Machine learning approaches are increasingly used across numerous applications in order to learn from data and generate new knowledge discoveries, advance scientific studies and support automated decision making. In this knowledge entry, the fundamentals of Machine Learning (ML) are introduced, focusing on how feature spaces, models and algorithms are being developed and applied in geospatial studies. An example of a ML workflow for supervised/unsupervised learning is also introduced. The main challenges in ML approaches and our vision for future work are discussed at the end.
Regression analysis is a common statistical tool used to model relationships between variables and to explore the influencing factors underlying observed spatial data patterns. This entry focuses on the most basic form of regression model: linear regression. The notations, inference, assumptions, and diagnostics of linear regression are introduced, and interpretations of linear regression results are demonstrated using an empirical example in R software. The entry concludes with a brief discussion of the challenges of applying standard linear regression to spatial data.