Journal articles: 'Spatio temporal networks'

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Williams, Matthew J., and Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness." Royal Society Open Science 3, no. 6 (June 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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QIN, Chao, and Xiaoguang GAO. "Spatio-Temporal Generative Adversarial Networks." Chinese Journal of Electronics 29, no. 4 (July 1, 2020): 623–31. http://dx.doi.org/10.1049/cje.2020.04.001.

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Chao, Qin, and Gao Xiaoguang. "Distributed spatio-temporal generative adversarial networks." Journal of Systems Engineering and Electronics 31, no. 3 (June 2020): 578–92. http://dx.doi.org/10.23919/jsee.2020.000026.

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Pichardo-Corpus, J. A., H. A. Solano Lamphar, R. Lopez-Farias, and O. Delgadillo Ruiz. "Spatio-temporal networks of light pollution." Journal of Quantitative Spectroscopy and Radiative Transfer 253 (September 2020): 107068. http://dx.doi.org/10.1016/j.jqsrt.2020.107068.

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Gao, Nan, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D. Salim. "Generative Adversarial Networks for Spatio-temporal Data: A Survey." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3474838.

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Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

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Yang, Zhaoqilin, Gaoyun An, and Ruichen Zhang. "STSM: Spatio-Temporal Shift Module for Efficient Action Recognition." Mathematics 10, no. 18 (September 10, 2022): 3290. http://dx.doi.org/10.3390/math10183290.

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The modeling, computational complexity, and accuracy of spatio-temporal models are the three major foci in the field of video action recognition. The traditional 2D convolution has low computational complexity, but it cannot capture the temporal relationships. Although the 3D convolution can obtain good performance, it is with both high computational complexity and a large number of parameters. In this paper, we propose a plug-and-play Spatio-Temporal Shift Module (STSM), which is a both effective and high-performance module. STSM can be easily inserted into other networks to increase or enhance the ability of the network to learn spatio-temporal features, effectively improving performance without increasing the number of parameters and computational complexity. In particular, when 2D CNNs and STSM are integrated, the new network may learn spatio-temporal features and outperform networks based on 3D convolutions. We revisit the shift operation from the perspective of matrix algebra, i.e., the spatio-temporal shift operation is a convolution operation with a sparse convolution kernel. Furthermore, we extensively evaluate the proposed module on Kinetics-400 and Something-Something V2 datasets. The experimental results show the effectiveness of the proposed STSM, and the proposed action recognition networks may also achieve state-of-the-art results on the two action recognition benchmarks.

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Schutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut, and Markus Reischl. "Strategies for supplementing recurrent neural network training for spatio-temporal prediction." at - Automatisierungstechnik 67, no. 7 (July 26, 2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.

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Abstract In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.

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Tempelmeier, Nicolas, Udo Feuerhake, Oskar Wage, and Elena Demidova. "Mining Topological Dependencies of Recurrent Congestion in Road Networks." ISPRS International Journal of Geo-Information 10, no. 4 (April 8, 2021): 248. http://dx.doi.org/10.3390/ijgi10040248.

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The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

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Zhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.

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Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

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Li, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, and Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–21. http://dx.doi.org/10.1145/3510829.

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Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

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Suhartono, Suhartono, Dedy Dwi Prastyo, Heri Kuswanto, and Muhammad Hisyam Lee. "Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production." MATEMATIKA 34, no. 1 (May 28, 2018): 103–11. http://dx.doi.org/10.11113/matematika.v34.n1.1040.

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Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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Cui, Zhengyan, Junjun Zhang, Giseop Noh, and Hyun Jun Park. "MFDGCN: Multi-Stage Spatio-Temporal Fusion Diffusion Graph Convolutional Network for Traffic Prediction." Applied Sciences 12, no. 5 (March 4, 2022): 2688. http://dx.doi.org/10.3390/app12052688.

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Traffic prediction is a popular research topic in the field of Intelligent Transportation System (ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve road traffic efficiency. Graph neural networks are widely used in traffic prediction because they are good at dealing with complex nonlinear structures. Existing traffic prediction studies use distance-based graphs to represent spatial relationships, which ignores the deep connections between non-adjacent spatio-temporal information. The use of a simple approach to fuse spatio-temporal information is not conducive to obtaining long-term deep spatio-temporal dependencies. Therefore, we propose a new deep learning model Multi-Stage Spatio-Temporal Fusion Diffusion Graph Convolutional Network (MFDGCN). It generates multiple static and dynamic spatio-temporal association graphs to enhance features and adopts the multi-stage hybrid spatio-temporal fusion method. This promotes the effective fusion of a spatio-temporal multimodal and uses the diffuse convolution method to model the graph structure and time series in traffic prediction, respectively. The model can better predict both long and short-term traffic simultaneously. We evaluated MFDGCN using real road network traffic data and it shows good performance.

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Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (November 12, 2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

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Santos-Fernandez, Edgar, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Daniel J. Isaak, and Kerrie Mengersen. "Bayesian spatio-temporal models for stream networks." Computational Statistics & Data Analysis 170 (June 2022): 107446. http://dx.doi.org/10.1016/j.csda.2022.107446.

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Madadgar, Shahrbanou, and Hamid Moradkhani. "Spatio-temporal drought forecasting within Bayesian networks." Journal of Hydrology 512 (May 2014): 134–46. http://dx.doi.org/10.1016/j.jhydrol.2014.02.039.

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Saxena, Divya, and Jiannong Cao. "Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–23. http://dx.doi.org/10.1145/3458025.

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Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.

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Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.

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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.

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Diao, Xiaolei, Xiaoqiang Li, and Chen Huang. "Multi-Term Attention Networks for Skeleton-Based Action Recognition." Applied Sciences 10, no. 15 (July 31, 2020): 5326. http://dx.doi.org/10.3390/app10155326.

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The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

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Zhao, Ping, Zhijie Fan*, Zhiwei Cao, and Xin Li. "Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism." International Journal of Information Security and Privacy 16, no. 1 (January 2022): 1–20. http://dx.doi.org/10.4018/ijisp.290832.

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In order to improve the ability to detect network attacks, traditional intrusion detection models often used convolutional neural networks to encode spatial information or recurrent neural networks to obtain temporal features of the data. Some models combined the two methods to extract spatio-temporal features. However, these approaches used separate models and learned features insufficiently. This paper presented an improved model based on temporal convolutional networks (TCN) and attention mechanism. The causal and dilation convolution can capture the spatio-temporal dependencies of the data. The residual blocks allow the network to transfer information in a cross-layered manner, enabling in-depth network learning. Meanwhile, attention mechanism can enhance the model's attention to the relevant anomalous features of different attacks. Finally, this paper compared models results on the KDD CUP99 and UNSW-NB15 datasets. Besides, the authors apply the model to video surveillance network attack detection scenarios. The result shows that the model has advantages in evaluation metrics.

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Norman, Utku, and A. Ercument Cicek. "ST-Steiner: a spatio-temporal gene discovery algorithm." Bioinformatics 35, no. 18 (February 13, 2019): 3433–40. http://dx.doi.org/10.1093/bioinformatics/btz110.

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AbstractMotivationWhole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.ResultsHere, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.Availability and implementationThe code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.Supplementary informationSupplementary data are available at Bioinformatics online.

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Li, Zheng, Xueyuan Huang, Chun Liu, and Wei Yang. "Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation." ISPRS International Journal of Geo-Information 11, no. 11 (October 29, 2022): 543. http://dx.doi.org/10.3390/ijgi11110543.

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As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.

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Zheng, Hanle, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li. "Going Deeper With Directly-Trained Larger Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11062–70. http://dx.doi.org/10.1609/aaai.v35i12.17320.

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Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient implementation in neuromorphic hardware. However, the unique working mode of SNNs makes them more difficult to train than traditional networks. Currently, there are two main routes to explore the training of deep SNNs with high performance. The first is to convert a pre-trained ANN model to its SNN version, which usually requires a long coding window for convergence and cannot exploit the spatio-temporal features during training for solving temporal tasks. The other is to directly train SNNs in the spatio-temporal domain. But due to the binary spike activity of the firing function and the problem of gradient vanishing or explosion, current methods are restricted to shallow architectures and thereby difficult in harnessing large-scale datasets (e.g. ImageNet). To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed “STBP-tdBN”, enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware. With the proposed method and elaborated shortcut connection, we significantly extend directly-trained SNNs from a shallow structure (

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Li, Hanhui, Xudong Jiang, Boliang Guan, Ruomei Wang, and Nadia Magnenat Thalmann. "Multistage Spatio-Temporal Networks for Robust Sketch Recognition." IEEE Transactions on Image Processing 31 (2022): 2683–94. http://dx.doi.org/10.1109/tip.2022.3160240.

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Bui, Hung H., Svetha Venkatesh, and Geoff West. "Layered dynamic probabilistic networks for spatio-temporal modelling." Intelligent Data Analysis 3, no. 5 (September 1, 1999): 339–61. http://dx.doi.org/10.3233/ida-1999-3503.

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Kim, Jeong-Joon. "Spatio-Temporal Query Processing Operators in Sensor Networks." Journal of Engineering and Applied Sciences 14, no. 12 (December 10, 2019): 4109–15. http://dx.doi.org/10.36478/jeasci.2019.4109.4115.

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Wang, Jian, Yameng Shao, Jianqi Zhu, and Yuming Ge. "Spatio-Temporal Location Privacy Quantification for Vehicular Networks." IEEE Access 6 (2018): 62963–74. http://dx.doi.org/10.1109/access.2018.2877058.

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Ermentrout, Bard. "Neural networks as spatio-temporal pattern-forming systems." Reports on Progress in Physics 61, no. 4 (April 1, 1998): 353–430. http://dx.doi.org/10.1088/0034-4885/61/4/002.

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Clement, L., O. Thas, P. A. Vanrolleghem, and J. P. Ottoy. "Spatio-temporal statistical models for river monitoring networks." Water Science and Technology 53, no. 1 (January 1, 2006): 9–15. http://dx.doi.org/10.2166/wst.2006.002.

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When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

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Stiles, Bryan W., and Joydeep Ghosh. "Habituation based neural networks for spatio-temporal classification." Neurocomputing 15, no. 3-4 (June 1997): 273–307. http://dx.doi.org/10.1016/s0925-2312(97)00010-6.

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Bui, H. "Layered dynamic probabilistic networks for spatio-temporal modelling." Intelligent Data Analysis 3, no. 5 (November 1999): 339–61. http://dx.doi.org/10.1016/s1088-467x(99)00027-x.

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Rempe, Felix, Gerhard Huber, and Klaus Bogenberger. "Spatio-Temporal Congestion Patterns in Urban Traffic Networks." Transportation Research Procedia 15 (2016): 513–24. http://dx.doi.org/10.1016/j.trpro.2016.06.043.

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Sandu Popa, Iulian, Karine Zeitouni, Vincent Oria, and Ahmed Kharrat. "Spatio-temporal compression of trajectories in road networks." GeoInformatica 19, no. 1 (May 3, 2014): 117–45. http://dx.doi.org/10.1007/s10707-014-0208-4.

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Bak, Cagdas, Aysun Kocak, Erkut Erdem, and Aykut Erdem. "Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction." IEEE Transactions on Multimedia 20, no. 7 (July 2018): 1688–98. http://dx.doi.org/10.1109/tmm.2017.2777665.

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Liu, Panbiao, Yong Zhang, Dehui Kong, and Baocai Yin. "Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction." Applied Sciences 9, no. 4 (February 13, 2019): 615. http://dx.doi.org/10.3390/app9040615.

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Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

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Zhang, Qi, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. "Spatio-Temporal Graph Structure Learning for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1177–85. http://dx.doi.org/10.1609/aaai.v34i01.5470.

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As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects. First, traffic data are physically associated with road networks, and thus should be formatted as traffic graphs rather than regular grid-like tensors. Second, traffic data render strong spatial dependence, which implies that the nodes in the traffic graphs usually have complex and dynamic relationships between each other. Third, traffic data demonstrate strong temporal dependence, which is crucial for traffic time series modeling. To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting. Technically, SLC explicitly models the structure information into the convolutional operation. Under this framework, various non-Euclidean CNN methods can be considered as particular instances of our formulation, yielding a flexible mechanism for learning on the graph. Along this technical line, two SLC modules are proposed to capture the global and local structures respectively and they are integrated to construct an end-to-end network for traffic forecasting. Additionally, in this process, Pseudo three Dimensional convolution (P3D) networks are combined with SLC to capture the temporal dependencies in traffic data. Extensively comparative experiments on six real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones.

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Andrienko, Gennady, Donato Malerba, Michael May, and Maguelonne Teisseire. "Mining spatio-temporal data." Journal of Intelligent Information Systems 27, no. 3 (November 2006): 187–90. http://dx.doi.org/10.1007/s10844-006-9949-3.

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Jiang, Haiyang, Yaozong Pan, Jian Zhang, and Haitao Yang. "Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion." Symmetry 11, no. 6 (June 5, 2019): 761. http://dx.doi.org/10.3390/sym11060761.

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In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture—3D convolution Two-Stream model based on multi-scale feature fusion. Extensive experimental results on the simulation data show that our network significantly boosts the efficiency of existing convolutional neural networks in the aggregation behavior recognition, achieving the most advanced performance on the dataset constructed in this paper.

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Yang, Jian, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting." Journal of Advanced Transportation 2022 (October 3, 2022): 1–17. http://dx.doi.org/10.1155/2022/2806183.

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Accurate traffic state prediction plays an important role in traffic guidance, travel planning, etc. Due to the existence of complex spatio-temporal relationships, there are some challenges in forecasting. Firstly, in terms of spatial correlation, some models only consider the road network structure information, and ignore the relative location relationships between nodes. Secondly, some models ignore the different impacts of nodes in the global road network on traffic. To solve these problems, we propose a new traffic state-forecasting model, namely, spatio-temporal attention-gated recurrent neural network (ST-AGRNN). In the proposed model, structure-based and location-based localized spatial features are obtained simultaneously by Graph Convolutional Networks (GCNs) and DeepWalk. The localized temporal features are obtained by gated recurrent unit (GRU). The attention-based approach is used to obtain global spatio-temporal features. Experimental validation is performed with two real-world public datasets, and the results show that the ST-AGRNN model outperforms the state-of-the-art methods.

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Zhang, Lanfang, Zhiyong Zhang, and Ting Zhao. "A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification." International Journal of Cloud Applications and Computing 11, no. 2 (April 2021): 17–31. http://dx.doi.org/10.4018/ijcac.2021040102.

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With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

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Diaz, Juglar, Felipe Bravo-Marquez, and Barbara Poblete. "Language Modeling on Location-Based Social Networks." ISPRS International Journal of Geo-Information 11, no. 2 (February 18, 2022): 147. http://dx.doi.org/10.3390/ijgi11020147.

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The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places.

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Svecic, Andrei, Rihab Mansour, An Tang, and Samuel Kadoury. "Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks." PLOS ONE 16, no. 12 (December 7, 2021): e0259692. http://dx.doi.org/10.1371/journal.pone.0259692.

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Magnetic resonance imaging (MRI) plays a critical role in the planning and monitoring of hepatocellular carcinomas (HCC) treated with locoregional therapies, in order to assess disease progression or recurrence. Dynamic contrast-enhanced (DCE)-MRI sequences offer temporal data on tumor enhancement characteristics which has strong prognostic value. Yet, predicting follow-up DCE-MR images from which tumor enhancement and viability can be measured, before treatment of HCC actually begins, remains an unsolved problem given the complexity of spatial and temporal information. We propose an approach to predict future DCE-MRI examinations following transarterial chemoembolization (TACE) by learning the spatio-temporal features related to HCC response from pre-TACE images. A novel Spatial-Temporal Discriminant Graph Neural Network (STDGNN) based on graph convolutional networks is presented. First, embeddings of viable, equivocal and non-viable HCCs are separated within a joint low-dimensional latent space, which is created using a discriminant neural network representing tumor-specific features. Spatial tumoral features from independent MRI volumes are then extracted with a structural branch, while dynamic features are extracted from the multi-phase sequence with a separate temporal branch. The model extracts spatio-temporal features by a joint minimization of the network branches. At testing, a pre-TACE diagnostic DCE-MRI is embedded on the discriminant spatio-temporal latent space, which is then translated to the follow-up domain space, thus allowing to predict the post-TACE DCE-MRI describing HCC treatment response. A dataset of 366 HCC’s from liver cancer patients was used to train and test the model using DCE-MRI examinations with associated pathological outcomes, with the spatio-temporal framework yielding 93.5% classification accuracy in response identification, and generating follow-up images yielding insignificant differences in perfusion parameters compared to ground-truth post-TACE examinations.

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Grundmann, Jens, Sebastian Hörning, and András Bárdossy. "Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling." Hydrology and Earth System Sciences 23, no. 1 (January 16, 2019): 225–37. http://dx.doi.org/10.5194/hess-23-225-2019.

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Abstract. Knowledge of spatio-temporal rainfall patterns is required as input for distributed hydrologic models used for tasks such as flood runoff estimation and modelling. Normally, these patterns are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic, small-scale rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties arise in distributed rainfall–runoff modelling if poorly identified spatio-temporal rainfall patterns are used, since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves is underestimated. To address this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall patterns. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall–runoff model in a Monte Carlo framework. The simulated spatio-temporal rainfall patterns are conditioned on point rainfall data from ground-based monitoring networks and the observed hydrograph at the catchment outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several candidates for spatio-temporal rainfall patterns are feasible and allow for an analysis of their uncertainty. The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolution of 1 km2 for a catchment partly covered by rainfall. A set of plausible spatio-temporal rainfall patterns can be obtained by applying this inverse approach. Furthermore, results of a real-world study for a flash flood event in a mountainous arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.

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San Emeterio de la Parte, Mario, Sara Lana Serrano, Marta Muriel Elduayen, and José-Fernán Martínez-Ortega. "Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks." Agriculture 13, no. 2 (February 1, 2023): 360. http://dx.doi.org/10.3390/agriculture13020360.

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In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

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Skatchkovsky, Nicolas, Hyeryung Jang, and Osvaldo Simeone. "Spiking Neural Networks—Part II: Detecting Spatio-Temporal Patterns." IEEE Communications Letters 25, no. 6 (June 2021): 1741–45. http://dx.doi.org/10.1109/lcomm.2021.3050242.

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GUO, Long-Jiang. "Spatio-Temporal Query Processing Method in Wireless Sensor Networks." Journal of Software 17, no. 4 (2006): 794. http://dx.doi.org/10.1360/jos170794.

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Ganesan, Deepak, Sylvia Ratnasamy, Hanbiao Wang, and Deborah Estrin. "Coping with irregular spatio-temporal sampling in sensor networks." ACM SIGCOMM Computer Communication Review 34, no. 1 (January 2004): 125–30. http://dx.doi.org/10.1145/972374.972396.

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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 (February 10, 2020): e0228880. http://dx.doi.org/10.1371/journal.pone.0228880.

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Peer, Mansi, Vivek Ashok Bohara, and Anand Srivastava. "Real-World Spatio–Temporal Behavior Aware D2D Multicast Networks." IEEE Transactions on Network Science and Engineering 7, no. 3 (July 1, 2020): 1675–86. http://dx.doi.org/10.1109/tnse.2019.2947700.

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Nogueira, Keiller, Jefersson A. dos Santos, Nathalia Menini, Thiago S. F. Silva, Leonor Patricia C. Morellato, and Ricardo da S. Torres. "Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks." IEEE Geoscience and Remote Sensing Letters 16, no. 10 (October 2019): 1665–69. http://dx.doi.org/10.1109/lgrs.2019.2903194.

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Khatami, Roohallah, and Masood Parvania. "Spatio-Temporal Value of Energy Storage in Transmission Networks." IEEE Systems Journal 14, no. 3 (September 2020): 3855–64. http://dx.doi.org/10.1109/jsyst.2019.2956541.

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Journal articles on the topic 'Spatio temporal networks'

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