Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previo...
In the era of big data, a significant volume of spatiotemporal data exists in a multiscale format, describing diverse phenomena in the objective world across different spatial and temporal scales. While existing methods focus on analyzing the feature...
Many scientific fields show great interest in the extraction and processing of spatiotemporal data, such as medicine with an emphasis on epidemiology and neurology, geology, social sciences, meteorology, and a great interest is also observed in the s...
The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able t...
With the emergence of a large number of fuzzy spatiotemporal data on the Web, how to represent and operate fuzzy spatiotemporal data has become an important research issue. Meanwhile, the Resource Description Framework (RDF) is a standard data and kn...
With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS...
The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dyna...
Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there...
Crowdsensing data serves as a crucial resource for supporting spatiotemporal applications and services. However, its inherent heterogeneity and quality uncertainty present significant challenges for data usability assessment: the evaluation methods a...
In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the s...
Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially...
Spatiotemporal scale is a basic component of geographical problems because the size of spatiotemporal units may have a significant impact on the aggregation of spatial data and the corresponding analysis results. However, there is no clear standard f...
The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the Euro...
An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly import...
Visual analysis provides a powerful approach for exploring multidimensional spatiotemporal data, yet it faces significant challenges in simultaneously representing temporal and spatial attributes. A key limitation is the difficulty of visually encodi...
The increase in traffic in cities world-wide has led to a need for better traffic management systems in urban networks. Despite the advances in technology for traffic data collection, the collected data are still suffering from significant issues, su...
With the popularity of spatiotemporal big data applications, more and more sensitive data are generated by users, and the sharing and secure storage of spatiotemporal big data are faced with many challenges. In response to these challenges, the prese...
Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resou...
With the rapid development of smart water distribution systems, real-time monitoring data from large-scale sensor networks plays a critical role in system optimization and failure prediction. However, sensor data quality is often compromised by fault...
Spatiotemporal models for count data are required in a wide range of scientific fields, and they have become particularly crucial today because of their ability to analyze COVID-19-related data. The main objective of this paper is to present a review...
An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively wi...
Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common w...
In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel...
The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the m...
To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. T...
In a wireless sensor network, sensors collect data about natural phenomena and transmit them to a server in real-time. Many studies have been conducted focusing on the processing of continuous queries in an approximate form. However, this approach is...
With the development of high-performance computers, cloud storage, and advanced sensors, people’s ability to gather complex learning data has greatly improved. However, analyzing these data remains a significant challenge. Especially for spatio...
Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still no...
Analyzing and mining spatiotemporal processes refers to the extraction of geographic phenomena from spatiotemporal data and the analysis of available geographic knowledge and patterns. It finds applications in various fields such as natural disaster...
The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban c...
The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorith...
High-dimensional meteorological data offer a comprehensive overview of meteorological conditions. Nevertheless, predicting regional high-dimensional meteorological data poses challenges due to the vast scale and rapid changes. Apart from slow convent...
This study presents a robust framework designed to address the limitations of passive infrared (PIR) sensors in home-based elderly monitoring, particularly focusing on false detections and sensor blind times, which compromise data accuracy. While PIR...
Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typica...
This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to faci...
One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential...
Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale sp...
Taxis are significant contributors to carbon dioxide emissions due to their frequent usage, yet current research into taxi carbon emissions is insufficient. Emerging data sources and big data–mining techniques enable analysis of carbon emissions, whi...
Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensificat...
21 October 2020
The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have been analyz...
Accurate travel time prediction is essential for optimizing transportation systems and alleviating congestion in urban environments. This study introduces a comprehensive framework that combines advanced spatiotemporal data refinement techniques with...
Air pollution is an important problem for public health. The spatiotemporal analysis is a crucial step for understanding the complex characteristics of air pollution. Using many sensors and high-resolution time-step observations makes this task a big...
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments...
High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spat...
Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single sate...
Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the ac...
With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted...
Outdoor fire detection faces significant challenges due to complex and variable environmental conditions. Fiber Optic Distributed Temperature Sensing (FO-DTS), recognized for its high sensitivity and broad monitoring range, provides significant advan...
The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and e...
The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully...
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