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Der Sammelband vereint Beiträge von führenden Forscherinnen und Forschern im Bereich statistischer Methoden und deren Anwendung in den Sozialwissenschaften mit einem besonderen Fokus auf sozialen Räumen. Multivariate Skalierungsmethoden für kategoriale Daten, speziell Korrespondenzanalyse, werden verwendet um die wichtigsten Dimensionen aus komplexen Kreuztabellen mit vielen Variablen zu extrahieren und Zusammenhänge in den Daten bildlich darzustellen. In diesem Band werden statistische Weiterentwicklungen, grundsätzliche methodologische Überlegungen und empirische Anwendungen multivariater Analysemethoden diskutiert. Mehrere Anwendungsbeispiele thematisieren verschiedene Aspekte des Raumes und deren soziologische Bedeutung: die Rekonstruktion „sozialer Räume“ mit statistischen Methoden, die Illustration räumlicher Beziehungen zwischen Nähe, Distanz und Ungleichheit, aber auch konkrete Interaktionen in urbanen Räumen. Der Band erscheint zur Würdigung der wissenschaftlichen Leistungen von Prof. Jörg Blasius.
Disasters threaten all parts of the world and they appear to be increasing in frequency, scale and intensity. Despite huge improvements in the emergency response, permanent reconstruction is often uncoordinated, inefficiently managed and slow to begin. International agencies are geared to an efficient response in terms of humanitarian relief, but they are not well versed in the requirements of long-term reconstruction, which is often constrained by lack of planning and poorly coordinated management. The construction industry is typically engaged in a range of critical activities after a disaster, including provision of temporary shelter in the immediate aftermath and restoration of permanent shelter and public infrastructure once the immediate humanitarian needs have been attended to. Post-Disaster Reconstruction of the Built Environment identifies the challenges that face the industry and highlights best practice to enable the construction industry to address those problems which make an effective response to these unexpected events difficult. Written by an international team of experts, this book will help researchers and advanced students of construction understand the problems faced by communities and the construction industry when faced with a natural or man-made disaster, and identify the planning and management processes required by the industry to mount an effective response.
A full description of computer-based methods of analysis used to define and solve ecological problems. Multivariate techniques permit summary of complex sets of data and allow investigation of many problems which cannot be tackled experimentally because of practical restraints.
This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research.
Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of “omics” and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients’ pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system’s predictive power to patient-specific profiling.
Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.