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This book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future-oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.

The 7th EMB Forum took place online on 23rd October 2020. It provided an opportunity to further reflect on the role of big data in advancing marine science to support recent policy developments, namely the European Green Deal, the EU 2030 Biodiversity Strategy, and the development of a Digital Twin Ocean (DTO). 192 participants from Europe and further afield were present, representing academia, government, industry, and NGOs. Experts gave presentations and participated in panel discussions, with audience interaction. This document is a summary of the discussions, and full recordings of the sessions are available on the EMB YouTube Channel.

"The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student's learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom."--Provided by publisher.

The provision and dissemination of Open Data is a flourishing concept, which is highly recognized and established in the government and public administrations domains. Typically, the actual data is served as static file downloads, such as CSV or PDF, and the established software solutions for Open Data are mostly designed to manage this kind of data. However, the rising popularity of the Internet of things and smart devices in the public and private domain leads to an increase of available real-time data, like public transportation schedules, weather forecasts, or power grid data. Such timely and extensive data cannot be used to its full potential when published in a static, file-based fashion. Therefore, we designed and developed Ronda - an open source platform for gathering, processing and publishing real-time Open Data based on industry-proven and established big data and data processing tools. Our solution easily enables Open Data publishers to provide real-time interfaces for heterogeneous data sources, fostering more sophisticated and advanced Open Data use cases. We have evaluated our work through a practical application in a production environment.

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

蹂닿굔�븰怨� ; The rapid development of medical technology and information and communication technology (ICT) in Korea has led to the accumulation of vast amounts of information related to healthcare. The National Health Insurance Service (NHIS) and Health Insurance Review & Assessment Service (HIRA) in Korea collect and store health insurance claims data. Despite the excitement and recent interest in healthcare big data, few empirical studies have been conducted to evaluate the potential value of health insurance claims data. The following three methods were used to suggest strategies for optimal utilization of Korean health insurance claims data. First, Systematic Review was conducted of published studies related to Korean Health Insurance Claims Data. The PubMed and Cochrane database searches from 2007 to 2017. A total of 478 studies were included in the study after applying duplication and elimination criteria to the initial 3,951 search results. Second, comparative analysis was conducted to draw implications for using Korean Health Insurance Claims Big Data across countries (US, UK, Australia and Taiwan). Cross-country comparisons were performed based on horizontal as well as vertical comparison perspectives. Data analysis consisted of the constant comparison method. Third, a Delphi survey was conducted to 42 healthcare professionals working in National Health Insurance Service (NHIS), Health Insurance Review & Assessment Service (HIRA), and relevant agencies. The questionnaire content was intended to identify the obstacles to and policy priorities for the safe use of Health Insurance claims data. This study questionnaire was approved by the IRB Institutional Review Board at Yonsei University (IRB: 2-1040939-AB-N-01-2014-228). The results of the three methods of this study are as follows. First, 478 studies were selected as a result of systematic review. There were 55 studies (11.5%) between 2007 and 2012, and a total of 423 (88.5%) were found over the past five years (2013��2017). The HIRA database was used a little more often than NHIS database (HIRA: 51.9%, NHIS: 47.5%). The most frequent research type was health service utilization (41.4%), and 29 (6.9%) out of 478 cases were connected with external data. These data include the information from the cause of death data (12, 41.4 %), clinical data (9, 31.0%), cancer data (7, 24.1%), cost data (6, 20.7%), Surveillance data (2, 6.9%), other data (3, 10.3%). Second, this study shows the implications for policies in Korea through comparison of the big data utilization in the major countries. The experience of developed countries suggests important issues to be reflected in the formulation of strategies for national utilization of healthcare data; there is a national strategy and health and data governance was being built, it focuses on utilization of public interest objectives such as improvement of public health and medical quality, there is a balance between strengthening and balancing privacy and data security. Third, 13 policies that indicate four obstacles were included through the Delphi survey. Participants responded by rating the four obstacles in this order: legal immaturity for data use, lack of consensus on providing information, technical constraints on information sharing, and lack of government support. Policy priorities include policy for the �쐏atient�셲 consent to data use,�� a policy for legal revision for Health Insurance Big Data utilization, an institutional improvement policy for Health Insurance Big Data utilization, an institutional consent policy for data provision, technical privacy policies such as anonymization for data sharing, and a national governance establishment policy for health insurance claims data utilization. Finally, three strategies have been proposed for each issue derived from the three methodologies. First, it is necessary to establish �쏯ational Big Data Governance�� for the successful utilization of health related big data. Second, it is necessary to develop legal institutional guidelines in the framework of the separate big data law (differentiation of personal information consent, development of legal and institutional guidelines). The method of consent should be improved to resolve the dilemma whereby utilizing and protecting personal information. Third, it is a strategy to revitalize healthcare research for big data linkage (development of personal information protection technology for data linkage, utilization of user - centered health insurance claim data). Although Korea is aware of the global trends of big data, negative opinions are still common about the view that the use of personal information is inevitable to improve the quality of life through public well-being and public health promotion. Clear legislative and institutional grounds for the use of Health Insurance Big Data are needed and government support for the proposed policy recommendations should be established. ; open ; 諛뺤궗

PurposeThis paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents.Design/methodology/approachBased on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency.FindingsThe authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents.Research limitations/implicationsThis study is limited to Twitter data and one case. Our conceptual model needs to be operationalised.Practical implicationsComACom will improve decision-making to minimise human errors in maritime accidents.Social implicationsEmergency response will be improved by including the voices of the wider community.Originality/valueComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom Data science for whom Data science with whose interests in mind The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

PurposeThe purpose of this study is to empirically examine the direct effect of extra-industry network (EIN) and organization–stakeholder relationships (OSR) on absorptive capacity (ACAP). In addition, this study explored indirect effects of EIN and OSR on performance through ACAP among small- and medium-sized enterprises (SMEs) in Oman by considering the moderating role of big data analytics (BDA) outsourcing.Design/methodology/approachThis study utilized quantitative method through survey questionnaire. The hypotheses were tested with a sample size of 202 surveys completed by SME owners. Partial least squares-structural equation modeling (PLS-SEM) was administered to analyze data via the SmartPLS 4.0 software.FindingsThe analysis revealed that EIN and OSR had an indirect effect on performance through ACAP, while propensity to outsource BDA was found to have a positive moderating role between EIN and performance. Interestingly, propensity to outsource BDA was found to have a negative moderating influence on the relationship between ACAP and performance.Practical implicationsThis research is beneficial for entrepreneurs who wish to learn about the specific intangible resources significant for venture growth, to devise effective strategies to expand their EIN and OSR and to consider the significance of the correlations established in this study through ACAP. The result also assists managers in a way that the propensity to outsource BDA strengthens the positive effect of EIN on performance and weakens the positive effect of ACAP on performance.Originality/valueThis research appears to be among the first empirical studies that attempt to provide insights into the importance of ACAP as the key mechanisms to transform the advantages of EIN and OSR to enhance performance by considering the moderating role of propensity to outsource BDA.

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people's use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: (in)visibility, (dis)engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract.