About the author -- Preface -- Introduction -- 1. Indicators for transit-oriented development -- 2. Expanding the urban growth boundary -- 3. Intro to geospatial machine learning, part 1 -- 4. Intro to geospatial machine learning, part 2 -- 5. Geospatial risk modeling - Predictive policing -- 6. People-based ML models -- 7. People-based ML models: algorithmic fairness -- 8. Predicting rideshare demand -- Conclusion - Algorithmic governance -- Index.
Summary:
"Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand 'spatial process' and develop spatial analytics; how to develop 'useful' predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and 'Planning' are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.