Economic development and urban growth are the contested grounds of urban politics. Business elites and politicians tend to forge "pro-growth" coalitions centered around downtown development while progressive and neighborhood activists counter with a more balanced approach that features a strong neighborhood component. Urban politics is often shaped by this conflict, which has intellectual as well as practical dimensions. In some cities, neighborhood interests have triumphed; in others, the pro-growth agenda has prevailed. In this illuminating comparative study, Barbara Ferman demonstrates why neighborhood challenges to pro-growth politics were much more successful in Pittsburgh than they were in Chicago. Operating largely in the civic arena, Pittsburgh's neighborhood groups encountered a political culture and institutional structure conducive to empowering neighborhood progressivism in housing and economic development policymaking. In contrast, the pro-growth agenda in Chicago was challenged in the electoral arena, which was dominated by machine, ward-based politicians who regarded any independent neighborhood organizing as a threat. Consequently, neighborhood demands for policymaking input were usually thwarted. Besides revealing why the development policies of two important American cities diverged, Ferman's unique comparative approach to this issue significantly expands the scope of urban analysis. Among other things, it provides the first serious study to incorporate the civic sector-neighborhood politics-as an important component of urban regimes. Ferman also emphasizes institutional and cultural factors-often ignored or relegated to residual roles in other studies-and expounds on their influence in shaping local politics and policy. To add an analytical and normative dimension to urban analysis, she focuses on the "non-elite" actors, not just the economic and political elites who compose governing coalitions. Ultimately, Ferman takes a more holistic and balanced view of large cities than is typical for urban studies as she argues that neighborhoods are an important, integral part of what cities are and can be. For that reason especially, her work will have a profound impact upon our understanding of urban politics.

Two decades after Harvey Molotch’s “city as a growth machine,” this book offers a unique, critical assessment of his thesis.

The Encyclopedia of Community is a major four volume reference work that seeks to define one of the most widely researched topics in the behavioural and social sciences. Community itself is a concept, an experience, and a central part of being human. This pioneering major reference work seeks to provide the necessary definitions of community far beyond the traditional views.

Just as investors want the companies they hold equity in to do well, homeowners have a financial interest in the success of their communities. If neighborhood schools are good, if property taxes and crime rates are low, then the value of the homeowner’s principal asset—his home—will rise. Thus, as William Fischel shows, homeowners become watchful citizens of local government, not merely to improve their quality of life, but also to counteract the risk to their largest asset, a risk that cannot be diversified. Meanwhile, their vigilance promotes a municipal governance that provides services more efficiently than do the state or national government. Fischel has coined the portmanteau word “homevoter” to crystallize the connection between homeownership and political involvement. The link neatly explains several vexing puzzles, such as why displacement of local taxation by state funds reduces school quality and why local governments are more likely to be efficient providers of environmental amenities. The Homevoter Hypothesis thereby makes a strong case for decentralization of the fiscal and regulatory functions of government.

Despite local folklore, Chicago is not always a city that works. No longer the "Hog Butcher for the World," the Windy City has, in recent decades, pursued economic growth at all costs--to the detriment of many of its citizens. This book describes the social, economic, and political costs of the growth ideology and examines the populist response that promises an alternative Chicago. Tracing the city's uneven economic development since World War II, the authors demonstrate how unchecked growth in favor of private enterprise has resulted in severe poverty, unemployment, crime, reduced tax revenues and property values, a decline in municipal services, and racial, ethnic, and class divisiveness. And yet proponents of Daley-style machine politics and the notion of the city as a growth machine still assert that the future of the city depends exclusively on its ability to grow. The victory of Harold Washington is the most visible symbol of the movement toward an alternative Chicago. Naming different priorities and using more participatory tactics, this challenge to the politics of growth promotes development that is responsive to social need, not just market signals. Author note: Gregory D. Squires is Associate Professor of Sociology at the University of Wisconsin, Milwaukee. Larry Bennett is Associate Professor and Chair of the Political Science Department at DePaul University. Kathleen McCourt is Associate Professor and Chair of the Department of Sociology and Anthropology at Loyola University of Chicago. Philip Nyden is Associate Professor of Sociology at Loyola University of Chicago.

Examines the factors which limit human economic and population growth and outlines the steps necessary for achieving a balance between population and production. Bibliogs

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.