Environmental Data Science

This book comprehensively explores sustaining historic cities using a landscape approach and data science. The author offers valuable insights for professionals and enthusiasts interested in preserving and developing urban heritage through a data driven approach. Drawing on the synergy between landscape architecture and data science, the book delves into the intricate interplay between historical, cultural, and environmental factors in urban settings. Readers will understand how to navigate historic cities' complex challenges through case studies, research findings, and practical methodologies. The book equips readers with innovative strategies for preserving the authenticity of these cities while embracing sustainable development practices. By blending theory and real-world applications, this book is a comprehensive guide for creating thriving, resilient, and culturally rich urban environments.

Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems.