A review of challenges and solutions in adopting a participatory geographical information system for disaster management

Disaster management is a critical component in mitigating the impacts of various natural, and other disasters such as floods, cyclones, forest fires, earthquakes, disease spreading etc. The primary aim of disaster management in a specific region is to empower the local neighborhood to higher determine its natural danger instincts and therefore migrate closer to options for lowering that risk. Conventional techniques of disaster management are majorly driven by the quantitative information collected from various events. Some of the recent techniques have used more advanced data-driven and non-linear approaches such as machine learning, and spatial analysis tools such as GIS for making more informed decisions. However, these techniques cannot often represent the dynamics of demographic units, and event impact in small regions due to a multitude of reasons such as lack of data, equipment, more generalized approaches, etc. Participatory Geographic Information System (PGIS) overcomes some of the limitations present in the traditional techniques by incorporating local communities as stakeholders in making various policies, distributing risk information etc. PGIS has been adopted in various fields such as land cover planning, agriculture information systems, data collection systems etc. Other than these applications, the effectiveness of PGIS in disaster management in handling various natural disasters such as floods, cyclones, forest fires, and disease spread has been demonstrated in several studies. However, in many places, PGIS is not yet evolved and its implementation is still at the infancy level due to several reasons. Despite many advantages, PGIS presents many problems comprising insufficient infrastructure, training facilities, engagement and education of the community members towards a combined decision, etc. therefore provision of necessary infrastructure can improve the overall impact of implementing PGIS. Involving the local community and educating them on the right approach for the success of PGIS is a complex task. Further, the conflict of opinions between technical personnel and locals can be another factor that limits the usage. However, from the results of various studies, the advantages of PGIS implementation can outweigh the limitations of implementation

Exploring good practice knowledge transfer related to post tsunami housing re-construction in Sri Lanka

Sri Lanka was badly affected by the tsunami that occurred on 26th December 2004. The tsunami destroyed about two-thirds of the Sri Lankan coastline and affected more than 1,000,000 people. It does not only affected the lives of the community, but also had a devastating effect on their housing and livelihoods. The overall loss of 100,000 or more houses due to the tsunami proved to be a major challenge to the emergency response teams and disaster planners. Although several major disasters of varying magnitudes have occurred in the world, the body of knowledge related to post-disaster housing reconstruction and rehabilitation appears fragmented and poorly integrated. This paper attempts to fill this theoretical gap by focusing on the extent to which good practice knowledge transfer helps in overcoming this problem for more effective and efficient delivery of post-tsunami housing in Sri Lanka. The paper applied knowledge transfer principles within the context of the two housing reconstruction strategies employed in post-tsunami housing reconstruction in Sri Lanka; namely donor-driven housing and owner driven housing. The results of this study reveal that the knowledge transfer within this context cannot be simply copied and inserted from one context without any localisation. Therefore, the paper proposes a high-level abstraction of the core principles of community engagement through participatory techniques associated with appropriate capacity and capability building techniques that will enable the various stakeholders to create a new application to suit the appropriate context of the transfer destination (post-tsunami context in Sri Lanka)

Post-Disaster Housing Reconstruction in Sri Lanka: What Methodology?

Research methodology is the procedural framework within which the research is conducted. This includes the overall approach to a problem that could be put into practice in a research process, from the theoretical underpinning to the collection and analysis of data. Choice of methodology depends on the primary drivers: topic to be researched and the specific research questions. Hence, methodological perspectives of managing stakeholder expectations of PDHR context are composed of research philosophies, research strategy, research design, and research techniques. This research belonged to social constructivism or interpretivism within a philosophical continuum. The nature of the study was more toward subjectivism where human behavior favored voluntary stance. Ontological, methodological, epistemological, and axiological positioning carried the characteristics of idealism, ideographic, anti-positivism, and value laden, respectively. Data collection comprises two phases, preliminary and secondary. Exploratory interviews with construction experts in the United Kingdom and Sri Lanka were carried out to refine the interview questions and identify the case studies. Case study interviews during the secondary phase took place in Sri Lanka. Data collected at the preliminary stage were used to assess the attributes of power, legitimacy/proximity, and urgency of stakeholders to the project using Stakeholder Circle™ software. Moreover, the data collected at secondary phase via case studies will be analyzed with NVivo 8. This article aims to discuss these methodological underpinnings in detail applied in a post-disaster housing reconstruction context in Sri Lanka

A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages

Natural disasters have demonstrated the crucial role of social media before, during and after emergencies (Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4 the use of social media in enhancing the response of disaster-related agen-cies. To this end, we have collected corpora of social and formal media to study newsroom communication of emergency management organisations in English and Italian. Currently, emergency management agencies in English-speaking countries use social media in different measure and different degrees, whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed with a view to identifying communicative strategies and detecting sentiment in order to distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis uses humans to classify alert/warning messages or emer-gency response and mitigation ones based on the terminology used and the sentiment expressed. Results of linguistic analysis are then used to train an application by tagging messages and detecting disaster- and/or emergency-related terminology and emotive language to simulate human rating and forward information to an emergency management system

Event-Cloud Platform to Support Decision- Making in Emergency Management

The challenge of this paper is to underline the capability of an Event-Cloud Platform to support efficiently an emergency situation. We chose to focus on a nuclear crisis use case. The proposed approach consists in modeling the business processes of crisis response on the one hand, and in supporting the orchestration and execution of these processes by using an Event-Cloud Platform on the other hand. This paper shows how the use of Event-Cloud techniques can support crisis management stakeholders by automatizing non-value added tasks and by directing decision- makers on what really requires their capabilities of choice. If Event-Cloud technology is a very interesting and topical subject, very few research works have considered this to improve emergency management. This paper tries to fill this gap by considering and applying these technologies on a nuclear crisis use-case

Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

Development of a fusion adaptive algorithm for marine debris detection within the post-Sandy restoration framework

Recognition of marine debris represent a difficult task due to the extreme variability of the marine environment, the possible targets, and the variable skill levels of human operators. The range of potential targets is much wider than similar fields of research such as mine hunting, localization of unexploded ordnance or pipeline detection. In order to address this additional complexity, an adaptive algorithm is being developing that appropriately responds to changes in the environment, and context. The preliminary step is to properly geometrically and radiometrically correct the collected data. Then, the core engine manages the fusion of a set of statistically- and physically-based algorithms, working at different levels (swath, beam, snippet, and pixel) and using both predictive modeling (that is, a high-frequency acoustic backscatter model) and phenomenological (e.g., digital image processing techniques) approaches. The expected outcome is the reduction of inter-algorithmic cross-correlation and, thus, the probability of false alarm. At this early stage, we provide a proof of concept showing outcomes from algorithms that dynamically adapt themselves to the depth and average backscatter level met in the surveyed environment, targeting marine debris (modeled as objects of about 1-m size). The project relies on a modular software library, called Matador (Marine Target Detection and Object Recognition)