Trialing Innovative Technologies in Crisis Management - “Airborne and Terrestrial Situational Awareness” as Support Tool in Flood Response

Flooding represents the most-occurring and deadliest threats worldwide among natural disasters. Consequently, new technologies are constantly developed to improve response capacities in crisis management. The remaining challenge for practitioner organizations is not only to identify the best solution to their individual demands, but also to test and evaluate its benefit in a realistic environment before the disaster strikes. To bridge the gap between theoretic potential and actual integration into practice, the EU-funded project DRIVER+ has designed a methodical and technical environment to assess innovation in a realistic but non-operational setup through trials. The German Aerospace Center (DLR) interdisciplinary merged mature technical developments into the “Airborne and terrestrial situational awareness” system and applied it in a DRIVER+ Trial to promote a sustainable and demand-oriented R&D. Experienced practitioners assessed the added value of its modules “KeepOperational” and “ZKI” in the context of large-scale flooding in urban areas. The solution aimed at providing contextual route planning in police operations and extending situational awareness based on information derived through aerial image processing. The user feedback and systematically collected data through the DRIVER + Test-bed approved that DLR’s system could improve transport planning and situational awareness across organizations. However, the results show a special need to consider, for example, cross-domain data-fusion techniques to provide essential 3D geo-information to effectively support specific response tasks during flooding

Collaboration among humanitarian relief organizations and volunteer technical communities : identifying research opportunities and challenges through a systematic literature review

Collaboration is the foundation to strengthen disaster preparedness and for effective emergency response actions at all levels. Some studies have highlighted that remote volunteers, i.e., volunteers supported by Web 2.0 technologies, possess the potential to strengthen humanitarian relief organizations by offering information regarding disaster-affected people and infrastructure. Although studies have explored various aspects of this topic, none of those provided an overview of the state-of-the-art of researches on the collaboration among humanitarian organizations and communities of remote volunteers. With the aim of overcoming this gap, a systematic literature review was conducted on the existing research works. Therefore, the main contribution of this work lies in examining the state of research in this field and in identifying potential research gaps. The results show that most of the research works addresses the general domain of disaster management, whereas only few of them address the domain of humanitarian logistics. Collaboration among Humanitarian Relief Organizations and Volunteer Technical Communities: Identifying Research Opportunities and Challenges through a Systematic Literature Review (PDF Download Available). Available from: https://www.researchgate.net/publication/315790817_Collaboration_among_Humanitarian_Relief_Organizations_and_Volunteer_Technical_Communities_Identifying_Research_Opportunities_and_Challenges_through_a_Systematic_Literature_Review [accessed May 26, 2017]

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

THE NETTED HUMANITARIAN: IMPROVING THE INFORMATION AND COMMUNICATIONS TECHNOLOGY ASSESSMENT PROCESS FOR HUMANITARIAN ASSISTANCE/DISASTER RELIEF (HA/DR) MISSIONS C

Setting up reliable communications after a disaster is an extremely difficult yet crucial requirement for coordinating relief efforts. Lessons learned from recent humanitarian disasters point to major difficulties in establishing communications to quickly and accurately assess responder needs in the affected area and communicating this information to first responders from government relief agencies, Non-Governmental Organizations (NGOs) and the military. The militarys growing role in Humanitarian Assistance/Disaster Relief (HA/DR) missions makes it imperative that military first responders coordinate with partner relief groups to improve the current assessment and relief processes. The current assessment and relief processes are generally ineffective due to inherent mistrust between responding organizations and lack of experience working together in a chaotic and time-pressed environment. This research analyzes issues with the current processes utilized by responders in HA/DR missions and researches if Netted Humanitarian responders trained in the set up/operation of Hastily Formed Networks (HFN) equipment can improve the process of assessing the ICT situation on the ground following a disaster. It further delves into the possibility that these highly trained individuals will expedite overcoming the communications vacuum that immediately follows a natural disaster, and will be beneficial in coordinating the combined recovery efforts of responding organizations.http://archive.org/details/thenettedhumanit1094532832Lieutenant, United States NavyApproved for public release; distribution is unlimited

Geospatial crowdsourced data fitness analysis for spatial data infrastructure based disaster management actions

The reporting of disasters has changed from official media reports to citizen reporters who are at the disaster scene. This kind of crowd based reporting, related to disasters or any other events, is often identified as 'Crowdsourced Data' (CSD). CSD are freely and widely available thanks to the current technological advancements. The quality of CSD is often problematic as it is often created by the citizens of varying skills and backgrounds. CSD is considered unstructured in general, and its quality remains poorly defined. Moreover, the CSD's location availability and the quality of any available locations may be incomplete. The traditional data quality assessment methods and parameters are also often incompatible with the unstructured nature of CSD due to its undocumented nature and missing metadata. Although other research has identified credibility and relevance as possible CSD quality assessment indicators, the available assessment methods for these indicators are still immature. In the 2011 Australian floods, the citizens and disaster management administrators used the Ushahidi Crowd-mapping platform and the Twitter social media platform to extensively communicate flood related information including hazards, evacuations, help services, road closures and property damage. This research designed a CSD quality assessment framework and tested the quality of the 2011 Australian floods' Ushahidi Crowdmap and Twitter data. In particular, it explored a number of aspects namely, location availability and location quality assessment, semantic extraction of hidden location toponyms and the analysis of the credibility and relevance of reports. This research was conducted based on a Design Science (DS) research method which is often utilised in Information Science (IS) based research. Location availability of the Ushahidi Crowdmap and the Twitter data assessed the quality of available locations by comparing three different datasets i.e. Google Maps, OpenStreetMap (OSM) and Queensland Department of Natural Resources and Mines' (QDNRM) road data. Missing locations were semantically extracted using Natural Language Processing (NLP) and gazetteer lookup techniques. The Credibility of Ushahidi Crowdmap dataset was assessed using a naive Bayesian Network (BN) model commonly utilised in spam email detection. CSD relevance was assessed by adapting Geographic Information Retrieval (GIR) relevance assessment techniques which are also utilised in the IT sector. Thematic and geographic relevance were assessed using Term Frequency – Inverse Document Frequency Vector Space Model (TF-IDF VSM) and NLP based on semantic gazetteers. Results of the CSD location comparison showed that the combined use of non-authoritative and authoritative data improved location determination. The semantic location analysis results indicated some improvements of the location availability of the tweets and Crowdmap data; however, the quality of new locations was still uncertain. The results of the credibility analysis revealed that the spam email detection approaches are feasible for CSD credibility detection. However, it was critical to train the model in a controlled environment using structured training including modified training samples. The use of GIR techniques for CSD relevance analysis provided promising results. A separate relevance ranked list of the same CSD data was prepared through manual analysis. The results revealed that the two lists generally agreed which indicated the system's potential to analyse relevance in a similar way to humans. This research showed that the CSD fitness analysis can potentially improve the accuracy, reliability and currency of CSD and may be utilised to fill information gaps available in authoritative sources. The integrated and autonomous CSD qualification framework presented provides a guide for flood disaster first responders and could be adapted to support other forms of emergencies

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data

Mining Behavior of Citizen Sensor Communities to Improve Cooperation with Organizational Actors

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including \u27articulation\u27 and \u27awareness\u27, but Web 2.0 supported CSC offers new challenges as well as opportunities. A CSC presents information overload for the organizational actors, especially in finding reliable information providers (for awareness), and finding actionable information from the data generated by citizens (for articulation). Also, we note three data level challenges: ambiguity in interpreting unconstrained natural language text, sparsity of user behaviors, and diversity of user demographics. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues. I present a novel web information-processing framework, called the Identify-Match- Engage (IME) framework. IME allows operationalizing computation in design problems of awareness and articulation of the cooperative system between citizens and organizations, by addressing data problems of group engagement modeling and intent mining. The IME framework includes: a.) Identification of cooperation-assistive intent (seeking-offering) from short, unstructured messages using a classification model with declarative, social and contrast pattern knowledge, b.) Facilitation of coordination modeling using bipartite matching of complementary intent (seeking-offering), and c.) Identification of user groups to prioritize for engagement by defining a content-driven measure of \u27group discussion divergence\u27. The use of prior knowledge and interplay of features of users, content, and network structures efficiently captures context for computing cooperation-assistive behavior (intent and engagement) from unstructured social data in the online socio-technical systems. Our evaluation of a use-case of the crisis response domain shows improvement in performance for both intent classification and group engagement prioritization. Real world applications of this work include use of the engagement interface tool during various recent crises including the 2014 Jammu and Kashmir floods, and intent classification as a service integrated by the crisis mapping pioneer Ushahidi\u27s CrisisNET project for broader impact

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Emergency operations center organizational structure during disasters a qualitative study

This dissertation explores the State of Oklahoma Emergency Operations Center (EOC) organizational structure before and during disaster response. I utilized the lens of contingency theory to review prior EOC research and linked it with mechanistic and organic structures. After the review of literature, I developed the following research question: in what ways is a state-level emergency operations center (EOC) mechanistic or organic, and how do these organizational constructs influence disaster response?I utilized two methodological analyses to answer this research question. Content analysis is the first methodology I employed. Subsequently I analyzed three training documents from the federal government and one planning document from the State of Oklahoma. My analysis of the four documents revealed: 1) EOC organization swings between mechanistic and organic, 2) there are hidden organic structural elements, and 3) staff networking is essential. To better explore these three concepts, I conducted a second methodology involving semi-structured qualitative interviews. I used grounded theory methodology (GTM) and data driven codes to interpret the interview data. Interview results demonstrated 1) EOCs are dynamic organizations, 2) environmental cues are vital to staff completing their job, and 3) staff networking leads to relationship building and trust.Utilizing the literature I reviewed and the two qualitative analyses I conducted, I arrived at four suggestions: 1) training documents should illustrate EOC structure as dynamic, 2) networking among staff allows for trust and coordination, 3) staff refine their role during disasters and throughout the next disaster, and 4) staff must learn the hidden organic elements of the EOC. I combined all these elements into suggestions for future scholarly research on EOCs