schito, and Martin Raubal. "Map-based Visual Analytics of Moving Learners." International Journal of Mobile Human Computer Interaction 8, no. 4 (2016): 1–28. http://dx.doi.org/10.4018/ijmhci.2016100101.

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Location-based mobile learning (LBML) is a type of mobile learning in which the learning content is related to the location of the learner. The evaluation of LBML concepts and technologies is typically performed using methods known from classical usability engineering, such as questionnaires or interviews. In this paper, the authors argue for applying visual analytics to spatial and spatio-temporal visualizations of learners' trajectories for evaluating LBML. Visual analytics supports the detection and interpretation of spatio-temporal patterns and irregularities in both, single learners' as well as multiple learners' trajectories, thus revealing learners' typical behavior patterns and potential problems with the LBML software, hardware, the didactical concept, or the spatial and temporal embedding of the content.

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Liang, Yaoyuan, Xiao Liang, Yansong Tang, et al. "CoSTA: End-to-End Comprehensive Space-Time Entanglement for Spatio-Temporal Video Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3324–32. http://dx.doi.org/10.1609/aaai.v38i4.28118.

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This paper studies the spatio-temporal video grounding task, which aims to localize a spatio-temporal tube in an untrimmed video based on the given text description of an event. Existing one-stage approaches suffer from insufficient space-time interaction in two aspects: i) less precise prediction of event temporal boundaries, and ii) inconsistency in object prediction for the same event across adjacent frames. To address these issues, we propose a framework of Comprehensive Space-Time entAnglement (CoSTA) to densely entangle space-time multi-modal features for spatio-temporal localization. Specifically, we propose a space-time collaborative encoder to extract comprehensive video features and leverage Transformer to perform spatio-temporal multi-modal understanding. Our entangled decoder couples temporal boundary prediction and spatial localization via an entangled query, boasting an enhanced ability to capture object-event relationships. We conduct extensive experiments on the challenging benchmarks of HC-STVG and VidSTG, where CoSTA outperforms existing state-of-the-art methods, demonstrating its effectiveness for this task.

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Malet-Karas, Aurore, Marion Noulhiane, and Valérie Doyère. "Dynamics of Spatio-Temporal Binding in Rats." Timing & Time Perception 7, no. 1 (2019): 27–47. http://dx.doi.org/10.1163/22134468-20181124.

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Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task, preventing a comparison of their interaction. In this project, using a version of a timing task with a spatial component, we investigate the learning of a spatio-temporal rule in animals. To do so, rats were placed in front of a five-hole nose-poke wall in a Peak Interval (PI) procedure to obtain a reward, with two spatio-temporal combination rules associated with different to-be-timed cues and lighting contexts. We report that, after successful learning of the discriminative task, a single Pavlovian session was sufficient for the animals to learn a new spatio-temporal association. This was seen as evidence for a beneficial transfer to the new spatio-temporal rule, as compared to control animals that did not experience the new spatio-temporal association during the Pavlovian session. The benefit was observed until nine days later. The results are discussed within the framework of adaptation to a change of a complex associative rule involving interval timing processes.

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Liu, Tianyu, and Yuehuan Wang. "A Dual Stream Model for Anti-infrared Decoys: Exploiting Interaction and Contextual Relationship." Journal of Physics: Conference Series 2253, no. 1 (2022): 012039. http://dx.doi.org/10.1088/1742-6596/2253/1/012039.

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Abstract The infrared decoy jamming release process has spatio-temporal correlation. In order to mine the spatio-temporal correlation between target and decoys, we propose a dual stream model for anti-decoys based on graph convolutional network and long short-term network. The method proposed models the interaction relationship of the target and decoys, and combines contextual information to infer the real target. Experimental results on our prepared dataset with ground truth labels demonstrate that the algorithm proposed outperforms existed anti-decoy jamming methods in various metrics.

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Fu, Wen, Camille Bonnet, Julie Figoni, Alexandra Septfons, and Raphaëlle Métras. "Exploratory Space–Time Analyses of Reported Lyme Borreliosis Cases in France, 2016–2019." Pathogens 10, no. 4 (2021): 444. http://dx.doi.org/10.3390/pathogens10040444.

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In recent decades, the incidence of Lyme borreliosis (LB) in Europe seems to have increased, underpinning a growing public health concern. LB surveillance systems across the continent are heterogeneous, and the spatial and temporal patterns of LB reports have been little documented. In this study, we explored the spatio-temporal patterns of LB cases reported in France from 2016 to 2019, to describe high-risk clusters and generate hypotheses on their occurrence. The space–time K-function and the Kulldorf’s scan statistic were implemented separately for each year to evaluate space–time interaction between reported cases and searching clusters. The results show that the main spatial clusters, of radius size up to 97 km, were reported in central and northeastern France each year. In 2017–2019, spatial clusters were also identified in more southern areas (near the Alps and the Mediterranean coast). Spatio-temporal clustering occurred between May and August, over one-month to three-month windows in 2016–2017 and in 2018–2019. A strong spatio-temporal interaction was identified in 2018 within 16 km and seven days, suggesting a potential local and intense pathogen transmission process. Ongoing improved surveillance and accounting for animal hosts, vectors, meteorological factors and human behaviors are keys to further elucidate LB spatio-temporal patterns.

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Cui, Zhoujuan, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, and Xiaoming Tao. "Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction." Journal of Physics: Conference Series 2833, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1742-6596/2833/1/012010.

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Abstract For intelligent transportation systems, accurately forecasting the future trajectories of multiple agents is pivotal. Considering the increased diversity of agents within a scene, in order to capture and model the variations in their appearance, motion status, behavioral patterns, and interrelationships, we propose a simple yet effective framework based on Spatio-Temporal-Interaction Graph Neural Networks. Specifically, a Multi-Class Agent Encoder is meticulously tailored to the specific class of each agent to distill pertinent information from their motion attributes and historical trajectories. Subsequently, a Spatio-Temporal-Interaction Graph Attention Module is constructed to productively represent and learn the complex, dynamic interactions. Finally, a Multimodal Trajectory Generation Module is customized, and a learnable diversity sampling function is introduced to map the features of each agent to a set of potential variables, so as to capture the multimodal distribution of future trajectories. Empirical evaluations on the ETH/UCY and KITTI datasets reveal that our method can efficiently improve the accuracy of trajectory prediction.

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LU, CHUNSHENG, DAVID VERE-JONES, HIDEKI TAKAYASU, ALEX YU TRETYAKOV, and MISAKO TAKAYASU. "SPATIO-TEMPORAL SEISMICITY IN AN ELASTIC BLOCK LATTICE MODEL." Fractals 07, no. 03 (1999): 301–11. http://dx.doi.org/10.1142/s0218348x9900030x.

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An elastic block lattice model is proposed to simulate the spatio-temporal seismicity and stress patterns in the Earth's brittle crust. The famous Gutenberg-Richter magnitude-frequency law in seismology is reproduced. The synthetic catalogs generated by this model are analyzed by using a linked stress release model, which incorporates the stress transfer and spatial interactions. The results highlight the triggering mechanism of earthquake occurrence and the evidence that the crust may lie in a near-critical or self-organized critical state due to the long-range spatial interaction of elastic stress. The spatio-temporal complexity of seismicity is closely related to both nonlinear dynamics of faults and heterogeneities in a seismic region.

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Xie, Jincan, Shuang Li, and Chunsheng Liu. "Traffic Agents Trajectory Prediction Based on Spatial–Temporal Interaction Attention." Sensors 23, no. 18 (2023): 7830. http://dx.doi.org/10.3390/s23187830.

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Trajectory prediction aims to predict the movement intention of traffic participants in the future based on the historical observation trajectories. For traffic scenarios, pedestrians, vehicles and other traffic participants have social interaction of surrounding traffic participants in both time and spatial dimensions. Most previous studies only use pooling methods to simulate the interaction process between participants and cannot fully capture the spatio-temporal dependence, possibly accumulating errors with the increase in prediction time. To overcome these problems, we propose the Spatial–Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet), which can effectively model the spatial–temporal interaction information. Based on trajectory feature extraction, the novel Spatial–Temporal Interaction Attention Module (STIA Module) is proposed to extract the interaction relationships between traffic participants, including temporal interaction attention, spatial interaction attention, and spatio-temporal attention fusion. By adaptive allocation of attention weights, temporal interaction attention is a temporal attention mechanism used to capture the movement pattern of each traffic participant in the scene, which can learn the importance of historical trajectories at different moments to future behaviors. Since the participants number in recent traffic scenes dynamically changes, the spatial interaction attention is designed to abstract the traffic participants in the scene into graph nodes, and abstract the social interaction between participants into graph edges. Coupling the temporal and spatial interaction attentions can adaptively model the temporal–spatial information and achieve accurate trajectory prediction. By performing experiments on the INTERACTION dataset and the UTP (Unmanned Aerial Vehicle-based Trajectory Prediction) dataset, the experimental results show that the proposed method significantly improves the accuracy of trajectory prediction and outperforms the representative methods in comparison.

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Münkel, Markus, Friedemann Kaiser, and Ortwin Hess. "Spatio-Temporal Dynamics in Semiconductor Lasers with Delayed Optical Feedback." International Journal of Bifurcation and Chaos 08, no. 05 (1998): 951–63. http://dx.doi.org/10.1142/s0218127498000760.

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We investigate the influence of delayed optical feedback (DOF) on the dynamics of semiconductor lasers. In the case of the narrow single-stripe laser, we find that the presence of DOF leads to a wealth of dynamical phenomena in the coherence-collapsed regime, including mode-hopping between compound-cavity modes induced by DOF. Focusing on the twin-stripe laser — the most simple system with inherent spatio-temporal instabilities — we show that feedback may both induce and suppress spatio-temporal instabilities. Eigenmode analysis enables us to determine and identify the underlying spatio-temporal "supermodes". For appropriately chosen parameters, regular regimes including continuous wave operation can be obtained from an originally chaotic regime. For moderate to strong feedback, interaction between the spatial degrees of freedom in the twin-stripe laser and the compound cavity modes leads to a new phenomenon which we term "spatio-temporal mode-hopping".

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Williams, Matthew J., and Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness." Royal Society Open Science 3, no. 6 (2016): 160196. http://dx.doi.org/10.1098/rsos.160196.

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Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

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Meister, Daniel, Adam Pospíšil, Imari Sato, and Jiří Bittner. "Spatio-temporal BRDF: Modeling and synthesis." Computers & Graphics 97 (June 2021): 279–91. http://dx.doi.org/10.1016/j.cag.2021.04.001.

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Rallis, Ioannis, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos, and Vassilios Vescoukis. "Spatio-temporal summarization of dance choreographies." Computers & Graphics 73 (June 2018): 88–101. http://dx.doi.org/10.1016/j.cag.2018.04.003.

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37

Holden, Arun V., and A. V. Panfilov. "Graphical identification of spatio-temporal chaos." Computers & Graphics 15, no. 2 (1991): 301–2. http://dx.doi.org/10.1016/0097-8493(91)90084-u.

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Koo, Hyeongmo, Soyoung Lee, Jiyeong Lee, and Daeheon Cho. "Spatio-Temporal Variability of the Impact of Population Mobility on Local Business Sales in Response to COVID-19 in Seoul, Korea." ISPRS International Journal of Geo-Information 11, no. 10 (2022): 532. http://dx.doi.org/10.3390/ijgi11100532.

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Social distancing is an effective method for controlling the COVID-19 pandemic by decreasing population mobility, but it has also negatively affected local business sales. This paper explores the spatio-temporal impact of population mobility on local business sales in response to COVID-19 in Seoul, South Korea. First, this study examined the temporal variability by analyzing statistical interaction terms in linear regression models. Second, the spatio-temporal variability was captured using Moran eigenvector spatial filtering (MESF)-based spatially varying coefficients (SVC) models with additional statistical interaction terms. Population mobility and local business sales were estimated from public transportation ridership and restaurant sales, respectively, which were both obtained from spatial big datasets. The analysis results show the existence of various relationships between changes in the population mobility and local business sales according to the corresponding period and region. This study confirms the usability of spatial big datasets and spatio-temporal varying coefficients models for COVID-19 studies and provides support for policy-makers in response to infectious disease.

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PELEKIS, NIKOS, BABIS THEODOULIDIS, IOANNIS KOPANAKIS, and YANNIS THEODORIDIS. "Literature review of spatio-temporal database models." Knowledge Engineering Review 19, no. 3 (2004): 235–74. http://dx.doi.org/10.1017/s026988890400013x.

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Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

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Sheng, Hao, Yang Zhang, Yubin Wu, et al. "Hypothesis Testing Based Tracking With Spatio-Temporal Joint Interaction Modeling." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (2020): 2971–83. http://dx.doi.org/10.1109/tcsvt.2020.2988649.

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Redenbach, Claudia, and Aila Särkkä. "Parameter estimation for growth interaction processes using spatio-temporal information." Computational Statistics & Data Analysis 57, no. 1 (2013): 672–83. http://dx.doi.org/10.1016/j.csda.2012.08.006.

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Banerjee, M., and L. Zhang. "Stabilizing Role of Nonlocal Interaction on Spatio-temporal Pattern Formation." Mathematical Modelling of Natural Phenomena 11, no. 5 (2016): 103–18. http://dx.doi.org/10.1051/mmnp/201611507.

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Lee, Dae-Jin, and María Durbán. "P-spline ANOVA-type interaction models for spatio-temporal smoothing." Statistical Modelling: An International Journal 11, no. 1 (2011): 49–69. http://dx.doi.org/10.1177/1471082x1001100104.

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Neumaier, Sebastian, and Axel Polleres. "Enabling Spatio-Temporal Search in Open Data." Journal of Web Semantics 55 (March 2019): 21–36. http://dx.doi.org/10.1016/j.websem.2018.12.007.

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Shabunin, Aleksej, Vladimir Astakhov, and Artem Akopov. "Evolution of running waves to spatio-temporal chaos: interaction of temporal and spatial dynamics in a ring of period-doubling self-oscillators." Izvestiya VUZ. Applied Nonlinear Dynamics 11, no. 3 (2003): 31–37. http://dx.doi.org/10.18500/0869-6632-2003-11-3-31-37.

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In the work we consider transition from regular running waves to developed spatio-temporal chaos in а chain оf period-doubling oscillators. We investigate typical bifurcations which take place оn thе base of the chosen running wave regime from the period-one cycle to developed temporal chaos. We found that oscillations remain spatially periodic until transition to temporal chaos. The exact spatial periodicity is changed by the periodicity in the average in the chaotic region. Destroying of the averaged spatio-periodic structure is connected with losing оf coherence оn main frequencies in the temporal spectra оf neighbor oscillators in the chain.

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Alghodhaifi, Hesham, and Sridhar Lakshmanan. "Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions." Sensors 23, no. 17 (2023): 7361. http://dx.doi.org/10.3390/s23177361.

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Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians’ unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle–pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle–pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle–pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle–pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle–pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle–pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

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Dai, Hou-Ping, Wei Tan, and Zhou-Shun Zheng. "Spatio-temporal dynamics and interaction of lump solutions for the (4+1)-D Fokas equation." Thermal Science 22, no. 4 (2018): 1823–30. http://dx.doi.org/10.2298/tsci1804823d.

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The (4+1)-D Fokas equation is a new and important physical model. Its Hirota's bilinear form with a perturbation parameter is obtained by an appropriate trans-formation. A class of lump solutions and three different forms of spatio-temporal structure are obtained. Meanwhile, the theoretical analysis for the change of spatio-temporal structure is discussed by using the extreme value theory of multivariate function. Finally, the interaction between a stripe soliton and lump solution is discussed, and a new wave phenomenon that the lump solution is swallowed and drowned by the stripe soliton is investigated.

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Sighencea, Bogdan Ilie, Ion Rareș Stanciu, and Cătălin Daniel Căleanu. "D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks." Electronics 12, no. 3 (2023): 611. http://dx.doi.org/10.3390/electronics12030611.

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Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. For predicting pedestrian trajectories, an attention-based interaction-aware spatio-temporal graph neural network is introduced. This paper introduces an approach based on two components: a spatial graph neural network (SGNN) for interaction-modeling and a temporal graph neural network (TGNN) for motion feature extraction. The SGNN uses an attention method to periodically collect spatial interactions between all pedestrians. The TGNN employs an attention method as well, this time to collect each pedestrian’s temporal motion pattern. Finally, in the graph’s temporal dimension characteristics, a time-extrapolator convolutional neural network (CNN) is employed to predict the trajectories. Using a lower variable size (data and model) and a better accuracy, the proposed method is compact, efficient, and better than the one represented by the social-STGCNN. Moreover, using three video surveillance datasets (ETH, UCY, and SDD), D-STGCN achieves better experimental results considering the average displacement error (ADE) and final displacement error (FDE) metrics, in addition to predicting more social trajectories.

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Compieta, P., S. Di Martino, M. Bertolotto, F. Ferrucci, and T. Kechadi. "Exploratory spatio-temporal data mining and visualization." Journal of Visual Languages & Computing 18, no. 3 (2007): 255–79. http://dx.doi.org/10.1016/j.jvlc.2007.02.006.

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Andrienko, Natalia, Gennady Andrienko, and Peter Gatalsky. "Exploratory spatio-temporal visualization: an analytical review." Journal of Visual Languages & Computing 14, no. 6 (2003): 503–41. http://dx.doi.org/10.1016/s1045-926x(03)00046-6.

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Journal articles on the topic 'Spatio-temporal interaction'

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