Spatial-temporal Data Research Articles

A Review on the Current Trends in Crop Yield Forecasting Using Artificial Intelligence

In the face of escalating global food demands and the increasing unpredictability of climate conditions, the importance of precise crop yield forecasting has never been more critical. This paper provides a comprehensive review of the current trends in leveraging Artificial Intelligence (AI) to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security. Our review covers a range of AI methodologies, including machine learning, deep learning, and hybrid models, that have been employed to predict crop yields with increasing accuracy. Recent advancements have demonstrated that machine learning techniques, such as support vector machines and random forests, are effective in modeling complex agricultural data sets with a notable degree of precision. However, deep learning approaches, including convolutional and recurrent neural networks, have started to outperform traditional machine learning models, owing to their ability to process large-scale spatial-temporal data from remote sensing and IoT-based agricultural sensors. We also explore the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies, providing enhanced accuracy and robustness in yield prediction under varying climatic conditions. Additionally, this review discusses the integration of AI with geographic information systems (GIS) and remote sensing technologies, which has significantly improved the spatial resolution of yield predictions. We highlight several key challenges that remain, such as data scarcity, the need for model generalization, and the integration of socioeconomic factors into yield prediction models. In conclusion, AI presents transformative potential for crop yield forecasting. By harnessing cutting-edge AI technologies and addressing existing challenges, significant strides can be made towards more sustainable and efficient agricultural practices. This paper aims to inspire continued research and innovation in this critical field.

Towards spatio-temporal prediction of cavitating fluid flow with graph neural networks

In this article, we present a deep learning-based surrogate model for spatio-temporal prediction of cavitating fluid flow. Specifically, we introduce a finite element-inspired rotation equivariant hypergraph neural network for inferring and predicting dynamical behaviors of cavitating flow. We generate ground-truth spatial–temporal data by simulating a full-order variational system based on homogeneous mixture-based cavitation theory. We consider the flow past a NACA0012 hydrofoil to examine the predictive ability of the proposed graph neural network for cavitation dynamics. Results demonstrate that the network achieves stabilized and accurate temporal predictions of the system states, successfully forecasting the evolution patterns of individual cavitation events. Additionally, comparisons of predicted fluid loading coefficients are in good agreements with the ground-truth values. We also discuss some challenges encountered in the long-term prediction of flow patterns across multiple cavitation events. The proposed framework has implications for design optimization, active control and development of a physics-based digital twin of a cavitating marine propeller.

Spatial–temporal combination and multi-head flow-attention network for traffic flow prediction

Traffic flow prediction based on spatial–temporal data plays a vital role in traffic management. However, it still faces serious challenges due to the complex spatial–temporal correlation in nonlinear spatial–temporal data. Some previous methods have limited ability to capture spatial–temporal correlation, and ignore the quadratic complexity problem in the traditional attention mechanism. To this end, we propose a novel spatial–temporal combination and multi-head flow-attention network (STCMFA) to model the spatial–temporal correlation in road networks. Firstly, we design a temporal sequence multi-head flow attention (TS-MFA), in which the unique source competition mechanism and sink allocation mechanism make the model avoid attention degradation without being affected by inductive biases. Secondly, we use GRU instead of the linear layer in traditional attention to map the input sequence, which further enhances the temporal modeling ability of the model. Finally, we combine the GCN with the TS-MFA module to capture the spatial–temporal correlation, and introduce residual mechanism and feature aggregation strategy to further improve the performance of STCMFA. Extensive experiments on four real-world traffic datasets show that our model has excellent performance and is always significantly better than other baselines.

An analysis of agglomeration structure for Beijing, Tianjin, and Hebei based on spatial-temporal big data

The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area’s inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Spatial-Temporal Graph Deviation Neural Networks for Anomaly Detection in Time Series

Abstract Anomaly detection in time series data (e.g., sensor data) is becoming a fundamental research problem that has various applications. Due to the complex inter-sensor relationships, it is challenging to detect anomalous events such as system faults and attacks hidden the high-dimensional time series. Recent advancements in deep learning approaches such as Graph Neural Networks (GNN) have greatly improved anomaly detection performance in time series data. However, existing methods usually use separate components to capture spatial and temporal dependence relationship and ignore the heterogeneities in spatial-temporal data which motivates us to capture them together. In this paper, we propose a novel approach STGDNN (short for Spatial-Temporal Graph Deviation Neural Networks) that learns the spatial-temporal dependence relationship together in structure learning of graph neural networks. In addition, we use the learned spatial-temporal graph structure and attention weights to explain the detected anomalies. The experiments on three real world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation compared with state-of-the-art methods.

Preface

This special issue on Advances in Ecosystem-Scale Coral Reef Visual Surveys focuses on large-scale monitoring efforts that inform ecological and management topics that are highly relevant and timely as coral reefs worldwide continue to decline. For the past two decades, probability surveys have been implemented by federal, state, territorial, county, and academic partners. The probability surveys’ randomized design, large spatial scale, consistent methodology, participant expertise, and consistent temporal data collection have resulted in high quality, geographically comprehensive coral reef ecosystem data in the US Atlantic Ocean and Caribbean Sea. More recent advances in these long-term, periodic jurisdictional assessments have broad applicability to other coral reef jurisdictions looking to implement ecosystem-scale surveys. In particular, this special issue captures the many advances in ecosystem-scale coral reef visual surveys as it relates to the following themes: (1) Survey statistical design; (2) Reef fish, coral, and motile invertebrate ecology; and, (3) Ecosystem and fisheries management.Collectively, the ten visual census publications herein underscore the importance of these data to assess the present status and trends of the coral reef communities and specific assemblages (e.g., fishery-targeted species, benthic communities; Grove et al. 2024a, Viehman et al. 2024), habitat use (i.e., by species, life stage, guilds; Heidmann et al. 2024, Herbig et al. 2024), responses to anthropogenic stressors (Aeby et al. 2024) and episodic events (e.g., hurricanes; Langwiser et al. 2024), and to inform the fisheries management process (e.g., reliable fishery-independent indices; Ault et al. 2024) and support management actions (e.g., no-take marine reserves, size and bag limits; Keller et al. 2024). Notable advances in these long-term, large-scale surveys include an expansion to a new region (i.e., mesophotic reefs; Grove et al. 2024b, Heidmann et al. 2024) and to a new species (i.e., spiny lobster, Richter and Feeley 2024) in the US Caribbean. In addition, multiple publications use visual census data for new applications such as analyses (e.g., comparing uncalibrated survey indices, Langwiser et al. 2024), ecosystem status reports (Viehman et al. 2024, Grove et al. 2024a) and to inform the efficacy of place-based investments (i.e., restoration, Herbig et al. 2024), a current focus of coral reef conservation.As the breadth of research on these three visual census themes continues to expand in the US Atlantic and Caribbean, we hope to include related, subsequent publications as a second section to this special issue.

TAU: Trajectory Data Augmentation with Uncertainty for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has been proven effective at utilizing sparse, intricate spatial-temporal trajectory data to recommend subsequent POIs to users. While existing methods commonly alleviate the problem of data sparsity by integrating spatial-temporal context information, POI category features, and social relationships, they largely overlook the fact that the trajectory sequences collected in the datasets are often incomplete. This oversight limits the model’s potential to fully leverage historical context. In light of this background, we propose Trajectory Data Augmentation with Uncertainty (TAU) for Next POI Recommendation. TAU is a general graph-based trajectory data augmentation method designed to complete user mobility patterns by marrying uncertainty estimation into the next POI recommendation task. More precisely, TAU taps into the global transition pattern graph to identify sets of intermediate nodes located between every pair of locations, effectively leveraging edge weights as transition probabilities. During trajectory sequence construction, TAU selectively prompts intermediate nodes, chosen based on their likelihood of occurrence as pseudo-labels, to establish comprehensive trajectory sequences. Furthermore, to gauge the certainty and impact of pseudo-labels on the target location, we introduce a novel confidence-aware calibration strategy using evidence deep learning (EDL) for improved performance and reliability. The experimental results clearly indicate that our TAU method achieves consistent performance improvements over existing techniques across two real-world datasets, verifying its effectiveness as the state-of-the-art approach to the task.

Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).

Machine Learning Approaches in Spatial Data Mining

This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.

A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture

AbstractFloating car data (FCD), characterized by wide spatiotemporal coverage, low collection cost, and immunity to adverse weather conditions, are one of the key approaches for intelligent transportation systems to obtain real‐time urban road network traffic information. The research aims to utilize GPS data from taxis in Shanghai and vector geographic information data of the road network, with urban expressways as the research focus. Based on the different driving characteristics of expressways and the vehicles on the ramps below, a clustering analysis is employed to determine all floating vehicles traveling on the target road. Furthermore, an adaptive buffer zone consistent with the road orientation is established based on road vector geographic data. This allows for the extraction of FCD within segmented areas, and the average vehicle speed for that road segment is obtained through weighted calculations. This method fully exploits the natural characteristics of taxis in urban areas with a wide spatiotemporal distribution. The data effectiveness and coverage reach 90.2% and 85.7%, respectively, significantly surpassing the traditional grid‐based extraction method for FCD. Additionally, to capture the long‐term spatiotemporal dependencies of road network traffic states, a spatial–temporal autoformer (STAF) network based on spatial–temporal sequence autocorrelation is employed for traffic state prediction. The results indicate that the STAF method demonstrates good performance in medium‐ and long‐term prediction. We believe that the proposed FCD mining method in this paper provides a new approach for efficiently extracting large‐scale road network traffic states and conducting medium‐ to long‐term predictions.

A Neural Network Approach to Forest Fire Burned Area Prediction

Worldwide ecosystems and economies are seriously threatened by forest fires, which can result in extensive damage and financial losses. Strategies for managing and preventing wildfires depend heavily on the accurate prediction of burned areas. To create a predictive model for the burned areas caused by forest fires in Portugal's northeast, this study uses a machine learning technique, namely Neural Network. The model integrates meteorological and spatial-temporal data and makes use of regression algorithms. To maximize the performance of the model, data preprocessing, exploratory data analysis, model building, feature selection, and assessment are done. The results show that burned area is greatly influenced by meteorological factors, including temperature, humidity, and wind speed. The created model shows a respectable level of prediction accuracy of 97.11%, offering insightful information for efficient fire management and early warning systems. The potential for reducing the destructive effects of wildfires is enormous when machine learning is incorporated into the prediction of forest fires.

Research on Emotion Classification Based on Multi-modal Fusion

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of the three-dimensional dynamic expansion is established based on the common multi-modal data, for example video , sound ,text.Based on the framework, a multi-modal fusion-matched framework based on spatial and temporal feature enhancement, respectively to solve the dynamic correlation within and between modes, and then model the short and long term dynamic correlation information between different modes based on the proposed framework. Multiple group experiments performed on MOSI datasets show that the emotion recognition model constructed based on the framework proposed here in this paper can better utilize the more complex complementary information between different modal data. Compared with other multi-modal data fusion models, the spatial-temporal attention-based multimodal data fusion framework proposed in this paper significantly improves the emotion recognition rate and accuracy when applied to multi-modal emotion analysis, so it is more feasible and effective.

Imputed quantile vector autoregressive model for multivariate spatial–temporal data

AbstractImputing missing values in multivariate spatial–temporal data is important in many fields. Existing low rank tensor learning methods are popular for handling this task but are sensitive to high level of skewness. The aim of this paper is to develop an alternative method with robustness and high imputation accuracy for multivariate spatial–temporal data. In view of the fact that quantile regression is robust to noises and outliers, we propose an imputed quantile vector autoregressive (IQVAR) model. IQVAR can simultaneously impute missing values and estimate parameters of quantile vector autoregressive model. The objective function includes check loss and nuclear norm penalization. We develop an ADMM (Alternating Direction Method of Multipliers) algorithm to solve the resulting optimization problem. Simulation studies and real data analysis are conducted to verify the efficiency of IQVAR. Compared with other approaches, IQVAR is more robust and accurate.

Shield Tunnel (Segment) Uplift Prediction and Control Based on Interpretable Machine Learning

Shield tunnel segment uplift is a common phenomenon in construction. Excessive and unstable uplift will affect tunnel quality and safety seriously, shorten the tunnel life, and is not conducive to the sustainable management of the tunnel’s entire life cycle. However, segment uplift is affected by many factors, and it is challenging to predict the uplift amount and determine its cause accurately. Existing research mainly focuses on analyzing uplift factors and the uplift trend features for specific projects, which is difficult to apply to actual projects directly. This paper sorts out the influencing factors of segment uplift and designs a spatial-temporal data fusion mechanism for prediction. On this basis, we extract the key influencing factors of segment uplift, construct a prediction model of segment uplift amount based on Extreme Gradient Boosting (XGBoost) v2.0.3, and use SHapley Additive exPlanation (SHAP) v0.44.0 to locate factors affecting uplift, forming an Auxiliary Decision-making System for Segment Uplift Control (ADS-SUC). An ADS-SUC not only detects the sudden change of the segment uplift successfully and predicts the segment uplift in practical engineering accurately, it also provides a feasible method to control the uplift in time, which is of great significance for reducing the construction risk of the tunnel project and ensuring the quality of the completed tunnel.

Application of Spatial Temporal Graph Neural Network in Analyzing the Distribution of Goods Shipping with Dominating Set Technique

A logistics service company may face capacity issues due to distribution delays, resulting in goods accumulating in branch offices with unknown locations. To resolve this problem, we will implement the Spatial-Temporal Graph Neural Network (STGNN) combined with the dominating set technique to predict these branch office locations. The STGNN utilizes graph theory to represent relationships between branch offices in Indonesia. Simulation data on goods shipments across Indonesia are observed for 30 days, categorized as spatial-temporal data, and fed into the STGNN. This process involves three stages: node embeddings, training, and testing/forecasting. We implement some Artificial Neural Network (ANN) models with various hidden layer architectures. The results show that the best model of ANN is cascade forward metwork and the MSE 1,6714×〖10〗^(-9).

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. This paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities Data

MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities Data

Empirical Distribution of Glottal Edges (EDGE): A Statistical Assessment of Vocal Fold Kinematics Using High-Speed Videoendoscopy.

Although laryngeal high-speed videoendoscopy (HSV) is crucial for studying vocal fold vibrations, its translation to clinical practice has been hindered by the large volume of data it produces and the difficulty in interpreting current analysis methods. Although image processing techniques have been developed to map spatial-temporal data into two-dimensional representations, they alter the geometrical construction of the glottis and do not provide standard quantitative features, thus challenging clinical interpretation. In response, we propose a new visualization and analysis framework for assessing the dynamics of vocal folds based on the empirical distribution of the glottal edge using HSV. This procedure analyzes vocal fold oscillations by preserving the shape of the glottis and quantifying the asymmetry between right and left vocal fold displacements along the anterior-posterior axis. This method was evaluated on four groups of participants: ten with normal voices, ten with vocal fold nodules, ten with muscle tension dysphonia, and two with unilateral vocal fold paralysis. The proposed method produces distinct representations for normal and pathological vocal fold vibratory behaviors and derived features based on amplitude and phase asymmetry metrics that show statistically significant differences between normal and pathological groups. Comparative analysis with state-of-the-art techniques indicates that our proposed method can complement the assessment of vocal fold vibration and enhance the clinical translation of HSV.

Bicycle Sharing Demand Analysis and Operation Community Division based on Riding Spatial-Temporal Data -Taking Beijing as an Example

Under the background of vigorously building sustainable cities, shared bicycles have developed rapidly, and some operation and management problems have gradually become prominent. In recent years, the research based on spatial-temporal big data of shared bicycles mostly focuses on the analysis of hotspots in time and space, and the current management of shared bicycle is mostly divided into operating communities by administrative divisions. The construction of bicycle operation management is not carried out in combination with the characteristics of spatial-temporal data and actual needs. Based on the shared bicycle order data in Beijing, this paper analyzes the basic spatial and temporal characteristics of shared bicycles, and uses geographically weighted regression model to analyze the impact of various types of built environments on the demand distribution for shared bicycles. Finally using Fast Unfolding algorithm to identify communities with close demand for shared bicycles. The study finds that the overall utilization rate of bicycle resources is currently low, and various types of POIs have different ranges and degrees of impact on parking demand, as well as the division of bicycle communities does not fully coincide with administrative divisions. This study helps to understand the overall demand and impact characteristics of shared bicycles, provides a basis for the development of differentiated strategies for the delivery, management, and scheduling of shared bicycles, and helps urban managers to optimize the design of the shared bicycle system.