Introduction to Mathematical Techniques Used in GIS

Gordon Ye is an instructor at the City College of San Francisco who developed and taught a well-regarded GIS certificate program from 1999 to 2019 over a span of 21 years. He has a master’s degree in physics from University of California at Berkeley in 1993 and started specializing in Geographic Information Systems at the UC Berkeley City Regional Planning department as a research assistant. His research projects include modeling wildfire susceptibility and modeling real estate value impacts by environmental events. In 1996 he started working for San Francisco-based Pacific Gas and Electric Co. as a GIS analyst and later as an application engineer, during which he developed GIS mapping systems for management of both gas and electric infrastructure. During 2008-2010 he worked briefly for a defense contractor as a software engineer and developed automated tools to build 3D virtual reality models of foreign war zones for military training games. He returned to work for Pacific Gas and Electric Co. after the devastating gas pipeline explosion at San Bruno, California in 2010. Since 2011 he has been focused on modeling gas pipeline integrity risks such as corrosion and earthquakes, and their potential impacts to people and the environment. Peter Dale trained as a land surveyor and worked for seven years in Uganda before entering the academic world. He ultimately became a professor in land information management at the University College London. He is an Honorary President of the International Federation of Surveyors and was awarded an OBE in recognition of his services to surveying. He is now retired and lives in a remote area of Scotland.

• 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.