Support Disaster Response Research Articles

EARTH AND PLANETARY INSIGHTS FOR HUMAN SUSTAINABILITY

Among different human activities having the goal to increase humanity’s quality of life as industrial, agricultural, commercial, transport, touristic ones, also activities connected to Earth and Planetary Insights must be mentioned. In this regard Earth and Planetary Insights especially by exploration, analysis and assessment encompass several scientific fields being an inter-, multi- and transdisciplinary task. The multitude of available approaches and existing applications of mentioned insights are covering almost all human activity fields ranging from main branches of Earth & Planetary Sciences such as for instance Surveying, Geology, in all its aspects, naming here hydrogeology, geotechnics, geophysics etc., Ecology and Environmental Sciences by considering relevant environmental elements such as air, water, soil, oceans, forests, as well as potential environmental impacts because of human activities such as environmental pollution and climate change, coming to Green, Nano- and Space Technologies, as well as to the use of renewable energy resources, all of them being interconnected by modern Management Tools. Space activities must be mentioned in the context of designing new technologies for researching our planet and Outer Space with its many celestial bodies, what means activities for planetary exploration. On the other side using space activities for getting human sustainability also means setting up regional and global emergency programs to support disaster response operations through the provision of satellite data being oriented to reduce disaster risks. With the vision of getting Earth and Planetary Insights for improving humanity quality of life, holistic thinking is per-se required, and in this context connected chances and challenges will be debated with the goal of assuring human sustainability. Developments on global level have emphasized that it depends on our mutual work and cooperation to prevent the increasing processes of current resources consumption from compromising the ability of future generations to meet their own needs.

Leveraging involution and convolution in an explainable building damage detection framework

ABSTRACT Timely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on bi-temporal pre/post-earthquake dataset extraction information from bi-temporal images, which is difficult. Furthermore, these methods require manual feature engineering for supervised learning models. To tackle the abovementioned limitation of the traditional damage detection frameworks, this research proposes a novel building damage map generation approach based only on post-event RS satellite imagery and advanced deep feature extractor layers. The proposed DL based framework is applied in an end-to-end manner without additional processing. This method can be conducted in five main steps: (1) pre-processing, (2) model training and optimization of model parameters, (3) damage mapping generation, (4) accuracy assessment, and (5) visual explanations of the proposed method’s predictions. The performance of the proposed method is evaluated by two real-world RS datasets that include Haiti-earthquake and Bata-explosion. Results of damage mapping show that the proposed method is highly efficient, yielding an OA of more than 84%, which is superior to other advanced DL-based damage detection methods.

An Improved Understanding of Natural Hazards and Disasters through the Use of Satellite Technologies: Contributions from UN-SPIDER

This communication informs on how the UN-SPIDER program of the United Nations Office for Outer Space Affairs contributes to the efforts of the United Nations’ system to mobilize international cooperation to mitigate the risks related to natural hazards, to coordinate disaster relief efforts, and to facilitate the links between disaster response and recovery. This communication presents information on how UN-SPIDER shapes its advisory support to developing countries to contribute to the institutionalization of the use of space-based data, information, products, and services by government agencies in charge of disaster management. It does so by providing examples of step-by-step procedures developed using open software and open satellite imagery; information regarding some of the efforts carried out by the program to facilitate access to space-based information provided by mechanisms established by the space community to support disaster response efforts worldwide; and information on its advisory support to developing countries, including training efforts. Lastly, this paper shows that the approach implemented by UN-SPIDER is allowing national disaster management agencies and other government agencies and ministries to institutionalize the use of space-based data, information, products, and services.

Conceptualizing State Voluntary Aid Organizations in Disaster (SVOAD) effectiveness

AbstractVoluntary Organizations Active in Disaster (VOADs) exist at the national, state, regional, and local levels as a platform for sharing information and supporting coordination among nongovernmental organizations (NGOs) that support disaster response and recovery efforts in the United States. These entities are described as important in disaster response and recovery policy and plans and in disaster scholarship. Yet, virtually no research has explicitly examined these entities at any level. This research began to address this gap in the literature by exploring what State VOAD (SVOAD) effectiveness meant to current and past SVOAD chairs through semi‐structured interviews. A grounded theory approach was used to analyze data gathered. The result of this analysis is a grounded definition of SVOAD effectiveness and identification of its dimensions. This manuscript presents the definition and the evidence that supports it and discusses how this information may be applied to support practice and future research.

Large scale simulation of pedestrian seismic evacuation including panic behavior

Understanding human behavior and predicting evacuation processes subsequently to an earthquake are critical aspects to provide valuable information to support disaster response activities. One possible and relevant aspect that characterizes human behavior in emergency situations is panic reactions, which can lead to dysfunctional and irrational responses by fugitives. Given the relevance of this component, this paper implements a panic behavior model in a large-scale agent-based model considering phenomenal parameters, such as seismic damage to the built environment, disruption of roads by earthquake-induced falling debris, and injuries of individuals. The proposed model is applied and tested in IdealCity, a virtual city simulation environment which resembles the geometry of the city of Turin, with about 900,000 inhabitants. The numerical simulations show that the inclusion of a panic behavior model increases the evacuation time despite the increased speed of agents during the evacuation process and this is probably caused by the fact that pedestrians tend to perform random actions before reaching their destination. Furthermore, existing human relationships between agents tend to convert the crowd from individual agents to group of agents that move together, and this behavior might affect the shelter saturation time of both shelters and hospitals as shown in the simulations analyzed in this paper.

GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid

The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.

Implications in the light of the experiences and perspectives of Mukhtars as local community leaders in the scope of disaster management

Involving local actors in disaster management practices is crucial to the transition to community-based disaster management (CBDM). This study, which includes the perspectives of mukhtars who have a say in local policy and planning decisions but are not sufficiently included in the local disaster management system, aims to give policymakers a new perspective on the roles and responsibilities that mukhtars can take in disasters and emergencies. In this descriptive study, thematic analysis of the data obtained by the semi-structured interview technique, one of the qualitative research methods, was carried out. Within the scope of the study, using a purposeful sampling method, interviews were conducted with 22 village mukhtars who had experienced landslides, rockfalls, avalanches, and floods in the past. In the analyses, the themes of preparedness, service continuity, response, organization, communication, and capacity building, which include the roles and responsibilities of mukhtars in disaster management, were revealed. In addition, the study results showed that the most significant shortcomings of local disaster organizations are education, infrastructure, and equipment, and the points that the mukhtars consider disadvantageous are incompetence, non-participatory disaster policies, and cumbersome bureaucratic structure. Organizing the local people, who have a high level of volunteerism and a spirit of solidarity, and who currently support disaster response with simple equipment to solve the stated deficiencies and disadvantages and to guide them effectively in disaster situations, offers unique opportunities for CBDM.

Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation.

Today, unpredictable damage can result from extreme weather such as heat waves and floods. This damage makes communities that cannot respond quickly to disasters more vulnerable than cities. Thus, people living in such communities can easily become isolated, which can cause unavoidable loss of life or property. In the meantime, many disaster management studies have been conducted, but studies on effective disaster response for areas surrounded by mountains or with weak transportation infrastructure are very rare. To fill the gap, this research aimed at developing an automated analysis tool that can be directly used for disaster response and recovery by identifying in real time the communities at risk of isolation using a web-based geographic information system (GIS) application. We first developed an algorithm to automatically detect communities at risk of isolation due to disaster. Next, we developed an analytics module to identify buildings and populations within the communities and efficiently place at-risk residents in shelters. In sum, the analysis tool developed in this study can be used to support disaster response decisions regarding, for example, rescue activities and supply of materials by accurately detecting isolated areas when a disaster occurs in a mountainous area where communication and transportation infrastructure is lacking.

Twitter data from the 2019–20 Australian bushfires reveals participatory and temporal variations in social media use for disaster recovery

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019–20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort.

Social work competence in disaster management: an integrative review

ABSTRACT This integrative review provides an overview of the core competencies (i.e. knowledge, values and skills) of social workers in each phase of disaster management (i.e. mitigation, preparedness, response and recovery), using the core competency framework for social work practice. A total of 183 items, including 77 journal articles, 97 book chapters, 8 editorials and 1 dissertation, were retrieved from seven academic databases and their content analysed. This paper shows that reflective practice is an important resource for social work training and provides evidence of social work competencies in disaster management. In addition to supporting disaster response and recovery, social work can contribute significantly to disaster mitigation and preparedness. This paper identifies three important competencies for social work practice in disaster contexts: community resilience building, localisation and environmental sustainability.

Domestic application of lessons learned by Canadian health care professionals working in international disaster settings: a qualitative research study.

Background:Individuals with prior experience in international disaster response represent an essential source of expertise to support disaster response in their home countries. Our objective was to explore the experiences of personnel involved in international emergency health response regarding their perceptions of essential disaster response attributes and capacities and determine how these competencies apply to the Canadian context.Methods:For this qualitative study, we conducted semistructured interviews with key informants in person or over the telephone from May to December 2018. Participants were delegates deployed as part of the Canadian Red Cross medical response team in a clinical or technical, or administrative role within the last 5 years. Interviews were audio-recorded and transcribed. Conventional content analysis was performed on the transcripts, and themes were developed.Results:Eighteen key informants from 4 Canadian provinces provided perspectives on individual attributes acquired during international deployments, such as agility and stress management, and team capacities developed, including collaboration and conflict management. Key informants, including administrators (n = 5), technicians (n = 4), nurses (n = 4), physicians (n = 3) and psychosocial support workers (n = 2), described these experiences as highly relevant to the Canadian domestic context.Interpretation:Canadian physicians and health care workers involved with international disaster response have already acquired essential capacities, and this experience can be vital to building efficient disaster response teams in Canada. These findings complement the Canadian Medical Education Directives for Specialists (CanMEDS) roles and can inform course design, competency and curriculum development for physician and professional training programs related to disaster response and preparedness.

Quantifying Disaster Impacts on Local Public Health Agency's Leadership, Staffing, and Provision of Essential Public Health Services.

The objective of this study is to assess the impact that natural disaster response has on local health departments' (LHD) ability to continue to provide essential public health services. A web-based survey was sent to all North Carolina Local Health Directors. The survey asked respondents to report on LHD functioning following Hurricanes Florence (2018) and Dorian (2019). After Hurricane Florence, the positions who most frequently had regular duties postponed or interrupted were leadership (15 of 48; 31.3%), and professional staff (e.g., nursing and epidemiology: 11 of 48; 22.9%). Staffing shelters for all phases - from disaster response through long-term recovery - was identified as a burden by LHDs, particularly for nursing staff. Approximately 66.6% of LHD jurisdictions opened an Emergency Operations Center (EOC) or activated Incident Command System in response to both hurricanes. If an EOC was activated, the LHD was statistically, significantly more likely to report that normal duties had been interrupted across every domain assessed. The ability of LHDs to perform regular activities and provide essential public health services is impacted by their obligations to support disaster response. Better metrics are needed to measure the impacts to estimate indirect public health impacts of disasters.

SO-AODV

MANET has emerged as an eager field for supporting disaster response and prevention applications like climate and weather observation, tracking, tsunamis, wildfire and emergency rescue operations, underwater level navigation, etc. In this paper, a new ad-hoc routing protocol named SO-AODV (Secured and Optimized Ad-Hoc On-Demand Distance Vector) is proposed for secured and optimized communication in any disaster like situations. For shortest route selection a technique called pigeons swarm optimization (PiSO) is used that also leads to minimize the hop count in selected optimal route. Lightweight digital watermarking (LDW) is used that ensures the authenticity of “hello” packets. For security of event messages, cyphertext stealing technique (CST) is used with encryption qu-Vanstone elliptic curve cryptography (qV-ECC) based public key cryptography. The qV-ECC generates public key. Experiments are conducted using NS2 and performance is evaluated and compared over AODV for several metrics (i.e., packet delivery ration, throughput, end-to-end delay, security, and hop count).

Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities

Under normal circumstances, people’s homes and work locations are given by their addresses, and this information is used to create a disaster management plan in which there are instructions to individuals on how to evacuate. However, when a disaster strikes, some shelters are destroyed, or in some cases, distance from affected areas to the closest shelter is not reasonable, or people have no possibility to act rationally as a natural response to physical danger, and hence, the evacuation plan is not followed. In each of these situations, people tend to find alternative places to stay, and the evacuees in shelters do not represent the total number of the displaced population. Knowing the spatial distribution of total displaced people (including people in shelters and other places) is very important for the success of the response activities which, among other measures, aims to provide for the basic humanitarian needs of affected people. Traditional methods of people displacement estimation are based on population surveys in the shelters. However, conducting a survey is infeasible to perform at scale and provides low coverage, i.e., can only cover the numbers for the population that are at the shelters, and the information cannot be delivered in a timely fashion. Therefore, in this research, anonymized mobile Call Detail Records (CDRs) are proposed as a source of information to infer the spatial distribution of the displaced population by analyzing the variation of home cell-tower for each anonymized mobile phone subscriber before and after a disaster. The effectiveness of the proposed method is evaluated using remote-sensing-based building damage assessment data and Displacement Tracking Matrix (DTM) from an individual’s questionnaire survey conducted after a severe cyclone in Beira city, central Mozambique, in March 2019. The results show an encouraging correlation coefficient (over 70%) between the number of arrivals in each neighborhood estimated using CDRs and from DTM. In addition to this, CDRs derive spatial distribution of displaced populations with high coverage of people, i.e., including not only people in the shelter but everyone who used a mobile phone before and after the disaster. Moreover, results suggest that if CDRs data are available right after a disaster, population displacement can be estimated, and this information can be used for response activities and hence contribute to reducing waterborne diseases (e.g., diarrheal disease) and diseases associated with crowding (e.g., acute respiratory infections) in shelters and host communities.

Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: an impact analysis of Cyclones Idai and Kenneth in Mozambique

ObjectivesModelling and assessing the loss of geographical accessibility is key to support disaster response and rehabilitation of the healthcare system. The aim of this study was therefore to estimate postdisaster...

Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization.

Establishment of a Disaster Response Support System Based on Public-Private Partnerships - Focused on Cooperation with a Private Institution Using Drone -

Recently, drones are used widely in disaster areas, such as disaster relief and rescue, information collection and monitoring of affected areas. In particular, drones are highly useful to quickly identify the broader range of disaster damages. However, considering the specialized skills required for the operation of drones, training human resources and preparing equipment needs considerable time and budget. The public resources are not sufficient to monitor the damage, particularly in the case of large-scale disaster. This study aims at providing a new public-private cooperation model to support disaster response using private expertise and resources. In order to support disaster response during Typhoon Soulik in August 2018, we organized a collaborative team of emergency drone deployment based on the public-private cooperation and tested its efficacy. For a continuous operation and development of such activities based on public-private partnerships, increased efforts for institutionalization would be needed, including update of joint public-private organizations and operating systems, financial support, public relations, and routine training programs.

The need for scientific rigour and accountability in flood mapping to better support disaster response

Hydrological ProcessesVolume 33, Issue 24 p. 3138-3142 INVITED COMMENTARY The need for scientific rigour and accountability in flood mapping to better support disaster response Guy J.-P. Schumann, Corresponding Author Guy J.-P. Schumann gjpschumann@gmail.com orcid.org/0000-0003-0968-7198 School of Geographical Sciences, University of Bristol, Bristol, UK Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder, Colorado Remote Sensing Solutions, Inc., Barnstable, Massachusetts Correspondence Guy J.-P. Schumann, School of Geographical Sciences, University of Bristol, Bristol, UK. Email: gjpschumann@gmail.comSearch for more papers by this author Guy J.-P. Schumann, Corresponding Author Guy J.-P. Schumann gjpschumann@gmail.com orcid.org/0000-0003-0968-7198 School of Geographical Sciences, University of Bristol, Bristol, UK Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder, Colorado Remote Sensing Solutions, Inc., Barnstable, Massachusetts Correspondence Guy J.-P. Schumann, School of Geographical Sciences, University of Bristol, Bristol, UK. Email: gjpschumann@gmail.comSearch for more papers by this author First published: 04 July 2019 https://doi.org/10.1002/hyp.13547Citations: 6Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume33, Issue2430 November 2019Pages 3138-3142 This article also appears in:HPToday: Invited Commentaries RelatedInformation

Use of drones (unmanned aerial vehicles) for supporting emergency medical services in India

Background: Drone technology or correctly termed as unmanned aerial vehicles (UAVs) is the newest advancement of science which has taken the world by storm. India is yet to witness the fantastic work these exciting flying vehicles can do. There are innumerable applications within the domain of both private and public space, especially in health care and emergency medical response. Aerial professional drones are small, portable, and have advanced capabilities which include audio-video broadcast, GPS navigation capabilities, fully automated and predetermined flights, and use of artificial intelligence for decision-making. Methodology: A toy drone bought from an online retailer was fitted with a professional camera connected to portable tablet through WiFi. Two emergency physicians were trained to operate the drone and camera in a simulated disaster scenario, cardiac arrest scenario, as well as transportation of medicines and biological samples within the hospital. Proof of concept for three different applications of drone technology in emergency care was experimented. Observations: (1) Drone as the first response in disaster: The drone was able to fly unhindered, hover over the accident location, and relay details of the crash site, including type of accident, hazards present, number of injured victims, roads leading to/from the location, and the number of bystanders present. (2) Supervision of patient transfers in disaster: The drone was able to send live video feed continuously as each patient was being evacuated. The drone pilots were in constant touch with disaster command and control. (3) Cordoning the disaster site: Based on the drone video feed, the ingress/egress routes for the ambulance vehicles were identified and secured. (4) Basic life support: The drone was able to provide live views of arrest scenarios, get live visuals from the site to the copilot and helped in guiding the first responder for cardiopulmonary resuscitation in a victim of cardiac arrest. (5) Transport of samples: The drone was able to transport samples for the stroke patient from the emergency room to the laboratory in the hospital without any hindrance. (6) Intrahospital transport of medicines: The drones were also able to carry medicines from the inpatient pharmacy to the wards, which were immediate, effective, and with reduction in workforce. Conclusion: This project was clear example of seamless integration of flying drones into existing disaster response protocols. Drones can provide real-time video information about disaster/accident site to the disaster command and control. Drones can supervise the transfer of individual victims from the disaster zone. Emergency physicians can learn to fly drones and use them for supporting emergency medical services disaster response. The DGCA should facilitate the utilization of drones and UAVs for supporting emergency medical personnel when they respond to disasters (natural, chemical, biological, radiation, and nuclear), multicasualty incidents, road accidents, cardiac arrest victims, in hospital use for transport of medicines, samples, and out-of-hospital transport of organs for transplantation.

Damage Detection Method for Buildings with Machine-Learning Techniques Utilizing Images of Automobile Running Surveys Aftermath of the 2016 Kumamoto Earthquake

In order to understand the damage situation immediately after the occurrence of a disaster and to support disaster response, we developed a method to classify the degree of building damage in three stages with machine-learning using road-running survey images obtained immediately after the Kumamoto earthquake. Machine-learning involves a learning phase and a discrimination phase. As training data, we used images from a camera installed in the travel direction of an automobile, in which the degree of damage was visually categorized. In the learning phase, class separation is carried out by support vector machine (SVM) on a basis of a feature calculated from training patch images for each extracted damage category. In the discrimination phase, input images are provided with raster scan so that the class separation is carried out in units of the patch image. In this manner, learning, discrimination, and parameter tuning are repeated. By doing so, we developed a damage-discrimination model for each patch image and validated the discrimination accuracy using a cross-validation method. Furthermore, we developed a method using an optical flow for preventing double counting of damaged areas in cases where an identical building is captured in multiple photos.