May 22, 2013 Journal article Open Access

Ferreira, Karine; Camara, Gilberto; Monteiro, Miguel

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{
  "description": "<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&nbsp;<em>objects</em>&nbsp;and&nbsp;<em>fields</em>&nbsp;evolve over time. However, to properly capture changes, it is also necessary to describe&nbsp;<em>events</em>. As a contribution to this research, this article presents an algebra for spatiotemporal data. Algebras give formal specifications at a high\u2010level 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\u2010world observations:&nbsp;<em>time series</em>,&nbsp;<em>trajectory</em>&nbsp;and&nbsp;<em>coverage</em>. Based on these abstractions, it defines&nbsp;<em>object</em>&nbsp;and&nbsp;<em>event</em>&nbsp;types. The proposed data types and functions can model and capture changes in a large range of applications, including location\u2010based services, environmental monitoring, public health, and natural disasters.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "INPE (National Institute for Space Research), Brazil", 
      "@id": "https://orcid.org/0000-0003-2656-5504", 
      "@type": "Person", 
      "name": "Ferreira, Karine"
    }, 
    {
      "affiliation": "INPE (National Institute for Space Research), Brazil", 
      "@id": "https://orcid.org/0000-0002-3681-487X", 
      "@type": "Person", 
      "name": "Camara, Gilberto"
    }, 
    {
      "affiliation": "INPE (National Institute for Space Research), Brazil", 
      "@type": "Person", 
      "name": "Monteiro, Miguel"
    }
  ], 
  "headline": "An Algebra for Spatiotemporal Data: From Observations to Events", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2013-05-22", 
  "url": "https://zenodo.org/record/3832891", 
  "keywords": [
    "Spatio-temporal data, GIS, abstract data type, observations, events"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1111/tgis.12030", 
  "@id": "https://doi.org/10.1111/tgis.12030", 
  "@type": "ScholarlyArticle", 
  "name": "An Algebra for Spatiotemporal Data: From Observations to Events"
}