GIScience for Risk Management in Big Data Era

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 31177

Special Issue Editors


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Guest Editor
Laboratory on Geoinformatics and Cartography, Department of Geography, Faculty of Science, Masaryk University, Kotlarska 2, 61137 Brno, Czech Republic
Interests: disaster risk reduction; disaster mapping; context and adaptive cartography; health cartography; big spatial data
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Geography, Nanjing Normal University, Wenyuna Road 1, Nanjing 210023, China
Interests: disaster mapping; context and adaptive cartography; indoor navigation; map genreralization

Special Issue Information

Dear Colleagues,

According to the updated “2009 UNISDR Terminology” (Terminology of UNISDR, 2016) and modified Terminology of UN DRR (2019), disaster risk management (DRM) is the application of disaster risk reduction policies and strategies to prevent new disaster risk, reduce existing disaster risk, and manage residual risk, contributing to the strengthening of resilience and reduction of disaster losses. Disaster risk reduction (DRR) is aimed at preventing new while reducing existing disaster risk and managing residual risk, all of which contribute to strengthening resilience and, therefore, to the achievement of sustainable development. In other words, DRR is the policy objective of disaster risk management, and its goals and objectives are defined in disaster risk reduction strategies and plans.

New concepts and strategies are being developed and also improved by changing the scientific and data frameworks in which new approaches are applied. We are now living in the big data era, with efforts toward creating smart solutions and developing data-driven geography and new approaches in various disciplines, like cyberspace questions. Taken together, these have led to the creation of new knowledge and a technological situation with new potentials for solving early warning (EW), DRM, and DRR problems. Very important fundamental aspects of the mentioned processes are various data concepts and the development of new quality data arsenals. Critical efforts for the contemporary world are sustainable development goals (SDGs) defined by the UN in 2015 as “2030 Agenda for Sustainable Development”, defining 17 tasks and newly accompanied by sets of indicators and DRR Sendai goals and global indicators. Both initiatives are newly accompanied not only by goals but also by two kinds of indicators.

In the Third UN World Conference on DRR, March 14, 2015, in Sendai, Japan, the Sendai Framework for Disaster Risk Reduction 2015–2030 was adopted. The UN DRR conference is a culmination of contemporary state-of-the-art approaches to solving problems of risks and disasters on our planet. As never before, the conference, in its materials, mentioned the role of Information and Communication Technologies, GIScience, GIS, remote sensing, mapping, sensors, and volunteer geographic information, amongst other aspects.

In the Sendai Framework, four new priorities of action were defined:

  • Priority 1: Understanding disaster risk
  • Priority 2: Strengthening disaster risk governance to manage disaster risk
  • Priority 3: Investing in disaster risk reduction for resilience
  • Priority 4: Enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation, and reconstruction.

Though already established and described, systems of SGD indicators and global indicators of Sendai Framework are still being developed.

A very important presumption of the realization of the abovementioned steps is creation of the so-called Global Data Ecosystem which should be realized during the pre-existing UN Geospatial Global Information Management (UN GGIM) which is already a mature initiative. There are also other important data initiatives, like GEO and GEOSS, Digital Belt and Road (DBAR), COPERNICUS (formerly GEOSS), and INSPIRE, with ambitions to contribute to the creation of the Global Data Ecosystem.

Along these lines, this Special Issue aims to capture recent efforts and advancements in harnessing the power of GIScience for risk management in the big data era.

The first group of possible topics is to inspire potential authors to deal with basic and new trends related to the big data era. The contribution of novel approaches to spatial data collection (social networks, sensors, citizen science, VGI, etc.), disaster big data processing and sharing, real-time data-centric intelligence based on sensors, harmonization of heterogeneous data into a single structure, cybersecurity of geographical information systems and others, is welcomed, along with analyses and commentary.

The second thematic block will cover cartography and GIS theories such as mobile disaster cartography, concepts, ontologization and standardization, cross-cultural aspects of disaster cartography, investigation of the psychological condition of end-users given by their personal character and situation, and the psychological condition of rescued persons are offered together with questions that are still open on the mapping methodologies and technologies for EW&CM from children and senior perspectives.

The third group of topics aims to address mapping and visualization techniques. Dynamic and real-time cartographic visualization concepts and techniques for enhanced operational activities for selected EW, DRM, and DRR purposes are highlighted. Included in the same group are both virtual environments for EW, DRM, and DRR as well as 3D analysis and visualization of disaster events.

The last group of topics is devoted to services and applications, and may include analyses and descriptions of location-based services for emergencies (web services, etc.), multimodal emergency positioning, mapping based on social big data, internet of things for solutions and visualizations, and disaster chain modeling.

In particular, potential inspiring topics for authors include the following:

  1. Big data
  • Novel approaches to spatial data collection (social networks, sensors, citizen science, VGI, etc.)
  • Geospatial big data computing, analytics, and sharing for disaster management
  • Real-time data-centric intelligence based on sensors for purposes of DRM and DRR harmonization and homogenization of heterogenous data.
  • Searching and calculations of anomalies in geospatial big data in DRM and DRR process
  • Cartographic use of remotely sensed and other geospatial data for early warning, DRM, and DRR
  • Cybersecurity of geographical information systems (of data flows from sensor networks to GIS platforms)
  1. Cartography and GIS theories
  • Mobile disaster cartography
  • Concepts, ontologization, and standardization for early warning, hazard, risk, and vulnerability mapping
  • Mechanisms of command and control systems integration
  • Cross-cultural aspects of disaster cartography (traditions, universality, and conventions and their integration)
  • Investigation of the psychological condition of end-users given by their personal character and situation and the psychological condition of rescued persons
  • Mapping methodologies and technologies for EW&CM from the perspectives of children and seniors. Designing, understanding, and using maps for EW, DRM, and DRR for children and seniors
  1. Mapping and visualization techniques
  • Dynamic and real-time cartographic visualization concepts and techniques for enhanced operational early warning and DRM activities for selected purposes (various government levels, inter-state cooperation, first aid, etc.)
  • Virtual environments for EW and DRR (geographic, indoors, underground, etc.)
  • 3D disaster (floods, fires, slides, tsunamis, etc.) analysis and visualization
  1. Services and application
  • Location-based service for emergencies
  • Multimodal emergency positioning
  • Disaster risk analyses and mapping using social big data
  • Internet of things (IoT) in disaster solutions and visualizations
  • Disaster chain modeling

Guest Editors of the Special Issue do not wish to limit the possible topics to only those listed. The Special Issue is also open to other topics related to the theme.

Prof. Dr. Milan Konecny
Prof. Dr. Jie Shen
Prof. Dr. Zhenlong Li
Guest Editors

Manuscript Submission Information

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Keywords

  • big data
  • GIScience
  • disaster management
  • cartography

Published Papers (8 papers)

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Research

14 pages, 434 KiB  
Article
Wildland Fires in the Czech Republic—Review of Data Spanning 20 Years
by Pavel Špulák
ISPRS Int. J. Geo-Inf. 2022, 11(5), 289; https://doi.org/10.3390/ijgi11050289 - 29 Apr 2022
Cited by 3 | Viewed by 2036
Abstract
The following article deals with more than 20 years of historical wildland fire data from the Czech Republic, logged in the databases of the operational centers of the Fire and Rescue Service of the Czech Republic (FRS of CR). First, the definition of [...] Read more.
The following article deals with more than 20 years of historical wildland fire data from the Czech Republic, logged in the databases of the operational centers of the Fire and Rescue Service of the Czech Republic (FRS of CR). First, the definition of the term wildland fire is introduced. After that, the locations of wildland fires are discussed, from the point of view of their introduction into the information systems. Next, as the FRS of CR is organized on a regional basis, the number of wildland fires is analyzed regionally. On the basis of this analysis, some advice concerning the preparation for and prevention of wildland fires is provided—for example, focusing fire prevention campaigns in regions where the wildland fire incidence per inhabitant is high, planning aerial firefighting asset coverage with respect to the occurrence of wildland fires, or deploying the necessary fire suppression equipment according to the dominant wildland fire fuel type. Finally, questions concerning the homogeneity of groups of wildland fires which naturally emerge during the process of selection from the emergency database are discussed. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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16 pages, 5712 KiB  
Article
Development Characteristics and Causes of a Fatal Landslide Occurred in Shuicheng, Guizhou Province, China
by Yu Chen, Xiangli He, Chong Xu, Yuandong Huang, Pengfei Zhang, Zhihua Luo and Tao Zhan
ISPRS Int. J. Geo-Inf. 2022, 11(2), 119; https://doi.org/10.3390/ijgi11020119 - 8 Feb 2022
Cited by 5 | Viewed by 2461
Abstract
At about 20:40 on 23 July 2019, a high-level and long-runout landslide occurred in Jichang Town, Shuicheng County, Guizhou Province (hereafter called the Shuicheng landslide). This slope failure was highly devastating, and most of the local residents were severely affected, including 52 dead [...] Read more.
At about 20:40 on 23 July 2019, a high-level and long-runout landslide occurred in Jichang Town, Shuicheng County, Guizhou Province (hereafter called the Shuicheng landslide). This slope failure was highly devastating, and most of the local residents were severely affected, including 52 dead or missing. Based on the information provided by field investigations, drilling boreholes, and Google Earth, we describe the landform and stratigraphy characteristics of the Shuicheng landslide in this study. Additionally, the dataset of 1158 ancient landslides near the Shuicheng landslide is obtained by Google Earth and ArcGIS, including their morphological scales and spatial distribution characteristics, to analyze the landslide development preference in this region. Furthermore, the causes of the Shuicheng landslide are discussed by analyzing the effects of active tectonic activities on the broken basalt and the steep terrain, as well as the trigger action of continuous heavy rainfall. Finally, a previous empirical prediction formula of sliding distance is verified by the Shuicheng landslide parameters and is applied into the width range calculation of the ancient landslide risk zones, which is a kind of risk source for future landslides. The result indicates the area up to ≈3500 m away from the landslide risk source should be concerned during engineering construction in the study area. This study provides significant scientific guidance for the risk management of potential landslide hazards in this area. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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21 pages, 16525 KiB  
Article
Inventory and Distribution Characteristics of Large-Scale Landslides in Baoji City, Shaanxi Province, China
by Lei Li, Chong Xu, Xiwei Xu, Zhongjian Zhang and Jia Cheng
ISPRS Int. J. Geo-Inf. 2022, 11(1), 10; https://doi.org/10.3390/ijgi11010010 - 29 Dec 2021
Cited by 15 | Viewed by 3837
Abstract
Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, [...] Read more.
Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, a comprehensive and detailed map of the landslide distribution in this area was prepared in conjunction with the historical literature, which includes 3440 landslides. On this basis, eight variables, including elevation, slope, aspect, slope position, distance to the fault, land cover, lithology and distance to the stream were selected to examine their influence on the landslides in the study area. Landslide number density (LND) and landslide area percentage (LAP) were used as evaluation indicators to analyze the spatial distribution characteristics of the landslides. The results show that most of the landslides are situated at elevations from 500 to 1400 m. The LND and LAP reach their peaks at slopes of 10–20°. Slopes facing WNW and NW directions, and middle and lower slopes are more prone to sliding with higher LND and LAP. LND and LAP show a decreasing trend as the distance to the fault or stream increases, followed by a slow rise. Landslides occur primarily in the areas covered by crops. Regarding lithology, the regions covered by the Quaternary loess and Cretaceous gravels are the main areas where landslides occur. The results would be helpful for further understanding the developmental characteristics and spatial distribution of landslides on the Loess Plateau, and also provide a support to subsequent landslide susceptibility mapping in this region. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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20 pages, 3046 KiB  
Article
Crisis Map Design Considering Map Cognition
by Ping Du, Dingkai Li, Tao Liu, Liming Zhang, Xiaoxia Yang and Yikun Li
ISPRS Int. J. Geo-Inf. 2021, 10(10), 692; https://doi.org/10.3390/ijgi10100692 - 14 Oct 2021
Cited by 2 | Viewed by 2126
Abstract
Crisis maps play a significant role in emergency responses. Users are challenged to interpret a map rapidly in emergencies, with limited visual information-processing resources and under time pressure. Therefore, cartographic techniques are required to facilitate their map cognition. In this study, we analyzed [...] Read more.
Crisis maps play a significant role in emergency responses. Users are challenged to interpret a map rapidly in emergencies, with limited visual information-processing resources and under time pressure. Therefore, cartographic techniques are required to facilitate their map cognition. In this study, we analyzed the exogenous and endogenous disruptions that users needed to overcome when they were reading maps. The analysis results suggested that cartographers’ taking the stressors into consideration could promote the cognitive fit between cartographers and users, improving map cognition and spatial information supply–demand matching. This paper also elaborates the course of map visual information processing and related graphic variables to visual attention attributes. To improve the users’ map cognition in time-critical emergency situations, crisis map design principles and a methodology were proposed. We developed three fire emergency rescue road maps and performed two evaluations to verify the effectiveness of the principles. Our experiments showed that the principles could effectively facilitate the users’ rapid map perception and proper understanding, by reducing their cognitive load, and could improve the quality of the crisis maps to some extent. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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20 pages, 11108 KiB  
Article
Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province
by Shenghua Xu, Meng Zhang, Yu Ma, Jiping Liu, Yong Wang, Xinrui Ma and Jie Chen
ISPRS Int. J. Geo-Inf. 2021, 10(10), 646; https://doi.org/10.3390/ijgi10100646 - 26 Sep 2021
Cited by 17 | Viewed by 3099
Abstract
Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary [...] Read more.
Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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19 pages, 4821 KiB  
Article
GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia
by Matej Vojtek, Jana Vojteková and Quoc Bao Pham
ISPRS Int. J. Geo-Inf. 2021, 10(9), 578; https://doi.org/10.3390/ijgi10090578 - 27 Aug 2021
Cited by 16 | Viewed by 3166
Abstract
The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in [...] Read more.
The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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19 pages, 16366 KiB  
Article
Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach
by Jagpal Singh Tomar, Nikola Kranjčić, Bojan Đurin, Shruti Kanga and Suraj Kumar Singh
ISPRS Int. J. Geo-Inf. 2021, 10(7), 447; https://doi.org/10.3390/ijgi10070447 - 30 Jun 2021
Cited by 32 | Viewed by 6698
Abstract
The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires [...] Read more.
The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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17 pages, 3380 KiB  
Article
Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?
by Xiao Huang, Zhenlong Li, Junyu Lu, Sicheng Wang, Hanxue Wei and Baixu Chen
ISPRS Int. J. Geo-Inf. 2020, 9(11), 675; https://doi.org/10.3390/ijgi9110675 - 13 Nov 2020
Cited by 47 | Viewed by 4907
Abstract
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta [...] Read more.
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Full article
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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