Subdivided


Book Description

Using Toronto as a case study, Subdivided asks how cities would function if decision-makers genuinely accounted for race, ethnicity, and class when confronting issues such as housing, policing, labor markets, and public space. With essays contributed by an array of city-builders, it proposes solutions for fully inclusive communities that respond to the complexities of a global city. Jay Pitter is a writer and professor based in Toronto. She holds a Masters in Environmental Studies from York University. John Lorinc is a Toronto-based journalist who writes about urban affairs, politics, and business. He co-edited The Ward: The Life and Loss of Toronto's First Immigrant Neighbourhood (Coach House, 2015).




Maps for America


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Parliamentary Papers


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A Handbook of Asia Minor


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Reports by the Juries


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Genetic Structure and Selection in Subdivided Populations (MPB-40)


Book Description

Various approaches have been developed to evaluate the consequences of spatial structure on evolution in subdivided populations. This book is both a review and new synthesis of several of these approaches, based on the theory of spatial genetic structure. François Rousset examines Sewall Wright's methods of analysis based on F-statistics, effective size, and diffusion approximation; coalescent arguments; William Hamilton's inclusive fitness theory; and approaches rooted in game theory and adaptive dynamics. Setting these in a framework that reveals their common features, he demonstrates how efficient tools developed within one approach can be applied to the others. Rousset not only revisits classical models but also presents new analyses of more recent topics, such as effective size in metapopulations. The book, most of which does not require fluency in advanced mathematics, includes a self-contained exposition of less easily accessible results. It is intended for advanced graduate students and researchers in evolutionary ecology and population genetics, and will also interest applied mathematicians working in probability theory as well as statisticians.