The U.S. Department of Housing and Urban Development's (HUD's) Comprehensive Housing Affordability Strategy (CHAS) data provide vital information on housing affordability measures. This article presents potential uses of the CHAS data, as well as limitations of the data. Researchers should explore the benefits of using CHAS data to examine the role of affordable housing market structure on the persistence of urban issues.

Provides housing data tabulated from the Census for states, counties, minor civil divisions, and places. Records include cost data for households at different income levels, as well as information about household size, affordability, and housing problems.

Legacy cities, also commonly referred to as shrinking, or post-industrial cities, are places that have experienced sustained population loss and economic contraction. In the United States, legacy cities are those that are largely within the Rust Belt that thrived during the first half of the 20th century. In the second half of the century, these cities declined in economic power and population leaving a legacy of housing stock, warehouse districts, and infrastructure that is ripe for revitalization. This volume explores not only the commonalities across legacy cities in terms of industrial heritage and population decline, but also their differences. Legacy Cities poses the questions: What are the legacies of legacy cities? How do these legacies drive contemporary urban policy, planning and decision-making? And, what are the prospects for the future of these cities? Contributors primarily focus on Cleveland, Ohio, but all Rust Belt cities are discussed.

This nuts and bolts book addresses specific waste minimization and pollution prevention techniques that work in specific types of laboratories for specific wastestreams. Concepts in the book may be directly applied to laboratory operations. In addition, the book illustrates other approaches to laboratory pollution prevention, such as reducing wastewater discharges and fume hood emissions. A wide range of waste types, including hazardous, infectious, medical, PCB, and radioactive, are discussed. This book helps you to develop a broad, institutional framework to plan and set priorities for pollution prevention. It responds to your laboratory's critical need to have readily available techniques and concepts for waste minimization and pollution prevention.

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.