Ferreira, Karine
Camara, Gilberto
Monteiro, Miguel
2013-05-22
<p>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 <em>objects</em> and <em>fields</em> evolve over time. However, to properly capture changes, it is also necessary to describe <em>events</em>. 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: <em>time series</em>, <em>trajectory</em> and <em>coverage</em>. Based on these abstractions, it defines <em>object</em> and <em>event</em> 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.</p>
https://doi.org/10.1111/tgis.12030
oai:zenodo.org:3832891
Zenodo
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Transactions in GIS, 18, 253-269, (2013-05-22)
Spatio-temporal data, GIS, abstract data type, observations, events
An Algebra for Spatiotemporal Data: From Observations to Events
info:eu-repo/semantics/article