Author Profile

Stephen Adams is a Professor of Spatial Data Science at the fictional University of Veritas, where he has spent over two decades mapping the intersection of human behavior and geographic landscapes. After earning his Ph.D. in Quantitative Geography from the Horizon Institute of Technology, Adams pioneered early open-source spatial modeling toolkits that are now industry standards. His research focuses on using predictive analytics to understand urban sprawl, climate migration, and resource distribution in developing mega-cities. When he isn't teaching advanced spatial statistics or consulting for global environmental initiatives, Adams is an avid cartographer of antique maps and a marathon runner. He lives in Portland, Oregon, with his family and a very energetic border collie who frequently accompanies him on fieldwork expeditions. Statistical Methods for Geography is the culmination of his lifelong mission to make complex spatial mathematics accessible, practical, and deeply engaging for the next generation of geographers. Before turning his attention to writing textbooks, Adams led numerous scientific expeditions across the Andes, the Sahara Desert, and the Arctic Circle. His research on shifting tectonic plates and glacial retreat has been widely published in leading academic journals. Today, Adams lives in a coastal village in Cornwall, England, where he divides his time between writing, teaching, and restoring antique maps. Through his books, he aims to inspire the next generation of students to look at the Earth not just as a collection of countries, but as a living, interconnected system. Driven by a passion to make complex global systems accessible and engaging, Stephen transitioned from fieldwork to the classroom. He currently serves as a senior lecturer in Earth Sciences, where he inspires the next generation of global citizens.

About The Book

This book is designed as a textbook for students learning how to apply quantitative analysis to geographic data. the book acts as an accessible, systematic introduction designed specifically for geography, earth, and environmental science students who need to answer the core quantitative question of "where?". Unlike standard statistics textbooks that rely heavily on generic mathematical formulas, this text filters statistics through a spatial lens. It is designed to be highly applied, meaning it focuses on how statistical tools solve real-world geographical problems, such as tracking environmental changes, mapping urban growth, or analyzing demographic distributions. The book bridges the gap between classic statistical inference and unique spatial dynamics by covering Spatial Data Analysis: An integrated look at how data behaves when tied to physical geographic coordinates. Core Statistical Concepts: Traditional foundational topics including descriptive statistics, probability models, sampling, hypothesis testing, and Analysis of Variance (ANOVA). Regression & Autocorrelation: Detailed emphasis on regression models alongside spatial autocorrelation-the principal tools geographers use to measure how things near each other are related. Spatial Patterns & Data Reduction: Practical analysis of point patterns and areal data, alongside advanced techniques like factor and cluster analysis. Software Integration: Guidance on using statistical software packages (such as SPSS) to manage and compute complex data sets. The use of statistical techniques in geography received an impetus only after the Second World War. Since then, application of statistical techniques in social sciences has increased enormously making it essential for geographers to acquire training in elementary statistical methods, particularly after the sixties when statistical geography came to occupy a distinct part of the post-graduate syllabus.

Table of Contents

Preface
Chapter 1. Introduction to Statistical Methods in Geography
Chapter 2. Data Collection and Sampling Methods
Chapter 3. Descriptive Statistics and Data Summarization
Chapter 4. Probability and Statistical Inference
Chapter 5. Correlation and Regression Analysis
Chapter 6. Spatial Statistics and Geographic Analysis
Chapter 7. Multivariate Statistical Methods
Chapter 8. Time Series and Trend Analysis
Chapter 9. Geographic Information Systems (GIS) and Statistical
Integration
Bibliography
Index