IJGI SPECIAL ISSUE “SCALING, SPATIO-TEMPORAL MODELING, AND CRISIS INFORMATICS”
We are inviting submissions to a new special issue “Scaling, Spatio-Temporal Modeling, and Crisis Informatics” by the ISPRS International Journal of Geo-Information.
There has been a significant increase in the severity and frequency of crises and hazards worldwide, which are defined as “an interruption in the reproduction of economic, cultural, social and/or political life (Johnston, R.J. (2002). Dictionary of human geography. (4th ed.). Oxford, UK: Blackwell.)”. While extreme weather events are usually the causes of crisis, 2020 has become an expensive and deadly year due to another type of crisis, i.e., the COVID-19 pandemic. Whatever the cause of a crisis, though, technologies like cloud computing, location-based services, network science, web applications, and artificial intelligence (AI) are being used for crisis informatics to aid with crisis management and resilience efforts.
Similarly, data obtained from both static and dynamic sources, such as remote sensing, unmanned aerial systems, and social media, enable the development of new approaches to characterize and predict disaster situations at different locations and scales. Human dynamics data in both physical and virtual spaces are big, spatial, temporal, dynamic, and unstructured. The proliferation of data and interactive mapping technologies has also significantly enhanced access to and utility of spatial decision support systems, helping communities to better prepare for, respond to, and recover from crises and hazards. Understanding human dynamics can help to more efficiently deal with natural or man-made disasters. Significant advancements have also been made in developing statistical as well as data-driven models to integrate these heterogeneous data for real-time and off-time informatics. Because of the heterogeneous nature of these data in terms of data structure, content, data sources, and the spatial and temporal resolutions at which they are being obtained, these data suffer from uncertainties associated with positional accuracy, reliability, and completeness, thereby impacting the quality of the models being generated and their reproducibility.
Due to the spatiotemporal nature of a crisis, geospatial data sets and spatiotemporal models integrating various data sources are being developed. In addition to the uncertainties associated with the data, the developed models rarely account for scale, which influences not only the mechanisms used to aggregate and integrate data sets, but also the final outputs of the model. The end result is the development of models for crisis informatics that produce varying results and hence may not be useful in real-time decision making.
In this Special Issue in ISPRS International Journal of Geo-Information, we solicit articles that advance theories and methods and/or applications integrating spatial and temporal datasets at varying scales for crisis informatics. The articles should leverage existing theories and/or develop new theories of scaling and spatiotemporal modeling while taking advantage of big data theories and technologies to aid with crisis/disaster preparedness, mitigation, recovery, and resilience.
Potential topics include (but are not limited to) the following:
1. Uncertainty in data and spatiotemporal models;
2. Data fusion methods and accuracies;
3. Data quality and impact on decision making;
4. Role of scale and reproducibility of models;
5. Human dynamics in crises and hazards;
6. Open knowledge network and convergence research;
7. Spatial decision support systsem for crisis management;
8. Geo-visualization and geo-computation techniques for real-time applications;
9. Models and analytics for crisis, human movement and behaviors, interaction of natural and built environments.
This Special Issue is scheduled to be published by 30 April 2021. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website (www.mdpi.com/journal/ijgi/special_issues/crisis)
Dr. Bandana Kar, Oak Ridge National Laboratory
Dr. Xinyue Ye, Texas A&M University
Dr. Zhenlong Li, University of South Carolina
Dr. Qunying Huang, University of Wisconsin-Madison