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

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
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Dr. Xinyue Ye
E-Mail Website
Guest Editor
Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
Interests: network science; natural language processing; open source geocomputation; spatio-temporal analysis; spatial econometrics; urban informatics; visual analytics
Special Issues and Collections 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 and Collections 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 and Collections 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 papers will be 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 (2 papers)

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Research

Open AccessArticle
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
Viewed by 272
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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Open AccessArticle
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 1 | Viewed by 981
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Disaster image recognition by fusing multi-modality social networks data
Authors: Zhiqiang Zou1,2*, Hongyu Gan1 and Qunying Huang3
Affiliation: 1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210023, China 3. Department of Geography, University of Wisconsin–Madison, 550 N. Park St., Science Hall, Madison, 53706 United States * Correspondence: Zhiqiang Zou ([email protected])
Abstract: Abstract: The application of social media in disaster assessment has received widespread attention. When a disaster occurs, many users will post messages in a variety of formats, e.g., photo and text, on social media platforms. Useful information may be mined from these multi-modality data, which can provide decision support to disaster assessors. However, the multi-modality data collected from social media contain much irrelevant content which need to be filtered out. Existing work mostly combined the information of image classification and text keyword extraction in a very simple way; in other words, these methods treated the image and text features separately. This paper will seamlessly integrate pictures and text information based on the multi-modality fusion method to complete the recognition of useful disaster pictures. A CNN architecture was designed to extract the visual features from photos and a RNN framework was then used to extract the textual features. Next, a novel data fusion model will be developed to combine both visual and textual features to complete disaster image recognition. Using different disaster events (e.g., Earthquakes) as case studies, the proposed method can largely improve the identification of disaster pictures by comparing with the state-of-the-art work. Keywords: feature extraction、image recognition、text classification 、CNN、RNN、multi-modality fusion

Title: Integrating high-resolution imagery and points of interest (POIs) for urban scene recognition
Authors: Wenjing Denga, Qian Shia*, Qiang Qiub a Sun Yat-sen University, Guangzhou 510220, China b Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China *Corresponding: [email protected]
Affiliation: a Sun Yat-sen University, Guangzhou 510220, China b Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China *Corresponding: [email protected]
Abstract: Urban scenes play an important role in urban planning and management. As a result, high spatial resolution remote sensing imagery is commonly considered an ideal choice to extract information about urban land use. However, since the recognition of urban scenes require socio-economic knowledge, the information extracted from remotely sensed imagery cannot accurately reflect human activities and urban functions. However, social-related data (e.g., social network data) can compensate for this shortcoming and provide semantic labeling of some urban targets. Current methods mostly use either remote sensing data or social data to describe urban land use at a pixel or object level. The combination of features derived from high-resolution remote sensing imagery and socioeconomic features derived from crowdsourced data –such as points of interest (POIs) in block level– can help to understand better urban scenes. In this paper, we use a convolutional neural network (CNN) to extract spatial and spectral features in functional zones using both high-resolution images and parcels in open street maps. Then, kernel density estimation is adopted to describe socio-economic features using POIs. Finally, a random forest classifier is used to delineate both spectral characteristics and spatial object distributions, as well as semantic information in urban scenes. Our experimental results demonstrate that remote sensing data combined with POIs can significant help to better understand complicated urban scenes at a block level. Key words: urban scene recognition; high-resolution imagery; points of interest (POIs); block level.

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