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Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 24304

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Prof. Dr. Maria Antonia Brovelli

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Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Songnian Li

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Guest Editor

Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON, Canada
Interests: big geospatial data analytics; data mining and knowledge discovery; human mobility; spatio-temporal dynamics; citizen science; digital twins for smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last fifty years, the number of annual instances of disasters has increased from around 60 to over 400. Various disasters kill over 60,000 people a year and cost hundreds of billions of US dollars (https://ourworldindata.org/natural-disasters). The geospatial understanding of the underlying factors that induce and influence disasters is often critical for analyzing and detecting them and possibly identifying the patterns that can lead to and/or help to predict similar disasters in the future. This Special Issue focuses on disasters that can be traced directly or indirectly to human actions, such as hazardous material spills, fires, groundwater contamination, transportation accidents, structure failures, mining accidents, explosions, and acts of terrorism, as well as conflicts and related damages. In addition, the amount of publicly available data that can be used to analyze and understand disaster nature is massive. Often, these data are inhomogeneous, inconsistent, and unstructured. Thus, they require innovative methods and tools for collection, processing, and transformation into datasets that can be analyzed and interpreted for practitioners to enhance their knowledge and understanding. This Special Issue invites researchers and practitioners to submit original work related to the entire spectrum of data analysis processes for disaster-related studies. We welcome practitioners and political and social scientists to present their requirements for the tools and processes needed for their daily work. We also welcome researchers presenting various methodologies to collect, process, and convert contextual and inconsistent publicly available data into ready-to-analyze geospatial datasets. Furthermore, we welcome submissions that focus on developing models for disaster-related data analysis. Submissions from cross-cutting disciplines such as climate change, environmental, political, social, data, and geospatial data science, among others, are welcome. Topics include, but are not limited to, the following:

Innovative geospatial data and crowdsourced information (such as volunteered geographic information, collaborative maps, social media) for analysis and prevention of human-induced disasters and conflicts;
Disaster/conflict assessment and mapping;
Spatiotemporal monitoring of disaster and conflicts;
Event detection and prediction regarding human-induced disaster and conflicts;
Geospatial planning tools for disaster/conflict prevention and humanitarian actions to alleviate the effects of human-induced disasters and conflicts;
Geospatial data science applied to disaster/conflict analysis and prevention;
Collaborative web geospatial platforms for humanitarian actions to alleviate the effects of human-induced disasters and conflicts.

Prof. Dr. Maria Antonia Brovelli
Prof. Dr. Songnian Li
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 1700 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.

Keywords

disaster
conflicts
human-induced
geospatial
open data
analytics
prediction
prevention

Published Papers (12 papers)

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Research

32 pages, 10787 KiB

Open AccessArticle

Climate Change, Forest Fires, and Territorial Dynamics in the Amazon Rainforest: An Integrated Analysis for Mitigation Strategies

by Nathalia Celis, Alejandro Casallas, Ellie Anne Lopez-Barrera, Martina Felician, Massimo De Marchi and Salvatore E. Pappalardo

ISPRS Int. J. Geo-Inf. 2023, 12(10), 436; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12100436 - 23 Oct 2023

Cited by 2 | Viewed by 2904

Abstract

Recent times have witnessed wildfires causing harm to both ecological communities and urban–rural regions, underscoring the necessity to comprehend wildfire triggers and assess measures for mitigation. This research hones in on Cartagena del Chairá, diving into the interplay between meteorological conditions and land cover/use that cultivates a conducive environment for wildfires. Meteorologically, the prevalence of wildfires is concentrated during boreal winter, characterized by warm and dry air, strong winds, and negligible precipitation. Additionally, wildfires gravitate toward river-adjacent locales housing agriculture-linked shrubs, notably in the northern part of the zone, where a confluence of land attributes and meteorological factors synergize to promote fire incidents. Employing climate scenarios, we deduced that elevated temperature and reduced humidity augment wildfire susceptibility, while wind speed and precipitation discourage their propagation across most scenarios. The trajectory toward a warmer climate could instigate fire-friendly conditions in boreal summer, indicating the potential for year-round fire susceptibility. Subsequently, via machine-learning-driven sensitivity analysis, we discerned that among the scrutinized socio-economic variables, GINI, low educational attainment, and displacement by armed groups wield the most substantial influence on wildfire occurrence. Ultimately, these findings converge to shape proposed wildfire mitigation strategies that amalgamate existing practices with enhancements or supplementary approaches. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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24 pages, 10136 KiB

Open AccessArticle

An Analysis of the Evolution of Public Sentiment and Spatio-Temporal Dynamics Regarding Building Collapse Accidents Based on Sina Weibo Data

by Dongling Ma, Chunhong Zhang, Liang Zhao, Qingji Huang and Baoze Liu

ISPRS Int. J. Geo-Inf. 2023, 12(10), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12100388 - 26 Sep 2023

Viewed by 1160

Abstract

Monitoring, analyzing, and managing public sentiment surrounding urban emergencies hold significant importance for city governments in executing effective response strategies and maintaining social stability. In this study, we present a study which was conducted regarding the self-built house collapse incident in Changsha, China, that occurred on 29 April 2022, with a focus on leveraging Sina Weibo (a Twitter-like microblogging system in China) comment data. By employing the Latent Dirichlet Allocation (LDA) topic model, we identified key discussion themes within the comments and explored the emotional and spatio-temporal characteristics of the discourse. Furthermore, utilizing geographic detectors, we investigated the factors influencing the spatial variations in comment data. Our research findings indicate that the comments can be categorized into three main themes: “Rest in Peace for the Deceased”, “Wishing for Safety”, and “Thorough Investigation of Self-Built Houses”. Regarding emotional features, the overall sentiment expressed in the public discourse displayed positivity, albeit with significant fluctuations during different stages of the incident, including the initial occurrence, rescue efforts, and the establishment of accountability and investigative committees. These fluctuations were closely associated with the emotional polarity of the specific topics. In terms of temporal distribution, the peak in the number of comments occurred approximately one hour after the topic was published. Concerning spatial distribution, a positive sentiment prevailed across various provinces. The comment distribution exhibited a stair-like pattern, which correlated with interregional population migration and per capita GDP. Our study provides valuable insights for city governments and relevant departments in conducting sentiment analysis and guiding public opinion trends. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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15 pages, 43590 KiB

Open AccessArticle

Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series

by Thomas Di Martino, Bertrand Le Saux, Régis Guinvarc’h, Laetitia Thirion-Lefevre and Elise Colin

ISPRS Int. J. Geo-Inf. 2023, 12(8), 332; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12080332 - 09 Aug 2023

Cited by 3 | Viewed by 1587

Abstract

With an increase in the amount of natural disasters, the combined use of cloud-penetrating Synthetic Aperture Radar and deep learning becomes unavoidable for their monitoring. This article proposes a methodology for forest fire detection using unsupervised location-expert autoencoders and Sentinel-1 SAR time series. The models are trained on SAR multitemporal images over a specific area using a reference period and extract any deviating time series over that same area for the test period. We present three variations of the autoencoder, incorporating either temporal features or spatiotemporal features, and we compare it against a state-of-the-art supervised autoencoder. Despite their limitations, we show that unsupervised approaches are on par with supervised techniques, performance-wise. A specific architecture, the fully temporal autoencoder, stands out as the best-performing unsupervised approach by leveraging temporal information of Sentinel-1 time series using one-dimensional convolutional layers. The approach is generic and can be applied to many applications, though we focus here on forest fire detection in Canadian boreal forests as a successful use case. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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22 pages, 1414 KiB

Open AccessArticle

Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

by Timur Obukhov and Maria A. Brovelli

ISPRS Int. J. Geo-Inf. 2023, 12(8), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12080322 - 02 Aug 2023

Viewed by 1414

Abstract

In this research, we focused on armed conflicts and related violence. The study reviewed the use of machine learning to predict the likelihood of conflict escalation and the role of conditioning factors. The results showed that machine learning and predictive models could help identify conflict-prone locations and geospatial factors contributing to conflict escalation. The study found 46 relevant papers and emphasized the importance of considering unique predictors and conditioning factors for each conflict. It was found that the conflict susceptibility of a region is influenced principally by its socioeconomic conditions and its political/governance factors. We concluded that machine learning has the potential to be a valuable tool in conflict analysis and, therefore, it can be an asset in conflict mitigation and prevention, but the accuracy of the models depends on data quality and the careful selection of conditioning factors. Future research should aim to refine the methodology for more accurate prediction of the models. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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24 pages, 7584 KiB

Open AccessArticle

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

by Mayra Salcedo-Gonzalez, Julio Suarez-Paez, Manuel Esteve and Carlos Enrique Palau

ISPRS Int. J. Geo-Inf. 2023, 12(7), 291; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12070291 - 20 Jul 2023

Viewed by 1194

Abstract

This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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19 pages, 15019 KiB

Open AccessArticle

Synergy of Road Network Planning Indices on Central Retail District Pedestrian Evacuation Efficiency

by Gen Yang, Tiejun Zhou, Mingxi Peng, Zhigang Wang and Dachuan Wang

ISPRS Int. J. Geo-Inf. 2023, 12(6), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12060239 - 09 Jun 2023

Viewed by 1125

Abstract

Pedestrian evacuation is an important measure to ensure disaster safety in central retail districts, the efficiency of which is affected by the synergy of road network planning indices. This paper established the typical forms of central retail district (CRD) road networks in terms of block form, network structure and road grade, taking China as an example. The experiment was designed using the orthogonal design of experiment (ODOE) method and quantified the evacuation time under different road network planning indices levels through the Anylogic simulation platform. Using range and variance analysis methods, the synergy of network density, network connectivity, road type and road width on pedestrian evacuation efficiency were studied from the perspectives of significance, importance and optimal level. The results showed that the type of CRD will affect the importance of network planning indices, and that the network connectivity is insignificant (P 0.477/0.581) in synergy; networks with wide pedestrian primary roads (30.1~40.0 m), medium secondary roads (3.1~5.0 m/side) and high density (11.0~13.0 km/km²) have the highest evacuation efficiency. From the perspective of evacuees, this paper put forward urban design implications on CRD road networks to improve their capacity for disaster prevention and reduction. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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19 pages, 4698 KiB

Open AccessArticle

Hybrid Prediction Model Based on Decomposed and Synthesized COVID-19 Cumulative Confirmed Data

by Zongyou Xia, Gonghao Duan and Ting Xu

ISPRS Int. J. Geo-Inf. 2023, 12(6), 215; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12060215 - 24 May 2023

Cited by 1 | Viewed by 1180

Abstract

Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the future. So, this paper proposes a hybrid prediction model based on particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) and AdaBoost algorithm. To address the issue of determining the optimal number of modes K and the penalty factor (

α

) in the variational mode decomposition (VMD), an adaptive value for particle swarm optimization (PSO) is proposed. Specifically, the weighted average sample entropy of the relevant coefficients is utilized to determine the adaptive value. First, the epidemic data are decomposed into multiple modal components, known as intrinsic mode functions (IMFs), using PSO-VMD. These components, along with policy-based factors, are integrated to form a multivariate forecast dataset. Next, each IMF is predicted using AdaBoost-LSTM. Finally, the prediction results of all the IMF components are reconstructed to obtain the final prediction result. Our proposed method is validated by the cumulative confirmed data of Hubei and Hebei provinces. Specifically, in the case of cumulative confirmation data, the coefficient of determination (

R^{2}

) of the mixed model is increased compared to the control model, and the average mean absolute error (MAE) and root mean square error (RMSE) decreased. The experimental results demonstrate that the VMD–AdaBoost–LSTM model achieves the highest prediction accuracy, thereby offering a new approach to COVID-19 epidemic prediction. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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14 pages, 2694 KiB

Open AccessArticle

The Spatial Data Analysis of Determinants of U.S. Presidential Voting Results in the Rustbelt States during the COVID-19 Pandemic

by Shianghau Wu

ISPRS Int. J. Geo-Inf. 2023, 12(6), 212; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12060212 - 23 May 2023

Viewed by 1096

Abstract

This study aims to analyze the factors that determine voting behavior in the rustbelt states during the 2020 U.S. presidential election. The rustbelt states are traditionally considered “swing states” and play a crucial role in determining the outcome of the presidential election. The study employs a spatial econometrics model that considers COVID-19-related factors, such as the percentage of people wearing masks and the number of COVID-19 deaths in each county of the rustbelt states. Firstly, the study identifies the most suitable spatial econometrics model. Secondly, the study shows that COVID-19 pandemic-related independent variables had a significant positive impact on the Republican Party’s results in the U.S. presidential election while mask-wearing behavior had a significant negative impact. These results suggest that the COVID-19 pandemic has influenced voting behavior and altered the political landscape, but it does not have geographical effects. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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18 pages, 5906 KiB

Open AccessArticle

Construction and Analysis of Space–Time Paths for Moving Polygon Objects Based on Time Geography: A Case Study of Crime Events in the City of London

by Zhangcai Yin, Yuan Chen and Shen Ying

ISPRS Int. J. Geo-Inf. 2023, 12(6), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12060210 - 23 May 2023

Viewed by 1362

Abstract

Time geography considers that the motion of moving objects can be expressed using space–time paths. The existing time geography methods construct space-time paths using discrete trajectory points of a moving point object to characterize its motion patterns. However, these methods are not suitable for moving polygon objects distributed by point sets. In this study, we took a type of crime event as the moving object and extracted its representative point at each moment, using the median center to downscale the polygon objects distributed by the point sets into point objects with timestamps. On this basis, space–time paths were generated by connecting the representative points at adjacent moments to extend the application scope of space–time paths, representing the motion feature from point objects to polygon objects. For the case of the City of London, we constructed a space–time path containing 13 nodes for each crime type (n = 14). Then, each edge of the space–time paths was considered as a monthly vector, which was analyzed statistically from two dimensions of direction and norm, respectively. The results showed that crime events mainly shifted to the east and west, and crime displacement was the greatest in April. Therefore, space–time paths as proposed in this study can characterize spatiotemporal trends of polygon objects (e.g., crime events) distributed by point sets, and police can achieve improved success by implementing targeted crime prevention measures according to the spatiotemporal characteristics of different crime types. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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17 pages, 25111 KiB

Open AccessArticle

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

by Yingze Song, Degang Yang, Weicheng Wu, Xin Zhang, Jie Zhou, Zhaoxu Tian, Chencan Wang and Yingxu Song

ISPRS Int. J. Geo-Inf. 2023, 12(5), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12050197 - 13 May 2023

Cited by 6 | Viewed by 1575

Abstract

Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samples tend to be much smaller than non-landslide samples, is often overlooked. This problem is often one of the important factors affecting the performance of landslide susceptibility models. In this paper, we take the Wanzhou district of Chongqing city as an example, where the total number of data sets is more than 580,000 and the ratio of positive to negative samples is 1:19. We oversample or undersample the unbalanced landslide samples to make them balanced, and then compare the performance of machine learning models with different sampling strategies. Three classic machine learning algorithms, logistic regression, random forest and LightGBM, are used for LSA modeling. The results show that the model trained directly using the unbalanced sample dataset performs the worst, showing an extremely low recall rate, indicating that its predictive ability for landslide samples is extremely low and cannot be applied in practice. Compared with the original dataset, the sample set optimized through certain methods has demonstrated improved predictive performance across various classifiers, manifested in the improvement of AUC value and recall rate. The best model was the random forest model using over-sampling (O_RF) (AUC = 0.932). Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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27 pages, 6488 KiB

Open AccessArticle

Analysis of the Spatial Distribution and Associated Factors of the Transmission Locations of COVID-19 in the First Four Waves in Hong Kong

by Daping Yang, Wenzhong Shi, Yue Yu, Liang Chen and Ruizhi Chen

ISPRS Int. J. Geo-Inf. 2023, 12(3), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi12030111 - 06 Mar 2023

Cited by 2 | Viewed by 1718

Abstract

Understanding the space–time pattern of the transmission locations of COVID-19, as well as the relationship between the pattern, socioeconomic status, and environmental factors, is important for pandemic prevention. Most existing research mainly analyzes the locations resided in or visited by COVID-19 cases, while few studies have been undertaken on the space–time pattern of the locations at which the transmissions took place and its associated influencing factors. To fill this gap, this study focuses on the space–time distribution patterns of COVID-19 transmission locations and the association between such patterns and urban factors. With Hong Kong as the study area, transmission chains of the four waves of COVID-19 outbreak in Hong Kong during the time period of January 2020 to June 2021 were reconstructed from the collected case information, and then the locations of COVID-19 transmission were inferred from the transmission chains. Statistically significant clusters of COVID-19 transmission locations at the level of tertiary planning units (TPUs) were detected and compared among different waves of COVID-19 outbreak. The high-risk areas and the associated influencing factors of different waves were also investigated. The results indicate that COVID-19 transmission began with the Hong Kong Island, further moved northward towards the New Territories, and finally shifted to the south Hong Kong Island, and the transmission population shows a difference between residential locations and non-residential locations. The research results can provide health authorities and policy-makers with useful information for pandemic prevention, as well as serve as a guide to the public in the avoidance of activities and places with a high risk of contagion. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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27 pages, 8090 KiB

Open AccessArticle

Analysis and Evaluation of Non-Pharmaceutical Interventions on Prevention and Control of COVID-19: A Case Study of Wuhan City

by Wen Cao, Haoran Dai, Jingwen Zhu, Yuzhen Tian and Feilin Peng

ISPRS Int. J. Geo-Inf. 2021, 10(7), 480; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070480 - 12 Jul 2021

Cited by 3 | Viewed by 5276

Abstract

As the threat of COVID-19 increases, many countries have carried out various non-pharmaceutical interventions. Although many studies have evaluated the impact of these interventions, there is a lack of mapping between model parameters and actual geographic areas. In this study, a non-pharmaceutical intervention model of COVID-19 based on a discrete grid is proposed from the perspective of geography. This model can provide more direct and effective information for the formulation of prevention and control policies. First, a multi-level grid was introduced to divide the geographical space, and the properties of the grid boundary were used to describe the quarantine status and intensity in these different spaces; this was also combined with the model of hospital isolation and self-protection. Then, a process for the spatiotemporal evolution of the early COVID-19 spread is proposed that integrated the characteristics of residents’ daily activities. Finally, the effect of the interventions was quantitatively analyzed by the dynamic transmission model of COVID-19. The results showed that quarantining is the most effective intervention, especially for infectious diseases with a high infectivity. The introduction of a quarantine could effectively reduce the number of infected humans, advance the peak of the maximum infected number of people, and shorten the duration of the pandemic. However, quarantines only function properly when employed at sufficient intensity; hospital isolation and self-protection measures can effectively slow the spread of COVID-19, thus providing more time for the relevant departments to prepare, but an outbreak will occur again when the hospital reaches full capacity. Moreover, medical resources should be concentrated in places where there is the most urgent need under a strict quarantine measure. Full article

(This article belongs to the Special Issue Human-Induced Disaster and Conflict Analysis, Prediction, and Prevention by Geospatial Analytics and Information Systems)

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