Call for Papers: Spatiotemporal Data Science: Theoretical Advances and Applications in Earth Science

Guest Editors:

Dr. Carlos Enrique Montenegro Marin; Dr. Xuyun Zhang; and Dr. Nallappan Gunasekaran

Spatiotemporal data science is an emerging area of research, and with the development of novel computation techniques, its applications are widely expanded across various spatiotemporal databases. Data science is widely used across various disciplines for the effective analysis of data streams. Among them, analyzing geographical (spatial) variations of earth science have played a crucial role in mitigating harmful environmental impacts and climate change. More recently, satellites and unmanned aerial vehicles (UAV) such as drones capture more precise images, providing additional information to improve data analysis and decision-making.  This is one of the fundamental tradeoffs in present day data analytics systems. The availability of the data sources has become more abundant, creating the need for more advanced data analytics paradigms. Environmental monitoring (weather forecasting, plant disease prediction, identifying soil contaminants, etc.), recording moving object trajectories (bird, car) using geographical information systems (GIS), and finding social events with tag and time stamps are some of the typical use cases of data science in earth science, which is most common in our day to day lives. As a unifying paradigm, spatiotemporal data science in earth science supports conventional processing, analysis, and data visualization, enabling the researchers to effectively utilize a plethora of data concerning different aspects of earth science oceanography, metrology, ecology, environmental science, and many more. With spatiotemporal data science, we can easily perform mathematical and geometric operations, classify pixels (object recognition) of the real-time earth data in parallel to high-end processing systems such as cloud computing infrastructures.

While spatiotemporal data science can offer a new dimension for earth data interpretation, it still remains in infancy, and even some of the most fundamental research gaps in this area are still largely unaddressed. This includes what sort of datasets can be extracted from trajectories and which sort of algorithms and techniques could be used to extract the data effectively. Further, the continuous and discrete change of the spatiotemporal objects both in terms of spatial and non-spatial properties and implications of collocated neighboring spatiotemporal objects to each other adds increasing complexity to the data analysis processes.

This special issue aims at exploring new challenges and opportunities of spatiotemporal data analytics for earth science. Cutting-edge research contributions focusing on theoretical advances and spatiotemporal data science applications for earth informatics are most invited for submission.

The topic of interest includes the following:

  • Spatiotemporal statistics and its application in earth science
  • Effective ways of overcoming uncertainties in observation and models in space and time
  • Classification, pattern recognition, and clustering of earth data
  • Time series analysis and its applications in earth science
  • Spatiotemporal data visualization for earth science
  • Statistical computing
  • Spatiotemporal crowdsourcing and applications
  • Effective ways of storage and indexing of large-scale spatiotemporal data
  • Spatiotemporal modelling using geo-statistics and machine learning algorithms
  • Advances in spatiotemporal data science to find the interaction between geosphere and astrosphere (e.g., land use change and land degradation)
  • Spatiotemporal data science for natural and anthropogenic hazard management
  • Advances in spatial analysis and geocomputation
  • Machine learning for spatiotemporal data science

Important Dates:

Paper Submission Deadline

December 20,2021

Author Notification

December 18,2021

Revised Papers Submission

February 25,2022

Final Acceptance

April 20,2022

Guest Editorial Team:

Lead Guest Editor

Name: Dr. Carlos Enrique Montenegro Marin

Affiliation: Professor,

District University Francisco José de Caldas,

Bogotá, Colombia

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Dr. Carlos Enrique Montenegro Marin received the Diploma of Advanced Studies degree from the Pontifical University of Salamanca, in 2008, the M.Sc. degree in Information and Communication Systems from the Universidad Distrital Francisco José de Caldas, and the Ph.D. degree in Systems and Computer Services for the Internet from the University of Oviedo, Asturias, Spain, in 2012. He was classified with the highest recognition of research by Colciencias in 2017 (Senior Researcher). He is the director of the GIIRA research group of the University District, a group that also received the highest recognition by Colciencias. He is currently a Systems Engineer. His skills and expertise are in the areas of Java Programming, Cloud Computing, Web Development, Object-Oriented Programming, Grid Computing, LMS, Virtualization, Software Engineering, and Linux Administration.

Co-Guest Editors

Name: Dr. Xuyun Zhang 

Affiliation: Senior Lecturer, Department of Computing

Macquarie University, Australia

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Dr. Xuyun Zhang is currently working as a senior lecturer in Department of Computing at Macquarie University in Australia. He worked as a lecturer in The University of Auckland during 2016 - 2019 and a postdoc researcher in NICTA (National ICT Australia, now Data61, and CSIRO) during 2014 - 2016. He got his PhD degree in Computer Science and Technology from University of Technology Sydney (UTS), Australia in 2014, and his Master’s and Bachelor’s degrees in the same major from Nanjing University, China in 2011 and 2008. He is an early/mid-career researcher with an international track record of research in areas including scalable and secure machine learning, big data privacy and cyber security, big data mining and analytics, cloud/edge/service computing and IoT, etc.

Co-Guest Editors

Name: Dr. Nallappan Gunasekaran

Affiliation: Research professor,

Department of Mathematical Sciences,

Shibaura Institute of Technology, Saitama 337-8570, Japan.

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Dr. Nallappan Gunasekaran received his Ph.D. degree in mathematics from Thiruvalluvar University, Vellore, India, in 2017. He completed his B.Sc. degree from the Mahendra Arts and Science College, Namakkal, affiliated to Periyar University, Salem, India, in 2009; master’s degree in mathematics from the Jamal Mohamed College, Affiliated to Bharathidasan University, Trichy, India, in 2012; and Master of Philosophy degree in mathematics (cryptography) from Bharathidasan University, in 2013. He was a Junior Research Fellow with the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), Government of India, New Delhi, India. He was also a Postdoctoral Research Fellow with the Research Center for Wind Energy Systems, Kunsan National University, Gunsan, South Korea, from May 2017 to October 2018. He is currently a Postdoctoral Research Fellow with the Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan. He has authored or coauthored of more than 30 research articles in various SCI journals. His research interests include complex-valued neural networks, complex dynamical networks, control theory, stability analysis, sampled-data control, multiagent systems, T-S fuzzy theory, cryptography, and so on. He serves as a Reviewer for various SCI journals.