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Full Description
GIS technology and applications have advanced as fast as computing technology to enhance business analytics, predictive modeling, virtual reality, and artificial intelligence. The third edition addresses these new topics of interest to students and practitioners who are using geographic information systems but have a limited mathematical background. Thoroughly updated and reorganized to focus more on applications and problem solving by mathematical techniques, this book explains the basic architecture of computing as it relates to GIS, includes new application examples of selected mathematical methods, and introduces 3D modeling, machine learning, and more.
Features
Explains the basic mathematics that underpins the manipulation of spatially related data and adds new technology direction such as machine learning.
Builds logically step-by-step from simple basic assumptions to real world GIS applications to illustrate mathematical techniques covered in each chapter.
Explains computing fundamentals including databases, and modeling techniques such as network modeling and topology overlay.
Includes two new chapters focused on how computing relates to mathematics, and new popular applications of GIS which connect with data science and artificial intelligence.
Prepares today's GIS students who do not have STEM backgrounds to follow the thought processes behind the practice of GIS.
This textbook is written for those who use global information systems and applications but have a limited mathematical background. It explains the mathematics behind the applications, making it an accessible book for both undergraduate and graduate students in GIS, Computer Science, Geography, and Geomatics.
Contents
Unit 1: Data, Information, Numbers. 1. Characteristics of Geographic Information. 2. Numbers and Numerical Analysis. 3. Data Inside Computers. Unit 2: Measuring Space. 4. Algebra: Treating Numbers as Symbols. 5. The Geometry of Common Shapes. 6. Plane and Spherical Trigonometry. 7. Applied Spatial Analysis. Unit 3: Transforming Space. 8. Vectors. 9. Matrices and Determinants. 10. Differential and Integral Calculus. Unit 4: Modeling Space. 11. 2D/3D Transformations. 12. Map Projections. 13. Curves and Surfaces. Unit 5: Finding Patterns and Predicting. 14. Basic Statistics. 15. Correlation and Regression. 16. Statistical Modeling and Machine Learning.
