Environmental Geocomputation with R presents theoretical and practical principles for geospatial data analysis, visualization, and modeling, covering both the individual object approach and the continuous field approach. The book addresses spatial data uncertainties and error management while integrating spatial context into analytical results for better understanding of the environment. It progressively builds complexity enabling readers to develop geocomputational projects in the real world using algorithms and big data with R and its packages. The applications are focused on environmental and agricultural analyses, helping to incorporate spatial-temporal dimensions into decision-making processes.
Features
- Explains the theory and applications of geocomputational methods for solving environmental problems involving spatial data and analysis.
- Covers big data, machine learning, methods for pattern recognition in spatial data, and tools and data analytics in the free and open-source (FOSS) R platform, making them accessible to everyone.
- Includes case studies from different environments and explains the diversity of big data and its applications.
- Provides hands-on exercises at the end of each chapter and encourages readers to understand both the potential and limitations of environmental systems.
- Supplemented by a comprehensive laboratory manual with solved exercises, homework assignments, and research suggestions in geocomputation within an environmental science context.
This textbook is intended for students in upper-level undergraduate or graduate programs using GIS, geocomputation, geoprocessing in environmental sciences and engineering, surveying, geosciences, and agriculture engineering. It is also an excellent resource for professionals in different areas who use geocomputation and image processing and wish to develop their skillset in these areas.