In a world of increasing mobility, how people of different cultures live together is a key issue of our age, especially for those responsible for planning and running cities. New thinking is needed on how diverse communities can cooperate in productive harmony instead of leading parallel or antagonistic lives. Policy is often dominated by mitigating the perceived negative effects of diversity, and little thought is given to how adiversity dividend or increased innovative capacity might be achieved. The Intercultural City, based on numerous case studies worldwide, analyses the links between urban change and cultural diversity. It draws on original research in the US, Europe, Australasia and the UK. It critiques past and current policy and introduces new conceptual frameworks. It provides significant and practical advice for readers, with new insights and tools for practitioners such as theintercultural lensindicators of opennessurban cultural literacy andten steps to an Intercultural City. Published with Comedia.
This book is written to serve as a general reference for biologists and resource managers with relatively little statistical training. It focuses on both basic concepts and practical applications to provide professionals with the tools needed to assess monitoring methods that can detect trends in populations. It combines classical finite population sampling designs with population enumeration procedures in a unified approach for obtaining abundance estimates for species of interest. The statistical information is presented in practical, easy-to-understand terminology. - Presented in practical, easy-to-understand terminology - Serves as a general reference for biologists and resource managers - Provides the tools needed to detect trends in populations - Introduces a unified approach for obtaining abundance estimates
Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
Sampling consists of selection, acquisition, and quantification of a part of the population. While selection and acquisition apply to physical sampling units of the population, quantification pertains only to the variable of interest, which is a particular characteristic of the sampling units. A sampling procedure is expected to provide a sample that is representative with respect to some specified criteria. Composite sampling, under idealized conditions, incurs no loss of information for estimating the population means. But an important limitation to the method has been the loss of information on individual sample values, such as, the extremely large value. In many of the situations where individual sample values are of interest or concern, composite sampling methods can be suitably modified to retrieve the information on individual sample values that may be lost due to compositing. This book presents statistical solutions to issues that arise in the context of applications of composite sampling.
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