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Spatial–Temporal Data Analysis and Its Applications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14416

Special Issue Editors


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Guest Editor
Department of Statistical Modeling, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa-shi, Tokyo 190-8562, Japan
Interests: statistical machine learning; spatial and temporal modelling; speech and image processing; environmental data analysis; epidemiological data analysis

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Guest Editor
Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW 2109, Australia
Interests: modelling extreme events; dependence modelling; state–space models; Monte Carlo methods; optimal stochastic control; machine learning methods
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: financial risk management and insurance; actuarial machine learning methodology; time series and state-space modelling; spatial statistics; stochastic processes in financial applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Brittany Atlantique Mathematics Laboratory, Campus of Vannes,University of Southern Brittany, 56100 Lorient, France
Interests: Bayesian statistics; Monte Carlo methods (MCMC, SMC); spatial–temporal modelling and inference; inverse problems

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to bring together an interdisciplinary selection of works that relate to the topics of spatial and temporal modelling with a methodology and application focus. The interface of spatial modelling and time-series analysis has emerged as an important field of research on the boundary of many disciplines, including computational statistics; machine learning and big data analytics; signal processing; environmental science; risk and insurance analytics in actuarial practice disciplines, such as catastrophe modelling, green finance, and demographic statistics; agricultural science; and network science, to list a few.

However, to date, these disciplines have largely formed somewhat independent views on best practices to develop methodology and implement practical solutions to spatial–temporal problems that arise in each of these disciplines. As research becomes increasingly interdisciplinary, the Guest Editors of this Special Issue see an opportunity to encourage a multidisciplinary selection of papers on topics in spatial–temporal analysis that provide perspectives on emerging trends and problems in these disciplines to learn and leverage experiences across these communities.

We encourage submissions that focus on but are not limited to one of the following sub-categories:

  • Recent methodological advances in spatial–temporal modelling;
  • Computational solutions to large scale estimation and simulation in big data spatial–temporal settings;
  • Application topics in global warming and environmental modelling;
  • Spatial–temporal risk modeling for decision making under uncertainty, which could include catastrophe and insurance applications, environmental–economic modelling, stress-testing, and scenario analysis models for integrated climate economic models;
  • Spatial–temporal demographic statistics for population planning and analysis with applications including, but not limited to, retirement planning, pensions, and aged care;
  • Spatial–temporal epidemiological solutions, especially with a focus on epidemics such as COVID-19 data analysis;
  • In particular, analysis and interpretation with the help of statistical tools based on entropy and information theory are included in this Special Issue.

Prof. Dr. Tomoko Matsui
Prof. Dr. Pavel Shevchenko
Prof. Dr. Gareth W. Peters
Prof. Dr. Francois Septier
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial and temporal modelling
  • statistical machine learning
  • environmental data analysis
  • epidemiological data analysis
  • natural language processing
  • speech and image processing

Published Papers (8 papers)

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Research

16 pages, 2914 KiB  
Article
Graph Regression Model for Spatial and Temporal Environmental Data—Case of Carbon Dioxide Emissions in the United States
by Roméo Tayewo, François Septier, Ido Nevat and Gareth W. Peters
Entropy 2023, 25(9), 1272; https://doi.org/10.3390/e25091272 - 29 Aug 2023
Viewed by 862
Abstract
We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more [...] Read more.
We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general regression relationships than classical linear ones through the use of generalized linear models (GLMs) and also captures the inherent structural dependencies or relationships of the data through this regularization based on the graph Laplacian. We use a publicly available dataset from the National Centers for Environmental Information (NCEI) in the United States of America and perform statistical inferences of future CO2 emissions in 59 counties. We empirically show how the proposed method outperforms widely used methods, such as the ordinary least squares (OLS) and ridge regression for this challenging problem. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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14 pages, 10278 KiB  
Article
Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)
by Zhigang Li, Ting Sun, Yu Wang, Yujie Liu and Xiaochuan Sun
Entropy 2023, 25(4), 689; https://doi.org/10.3390/e25040689 - 19 Apr 2023
Cited by 1 | Viewed by 1029
Abstract
In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly [...] Read more.
In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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22 pages, 759 KiB  
Article
Robust Variable Selection with Exponential Squared Loss for the Spatial Single-Index Varying-Coefficient Model
by Yezi Wang, Zhijian Wang and Yunquan Song
Entropy 2023, 25(2), 230; https://doi.org/10.3390/e25020230 - 26 Jan 2023
Viewed by 1168
Abstract
As spatial correlation and heterogeneity often coincide in the data, we propose a spatial single-index varying-coefficient model. For the model, in this paper, a robust variable selection method based on spline estimation and exponential squared loss is offered to estimate parameters and identify [...] Read more.
As spatial correlation and heterogeneity often coincide in the data, we propose a spatial single-index varying-coefficient model. For the model, in this paper, a robust variable selection method based on spline estimation and exponential squared loss is offered to estimate parameters and identify significant variables. We establish the theoretical properties under some regularity conditions. A block coordinate descent (BCD) algorithm with the concave–convex process (CCCP) is composed uniquely for solving algorithms. Simulations show that our methods perform well even though observations are noisy or the estimated spatial mass matrix is inaccurate. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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21 pages, 2608 KiB  
Article
Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data
by Qiqin Cai, Dingrong Yi, Fumin Zou, Zhaoyi Zhou, Nan Li and Feng Guo
Entropy 2022, 24(9), 1208; https://doi.org/10.3390/e24091208 - 29 Aug 2022
Cited by 7 | Viewed by 1481
Abstract
To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) [...] Read more.
To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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19 pages, 8880 KiB  
Article
An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages
by Guoce Feng, Lei Zhang, Feifan Ai, Yirui Zhang and Yupeng Hou
Entropy 2022, 24(8), 1111; https://doi.org/10.3390/e24081111 - 12 Aug 2022
Cited by 4 | Viewed by 2673
Abstract
A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer [...] Read more.
A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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23 pages, 8505 KiB  
Article
Multi-View Travel Time Prediction Based on Electronic Toll Collection Data
by Sijie Luo, Fumin Zou, Cheng Zhang, Junshan Tian, Feng Guo and Lyuchao Liao
Entropy 2022, 24(8), 1050; https://doi.org/10.3390/e24081050 - 30 Jul 2022
Cited by 9 | Viewed by 1466
Abstract
The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide [...] Read more.
The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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31 pages, 1228 KiB  
Article
Spatial Warped Gaussian Processes: Estimation and Efficient Field Reconstruction
by Gareth W. Peters, Ido Nevat, Sai Ganesh Nagarajan and Tomoko Matsui
Entropy 2021, 23(10), 1323; https://doi.org/10.3390/e23101323 - 11 Oct 2021
Cited by 2 | Viewed by 1517
Abstract
A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped [...] Read more.
A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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19 pages, 45026 KiB  
Article
Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets
by Shenghan Zhou, Bang Chen, Houxiang Liu, Xinpeng Ji, Chaofan Wei, Wenbing Chang and Yiyong Xiao
Entropy 2021, 23(10), 1305; https://doi.org/10.3390/e23101305 - 04 Oct 2021
Cited by 7 | Viewed by 2067
Abstract
Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most [...] Read more.
Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets. Full article
(This article belongs to the Special Issue Spatial–Temporal Data Analysis and Its Applications)
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