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:
| ISBN: | 9781032633510 |
| Publication date: | 8th December 2023 |
| Author: | Paula Moraga |
| Publisher: | Chapman & Hall/CRC an imprint of CRC Press |
| Format: | Hardback |
| Pagination: | 280 pages |
| Series: | Chapman & Hall/CRC Data Science Series |
| Genres: |
Geographical information systems, geodata and remote sensing Information visualization Data science and analysis: general Probability and statistics |
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:
Spatial Statistics for Data Science features in the following genres: Probability and statistics, Social research and statistics, Geochemistry, Biology, life sciences
Spatial Statistics for Data Science is available in Hardback
Spatial Statistics for Data Science was written by Paula Moraga and published by Chapman & Hall/CRC an imprint of CRC Press
Spatial Statistics for Data Science has 280 pages
Yes it is part of Chapman & Hall/CRC Data Science Series series
£93.59