Published by Conditions, 1981
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Softcover. Condition: Very Good+. First Edition. Conditions: Seven, Spring 1981. Very Good + paperback with minor cover edgewear. 197 pages, unmarked. Allen, Paula Gunn, Joy Harjo, Wendy Rose, Cherrie Moraga, Adrienne Rich, Janine Canan, Linda Hogan, Becky Birtha, Michelle Cliff, Nellie Wong, et al; OOCO A4A; 8vo 8" - 9" tall; 197 pages.
Language: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 036735795X ISBN 13: 9780367357955
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Language: English
Published by Chapman and Hall/CRC, 2023
ISBN 10: 1032633514 ISBN 13: 9781032633510
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Published by Chapman and Hall/CRC, 2023
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 036735795X ISBN 13: 9780367357955
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 036735795X ISBN 13: 9780367357955
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Published by Chapman and Hall/CRC, 2019
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 1032633514 ISBN 13: 9781032633510
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First Edition
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 036735795X ISBN 13: 9780367357955
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 1466510846 ISBN 13: 9781466510845
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 036735795X ISBN 13: 9780367357955
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Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032633514 ISBN 13: 9781032633510
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First Edition
Hardback. Condition: New. 1st. Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners.Key Features:Describes R packages for retrieval, manipulation, and visualization of spatial data.Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes.Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches.