Spatio-temporal characterization analysis plays a key role in spatio-temporal data mining tasks such as social relationship inference, traffic flow prediction, and spatio-temporal graph node classification. Although traditional numerical simulation m...
Integrating spatio-temporal kriging with machine learning improves estimation accuracy by addressing complex spatial and temporal variations in spatio-temporal phenomena. The improvement can be attributed to the enhanced flexibility of machine learni...
On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evo...
The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, th...
Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift...
In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan stati...
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data ge...
This work presents a new approach that aims to characterize the spatio-temporal relationships that exist between geographical objects that are absent or non-existent at the moment of analysis. First, we would like to propose a formal analysis of the...
In various applications of blockchain, how to index spatio-temporal data more efficiently has become a subject of continuous attention. The existing spatio-temporal data query in the blockchain is realized by adding additional external storage or fix...
The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, w...
In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the...
The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heter...
To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the “curse of dimensionality” problem in the nonparametric method, we improve the estimati...
Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the processes involved, in relation to one or more phe...
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of...
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional metho...
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...
Car-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal...
With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the predicti...
In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of locat...
Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semant...
Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear mode...
The raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as envir...
Traffic flow prediction can provide effective support for traffic management and control and plays an important role in the traffic system. Traffic flow has strong spatio-temporal characteristics, and existing traffic flow prediction models tend to e...
Within the realm of intelligent transportation systems, the precise forecasting of vehicular speed across individual road segments constitutes a fundamental task. This metric serves as a pivotal indicator for evaluating the extent of network congesti...
In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence...
As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently ex...
Temporal knowledge graphs aim to enhance the dynamic and evolutionary representation of knowledge while enabling time-based reasoning. However, the reasoning based on temporal knowledge graphs in real geographic environments suffers from low accuracy...
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 rela...
The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is of...
One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynom...
Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly devel...
With the rapid adoption of wind energy globally, there is a need for accurate short-term forecasting systems to improve the reliability and integration of such energy resources on a large scale. While most spatio-temporal forecasting systems comprise...
In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz [1], spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying...
Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have be...
In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the...
This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-tempora...
Intelligent and information systems in transportation record and accumulate large volumes of raw data on dynamic transportation processes. However, these data are not fully utilized for forecasting, real-time planning, and transportation management....
Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correl...
Unprecedented amounts of spatio-temporal data instigates an urgent need for patterns exploration in it. Clustering analysis is useful in extracting patterns from big data by grouping similar data elements into clusters. Compared with one-way clusteri...
Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis...
Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about central areas characterized by relatively stable growth, while...
The rapid development of electric vehicles (EVs) has brought great challenges to the power grid, so improving the EV load prediction accuracy is crucial to the safe operation of the power grid. Aiming at the problem of insufficient consideration of s...
Route choice is a complex issue in simulating individual behaviors and reproducing collective phenomena during evacuations. A growing concern has been given to the individual cognitive mechanism to investigate how routing decisions are made in specif...
Stochastic spatio-temporal processes are prevalent across domains ranging from the modeling of plasma, turbulence in fluids to the wave function of quantum systems. This letter studies a measure-theoretic description of such systems by describing the...
The randomness and volatility of wind power pose significant challenges for short-term forecasting, requiring the model to capture both temporal dynamics and the spatial correlations among turbines. To address this issue, this paper proposes a Spatio...
Spatial symmetries occur in combination with temporal symmetries in a wide range of physical systems in nature, including time-periodic quantum systems typically described by the Floquet formalism. In this context, groups formed by three-dimensional...
Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly in...
As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document...
Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance betwe...
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