Fields as a Generic Data Type for Big Spatial Data

This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in a continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.