An Algebra for Spatiotemporal Data: From Observations to Events

Recent technological advances in geospatial data gathering have created massive data sets with better spatial and temporal resolution than ever before. These large spatiotemporal data sets have motivated a challenge for Geoinformatics: how to model changes and design good quality software. Many existing spatiotemporal data models represent how objects and fields evolve over time. However, to properly capture changes, it is also necessary to describe events. As a contribution to this research, this article presents an algebra for spatiotemporal data. Algebras give formal specifications at a high‐level abstraction, independently of programming languages. This helps to develop reliable and expressive applications. Our algebra specifies three data types as generic abstractions built on real‐world observations: time series, trajectory and coverage. Based on these abstractions, it defines object and event types. The proposed data types and functions can model and capture changes in a large range of applications, including location‐based services, environmental monitoring, public health, and natural disasters.