Article
Computer Science, Artificial Intelligence
Mohd Yousuf Ansari, Mainuddin, Amir Ahmad, Gopal Bhushan
Summary: Spatial technologies generate large datasets quickly and continuously. The study developed a clustering algorithm to mine spatiotemporal co-location events in trajectory datasets, dividing trajectories into line segments and grouping them based on spatial and temporal aspects. Entropy and silhouette index were adopted to validate clusters, with experiments showing the algorithm effectively discovering optimal clusters. The algorithm outperformed other existing algorithms in discovering hidden and useful clusters.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Abid Mehmood, Iynkaran Natgunanathan, Yong Xiang
Summary: This paper discusses the issue of clustering spatiotemporal data on public Cloud using a MapReduce-based framework. A privacy preserving clustering algorithm based on MapReduce is proposed to address the problem of privacy concerns. Extensive experimental evaluation demonstrates that the proposed scheme efficiently produces higher quality clustering results.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Green & Sustainable Science & Technology
Richard J. Buning, Vijay Lulla
Summary: The study found differential usage patterns between visitors and local residents regarding bikeshare, with visitors primarily using it for leisure urban exploration and residents mainly using it for public transportation-related trips.
JOURNAL OF SUSTAINABLE TOURISM
(2021)
Article
Computer Science, Interdisciplinary Applications
Shishuo Xu, Songnian Li, Wei Huang, Richard Wen
Summary: This study utilizes geosocial media data, association rules, and a clustering-based method to achieve efficient traffic event detection, improving road safety and traffic management. Compared to traditional keyword-based query and text classification methods, this approach outperforms in correctly detecting traffic events.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2022)
Article
Virology
Ilaria Spassiani, Giovanni Sebastiani, Giorgio Palu
Summary: The study proposes a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data, using mathematical and statistical tools for in-depth analysis. Applying this methodology to a dataset of around 19,000 COVID-19 patients in the Veneto region, it identifies interesting patterns and clusters based on the temporal evolution of incidence, which could contribute to informing strategic decisions for epidemic control.
Article
Engineering, Biomedical
Tamara Dupuy, Clement Beitone, Jocelyne Troccaz, Sandrine Voros
Summary: This study proposes a rigid 2D/3D deep registration method to improve the accuracy of prostate cancer biopsy targeting through enhanced navigation. The proposed model achieves better performance by combining local spatial information with temporal information compared to more complex spatiotemporal combinations.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Runda Guan, Ziyu Wang, Xiaokang Pan, Rongjie Zhu, Biao Song, Xinchang Zhang
Summary: In this paper, a similarity-based minimum bounding rectangle (SbMBR) tree method is proposed for indexing and compressing spatiotemporal data in the field of data analysis and mining. It achieves data compression with multi-layer loss control by hierarchically selecting minimum bounding rectangles and replacing original data. Experimental results demonstrate the superiority of this method in preserving data quality over some typical indexing and compression algorithms.
APPLIED SCIENCES-BASEL
(2023)
Article
Green & Sustainable Science & Technology
Mu Lin, Zhengdong Huang, Tianhong Zhao, Ying Zhang, Heyi Wei
Summary: This paper explores the spatiotemporal evolution of travel patterns in Shenzhen using smart card data from 2011 to 2017. Cluster analysis reveals seven typical travel patterns, with the proportion of regular commuting passengers increasing over time. Additionally, spatial variations and annual migration of travel patterns are examined to identify characteristics in different periods.
Review
Water Resources
Saber Moazami, Wooyoung Na, Mohammad Reza Najafi, Camila de Souza
Summary: A novel approach combining regression-based quantile mapping, spatial interpolation, and clustering algorithm is proposed to adjust the spatial and temporal biases of precipitation measurements in Canada. The method shows promising results in reducing bias and improving correlation.
ADVANCES IN WATER RESOURCES
(2022)
Article
Agriculture, Multidisciplinary
Beatrice Adoyo, Urs Schaffner, Stellah Mukhovi, Boniface Kiteme, Purity Rima Mbaabu, Sandra Eckert, Simon Choge, Albrecht Ehrensperger
Summary: This study assessed the spatiotemporal trajectories and determinants of Prosopis cover in Baringo County, Kenya. The results revealed that most plots were only temporarily managed or not managed at all, while continuous management of Prosopis occurred mainly near rivers and on plots suitable for cultivation. It is important to clear Prosopis near roads, which are dispersal pathways for Prosopis seeds.
JOURNAL OF LAND USE SCIENCE
(2022)
Article
Biodiversity Conservation
Mo Wang, Furong Chen, Dongqing Zhang, Zijing Chen, Jin Su, Shiqi Zhou, Jianjun Li, Jintang Chen, Jiaying Li, Soon Keat Tan
Summary: Driven by fast urbanization, intense land use and land cover (LULC) changes have resulted in urban flooding becoming the most frequent and influential hazards. However, there is a lack of studies integrating LULC changes into urban development scenarios and flooding vulnerability assessment. This study proposed a robust modeling chain to predict temporal and spatial changes in urban flooding vulnerability using Maximum Entropy, System Dynamics, Patch-generating Land Use Simulation, and shared socio-economic pathways (SSPs). The results showed that the expansion of construction land has largely caused an increase in urban flooding, with substantial differences observed among the scenarios, and SSP585 having the highest flooding vulnerability.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Studies
Wancong Li, Hong Li, Shijun Wang, Zhiqiang Feng
Summary: This study examined shrinking cities in northeast China and found that while the total population continues to decline, the total construction area is still growing, and various types of construction land in most shrinking units are not decreasing with the loss of population. The majority of new construction land comes from cropland, and the growth rate of new construction land has slowed down compared to the previous decade. Furthermore, the expansion of newly added construction land is mainly in the form of sprawling expansion, even in shrinking areas.
Article
Transportation
Kaitai Yang, Hanyi Yang, Lili Du
Summary: This paper proposes a data-driven approach that integrates machine learning and traffic features to detect traffic shockwaves and estimate their propagation speeds. By processing partial vehicle trajectory data, this method efficiently detects shockwaves without the need for estimating nearby traffic density and flow.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Xinyi Liu, Meiliu Wu, Bo Peng, Qunying Huang
Summary: This paper proposes a graph-based method for classifying and identifying individual daily travel activities using spatiotemporal trajectories and point-of-interest data. The method reduces feature engineering efforts and improves model generalizability and identification accuracy by learning feature embeddings.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Civil
Kyoungok Kim
Summary: Determining bike-sharing usage patterns and explanatory factors is crucial for the effective operation of bike-sharing systems. This study explores the differences in usage patterns depending on the type of pass and the impact of explanatory factors on demand. Various machine learning techniques and statistical analysis are used to analyze the data.
