Special Issue "Scaling, Spatio-Temporal Modeling, and Crisis Informatics"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 4156

Special Issue Editors

Dr. Bandana Kar
E-Mail Website
Guest Editor
Remote Sensing Group, Oak Ridge National Laboratory, P.O. Box 2008, 1 Bethel Valley, Road Oak Ridge, TN 37831-6134, USA
Interests: scaling and reproducibility; spatiotemporal modeling; geoifnormatics; risk assessment; infrastructure and community resilience; risk communication; spatial decision support system; remote sensing applications; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenlong Li
E-Mail Website
Guest Editor
Geoinformation and Big Data Research Laboratory (GIBD), Department of Geography, University of South Carolina, Columbia, SC 29208, USA
Interests: GIScience; spatial computing; geospatial big data; social media analytics; CyberGIS
Special Issues, Collections and Topics in MDPI journals
Dr. Qunying Huang
E-Mail Website
Guest Editor
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706-1491, USA
Interests: spatial data mining; machine learning; social media analytics; natural hazards; human mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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 charaterize 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.

Dr. Bandana Kar
Dr. Xinyue Ye
Dr. Zhenlong Li
Dr. Qunying Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Article
Disaster Image Classification by Fusing Multimodal Social Media Data
ISPRS Int. J. Geo-Inf. 2021, 10(10), 636; https://doi.org/10.3390/ijgi10100636 - 24 Sep 2021
Cited by 1 | Viewed by 904
Abstract
Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to [...] Read more.
Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%. Full article
(This article belongs to the Special Issue Scaling, Spatio-Temporal Modeling, and Crisis Informatics)
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Article
Analyzing the Research Evolution in Response to COVID-19
ISPRS Int. J. Geo-Inf. 2021, 10(4), 237; https://doi.org/10.3390/ijgi10040237 - 07 Apr 2021
Cited by 1 | Viewed by 768
Abstract
In order to understand how these studies are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with [...] Read more.
In order to understand how these studies are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with the extracted information, the research evolution was analyzed from the temporal, spatial, and topic perspectives. From a temporal view, in the three months after the emergence of COVID-19, the number of published papers showed an obvious growth trend, and it showed a relatively stable cyclical trend in the later period, which is basically consistent with the development of COVID-19. Spatially, most of the authors who participated in related research are concentrated in the United States, China, Italy, the United Kingdom, Spain, India, and France. At the same time, with the continuous spread of COVID-19 in the world, the distribution of the number of authors has gradually expanded, showing to be correlated with the severity of COVID-19 at a spatial scale. From the perspective of topic, the early stage of COVID-19 emergence, the related research mainly focused on the origin and gene identification of the virus. After the emergence of the pandemic, studies related to the diagnosis and analysis of psychological health, personal security, and violent conflict are added. Meanwhile, some categories are most closely related to the control and prevention of the epidemic, such as pathology analysis, diagnosis, and treatment; epidemic situation and coping strategies; and prediction and assessment of epidemic situation. In most time periods, the majority of studies focused on these three categories. Full article
(This article belongs to the Special Issue Scaling, Spatio-Temporal Modeling, and Crisis Informatics)
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Article
Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication
ISPRS Int. J. Geo-Inf. 2020, 9(9), 532; https://doi.org/10.3390/ijgi9090532 - 05 Sep 2020
Cited by 5 | Viewed by 1592
Abstract
While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant [...] Read more.
While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms. Full article
(This article belongs to the Special Issue Scaling, Spatio-Temporal Modeling, and Crisis Informatics)
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