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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-XLI-B7-939-2016</article-id>
<title-group>
<article-title>HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Oliveira</surname>
<given-names>Ricardo</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Moreno</surname>
<given-names>Rafael</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>University of Colorado Denver, Department of Geography and Environmental Sciences, USA</addr-line>
</aff>
<pub-date pub-type="epub">
<day>22</day>
<month>06</month>
<year>2016</year>
</pub-date>
<volume>XLI-B7</volume>
<fpage>939</fpage>
<lpage>940</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2016 Ricardo Oliveira</copyright-statement>
<copyright-year>2016</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLI-B7/939/2016/isprs-archives-XLI-B7-939-2016.html">This article is available from https://isprs-archives.copernicus.org/articles/XLI-B7/939/2016/isprs-archives-XLI-B7-939-2016.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLI-B7/939/2016/isprs-archives-XLI-B7-939-2016.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLI-B7/939/2016/isprs-archives-XLI-B7-939-2016.pdf</self-uri>
<abstract>
<p>Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.</p>
</abstract>
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