Machine learning methods for spatial and temporal analysis
Machine learning (ML) methods have gained popularity in the GIScience community over the past few decades due to their success in dealing with the nonlinearities and heterogeneities of spatial and temporal datasets. However, their uptake is somewhat limited due to the steep learning curve. This workshop aims to provide a gentle, practical introduction to ML methods for addressing two common problems in spatial and temporal analysis: classification and regression. Attendees will be taught the key concepts underpinning a range of ML algorithms, including support vector machines and random forests. They will then be taught the essential skills necessary to train and test ML models using R statistical package. A number of real world datasets will be used as examples, including GPS tracks, road traffic data and environmental data. The workshop will conclude with a discussion of some the limitations of ML methods, advanced topics and future research directions.