Abstract
AbstractRecent 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.
Highlights
The age of big geospatial data has come
Space agencies worldwide plan to launch around 260 Earth observation satellites over the 15 years. These massive data sets present a challenge for Geoinformatics
Our model takes observations as a starting point, revisiting the classical work of Sinton (1978). This approach follows the ideas of Kuhn (2005): “All information rests on observations, whose semantics is physically grounded in processes and mathematically well understood
Summary
The age of big geospatial data has come. Mobile phones, social networks and GPS devices create data useful for planning better cities, capturing human interactions and improving quality of life. Space agencies worldwide plan to launch around 260 Earth observation satellites over the 15 years. These massive data sets present a challenge for Geoinformatics. To use these large spatiotemporal data sets properly, we need innovative software designs. Our model takes observations as a starting point, revisiting the classical work of Sinton (1978). This approach follows the ideas of Kuhn (2005): “All information rests on observations, whose semantics is physically grounded in processes and mathematically well understood. We have implemented the algebra using the open source TerraLib geospatial software library (Câmara et al 2008)
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