Fitting statistical models to spatiotemporal data requires finding the right balance between imposing smoothness and following the data. In the context of p-splines, we propose a Bayesian framework for choosing the smoothing parameter which allows the construction of fully-automatic data-driven methods for fitting flexible models to spatiotemporal data. A computationally efficient implementation, exploiting the sparsity of the arising design and penalty matrices, is proposed. The findings are illustrated using a simulation and two examples, all concerned with the modelling of contaminants in groundwater, which suggest that the proposed strategy is more stable that competing strategies based on the use of criteria such as GCV and AIC.
Periodic monitoring of groundwater quality at industrial and commercial sites generates large volumes of spatiotemporal concentration data. Data modelling is typically restricted to either the analysis of monotonic trends in individual wells, or independent fitting of spatial concentration distributions (e.g. Kriging) to separate monitoring time periods. Neither of these techniques satisfactorily elucidate the interaction between spatial and temporal components of the data. Potential negative consequences include an incomplete understanding of groundwater plume dynamics, which can lead to the …
Because of its important role in health policy-shaping, population health monitoring (PHM) is considered a fundamental block for public health services. However, traditional public health data collection approaches, such as clinic-visit-based data integration or health surveys, could be very costly and time-consuming. To address this challenge, this paper proposes a cost-effective approach called Compressive Population Health (CPH), where a subset of a given area is selected in terms of regions within the area for data collection in the traditional way, while leveraging inherent spatial correl…
Real-time urban climate monitoring provides useful information that can be utilized to help monitor and adapt to extreme events, including urban heatwaves. Typical approaches to the monitoring of climate data include weather station monitoring and remote sensing. However, climate monitoring stations are very often distributed spatially in a sparse manner, and consequently, this has a significant impact on the ability to reveal exposure risks due to extreme climates at an intra-urban scale. Additionally, traditional remote sensing data sources are typically not received and analyzed in real-tim…
We present a novel approach to adaptive optimal design of groundwater surveys - a methodology for choosing the location of the next monitoring well. Our dual-weighted approach borrows ideas from Bayesian Optimisation and goal-oriented error estimation to propose the next monitoring well, given that some data is already available from existing wells. Our method is distinct from other optimal design strategies in that it does not rely on Fisher Information and it instead directly exploits the posterior uncertainty and the expected solution to a dual (or adjoint) problem to construct an acquisiti…
Bayesian spatiotemporal modelling of political violence and conflict events using discrete-time Hawkes processes
Queensland University of Technology（昆士兰科技大学）；The Walter and Eliza Hall Institute of Medical Research（沃尔特和伊莱萨霍尔医学研究所）；Information Management, International Federation of the Red Cross Red Crescent Societies (IFRC)（红十字会与红新月会国际联合会信息管理）；CNRS, UMR 7534, Université Paris-Dauphine, PSL University（法国国家科学研究中心，UMR 7534，巴黎第九大学，PSL大学）
The statistical quantification of temperature processes for the analysis of urban heat island (UHI) effects and local heat-waves is an increasingly important application domain in smart city dynamic modelling. This leads to the increased importance of real-time heatwave risk management on a fine-grained spatial resolution. This study attempts to analyze and develop new methods for modelling the spatio-temporal behavior of ground temperatures. The developed models consider higher-order stochastic spatial properties such as skewness and kurtosis, which are key components for understanding and de…
Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地质科学学院经济地质学局）；State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences（中国科学院测量与地球物理研究所大地测量与地球动力学国家重点实验室）；Texas Advanced Computing Center, The University of Texas at Austin（德克萨斯大学奥斯汀分校德克萨斯高级计算中心）；Department of Geology and Geophysics, Indian Institute of Technology Kharagpur（印度理工学院卡格普尔分校地质与地球物理系）
Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we …
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to optimize the cooling system used to cool one of the rooms housing three of these large clusters and develop a general good-practice procedure for reducing cooling costs and monitoring other machine rooms. This work focuses on the statistical approach used to quantif…
Spatiotemporal Analysis for Frequency-Magnitude Distribution of Earthquakes Across Mainland China: Comparing Classical and b-positive b-values Across Tectonic Regimes
University of Chinese Academy of Sciences（中国科学院大学）；Harvard University（哈佛大学）
Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimens…
Spatiotemporal dynamics and ecological risk factors of highly pathogenic avian influenza A(H5N1) in Canadian wildlife: A One Health surveillance analysis
Auckland University of Technology（奥克兰理工大学）
本研究通过分析加拿大2022-2026年野生动物H5N1疫情监测数据，揭示了该病毒的时空动态及与检测数量相关的风险因素，采用描述流行病学、空间聚类方法和负二项混合模型进行分析，发现 Eurasian-North American 病毒谱系主导检测，并识别出年份、季节和谱系是关键预测因子。
Teikametrics；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）
We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron (MLP), for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to …
Arsenic (As) and other toxic elements contamination of groundwater in Bangladesh poses a major threat to millions of people on a daily basis. Understanding complex relationships between arsenic and other elements can provide useful insights for mitigating arsenic poisoning in drinking water and requires multivariate modeling of the elements. However, environmental monitoring of such contaminants often involves a substantial proportion of left-censored observations falling below a minimum detection limit (MDL). This problem motivates us to propose a multivariate spatial Bayesian model for left-…
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the root-cause analysis problem as a minimization problem via the proposed inference bas…
Polytechnique Montreal（蒙特利尔综合理工学院）；McGill University（麦吉尔大学）；Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)（校际企业网络、物流与交通研究中心（CIRRELT））
Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values. By integrating low-rank matrix/tensor factorization and vecto…
Spatiotemporal Satellite Image Downscaling with Transfer Encoders and Autoregressive Generative Models
University of Toronto（多伦多大学）；Harvard T.H. Chan School of Public Health（哈佛大学T.H. Chan公共卫生学院）；Harvard Medical School（哈佛医学院）；VA Healthcare System Boston, U.S. Department of Veterans Affairs（美国退伍军人事务部波士顿医疗系统）
Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We developed a weighted spatiotemporal Bayesian model and used it to integrate image based hard coral data collected by professional and citizen scientists, who captured and or classified underwater images. We used the model to predict coral cover across the GBR with estimates of uncertainty; thus filling gaps in space and time where no data exist. Additional data increased the models predictive ability by 43 percent, but did…
Spatio-temporal models for count data are required in a wide range of scientific fields and they have become particularly crucial nowadays because of their ability to analyse COVID-19-related data. Models for count data are needed when the variable of interest take only non-negative integer values and these integers arise from counting occurrences. Several R-packages are currently available to deal with spatiotemporal areal count data. Each package focuses on different models and/or statistical methodologies. Unfortunately, the results generated by these models are rarely comparable due to dif…
Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of o…
A Spatiotemporal, Quasi-experimental Causal Inference Approach to Characterize the Effects of Global Plastic Waste Export and Burning on Air Quality Using Remotely Sensed Data
University of Colorado Boulder（科罗拉多大学博尔德分校）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）
