Data DNA: The Next Generation of Statistical Metadata

Describes the components of a complete statistical metadata system and suggests ways to create and structure metadata for better access and understanding of data sets by diverse users

Performance analysis and coverage optimization methods for a digital radio system in the VHF bands

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Improving disaster response evaluations : Supporting advances in disaster risk management through the enhancement of response evaluation usefulness

Future disasters or crises are difficult to predict and therefore hard to prepare for. However, while a specific event might not have happened, it can be simulated in an exercise. The evaluation of performance during such an exercise can provide important information regarding the current state of preparedness, and used to improve the response to future events. For this to happen, evaluation products must be perceived as useful by the end user. Unfortunately, it appears that this is not the case. Both evaluations and their products are rarely used to their full extent or, in extreme cases, are regarded as paper-pushing exercises.The first part of this research characterises current evaluation practice, both in the scientific literature and in Dutch practice, based on a scoping study, document and content analyses, and expert judgements. The findings highlight that despite a recent increase in research attention, few studies focus on disaster management exercise evaluation. It is unclear whether current evaluations achieve their purpose, or how they contribute to disaster preparedness. Both theory and practice tend to view, and present evaluations in isolation. This limited focus creates a fragmented field that lacks coherence and depth. Furthermore, most evaluation documentation fails to justify or discuss the rational underlying the selected methods, and their link to the overall purpose or context of the exercise. The process of collecting and analysing contextual, evidence-based data, and using it to reach conclusions and make recommendations lacks methodological transparency and rigour. Consequently, professionals lack reliable guidance when designing evaluations.Therefore, the second part of this research aimed to gain an insights into what make evaluations useful, and suggest improvements. In particular, it highlights the values associated with the methodology used to record and present evaluation outcomes to end users. The notion of an ‘evaluation description’ is introduced to support the identification of four components that are assumed to influence the usefulness of an evaluation: its purpose, object description, analysis and conclusion. Survey experiments identified that how these elements – notably, the analysis and/ or conclusions – are documented significantly influences the usefulness of the product. Furthermore, different components are more useful depending on the purpose of the report (for learning or accountability). Crisis management professionals expect the analysis to go beyond the object of the evaluation, and focus on the broader context. They expect a rigorous evaluation to provide them with evidence-based judgements that deliver actionable conclusions and support future learning.Overall, this research shows that the design and execution of evaluations should provide systematic, rigorous, evidence-based and actionable outcomes. It suggests some ways to manage both the process and the products of an evaluation to improve its usefulness. Finally, it underlines that it is not the evaluation itself that leads to improvement, but its use. Evaluation should, therefore, be seen as a means to an end

Remote sensing-based proxies for urban disaster risk management and resilience: A review

© 2018 by the authors. Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Advanced deep neural networks for speech separation and enhancement

Ph. D. Thesis.Monaural speech separation and enhancement aim to remove noise interference from the noisy speech mixture recorded by a single microphone, which causes a lack of spatial information. Deep neural network (DNN) dominates speech separation and enhancement. However, there are still challenges in DNN-based methods, including choosing proper training targets and network structures, refining generalization ability and model capacity for unseen speakers and noises, and mitigating the reverberations in room environments. This thesis focuses on improving separation and enhancement performance in the real-world environment. The first contribution in this thesis is to address monaural speech separation and enhancement within reverberant room environment by designing new training targets and advanced network structures. The second contribution to this thesis is on improving the enhancement performance by proposing a multi-scale feature recalibration convolutional bidirectional gate recurrent unit (GRU) network (MCGN). The third contribution is to improve the model capacity of the network and retain the robustness in the enhancement performance. A convolutional fusion network (CFN) is proposed, which exploits the group convolutional fusion unit (GCFU). The proposed speech enhancement methods are evaluated with various challenging datasets. The proposed methods are assessed with the stateof-the-art techniques and performance measures to confirm that this thesis contributes novel solution

Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156