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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLIX-B4-2026-591-2026</article-id>
<title-group>
<article-title>Spatiotemporal Data Management for subnational Census Data on global Scale</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Brinkhoff</surname>
<given-names>Thomas</given-names>
<ext-link>https://orcid.org/0000-0002-5692-7855</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Institute for Applied Photogrammetry and Geoinformatics, Jade University of Applied Sciences, Oldenburg, Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>04</day>
<month>08</month>
<year>2026</year>
</pub-date>
<volume>XLIX-B4-2026</volume>
<fpage>591</fpage>
<lpage>596</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Thomas Brinkhoff</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/591/2026/isprs-archives-XLIX-B4-2026-591-2026.html">This article is available from https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/591/2026/isprs-archives-XLIX-B4-2026-591-2026.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/591/2026/isprs-archives-XLIX-B4-2026-591-2026.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/591/2026/isprs-archives-XLIX-B4-2026-591-2026.pdf</self-uri>
<abstract>
<p>Knowledge of the regional distribution of the world&amp;rsquo;s population is essential for political and social decisions not only but especially for achieving the 17 Sustainable Development Goals (SDGs). Census and other population data at the subnational level are important for this purpose. However, current population data management platforms largely ignore the spatiotemporal nature of census data. Here, we outline the requirements for a spatiotemporal population data management system and present its general architecture, data model and state of implementation. The developed system currently stores population data from approximately 200 countries, nearly 11 million spatial units and around 770 million individual population figures.</p>
</abstract>
<counts><page-count count="6"/></counts>
</article-meta>
</front>
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