Conference paper Open Access

Urban Planning for Active and Healthy Public Spaces with User-Generated Big Data

van Renswouw, Loes; Bogers, Sander; Vos, Steven

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  <identifier identifierType="DOI">10.5281/zenodo.570550</identifier>
      <creatorName>van Renswouw, Loes</creatorName>
      <familyName>van Renswouw</familyName>
      <affiliation>Fontys University of Applied Sciences</affiliation>
      <creatorName>Bogers, Sander</creatorName>
      <affiliation>Eindhoven University of Technology</affiliation>
      <creatorName>Vos, Steven</creatorName>
      <affiliation>KU Leuven</affiliation>
    <title>Urban Planning for Active and Healthy Public Spaces with User-Generated Big Data</title>
    <date dateType="Issued">2017-04-28</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.603949</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This paper explores the value of user-generated big data for urban planning of active and healthy public spaces. The research is situated in and focuses on Eindhoven, an innovative and sports-minded city in the Netherlands. Based on running data collected by two popular apps in The Netherlands and Belgium, we present three iterations that set out to gain understanding in what factors define good running environments. The first iteration uses data visualisation techniques to get geographic insight in our data, to identify running hotspots and other points of interest for further analysis. The second iteration uses a mixed method approach to combine running data with qualitatively scored environmental characteristics of the selected locations from iteration one to identify possible influencers of the attraction of these areas for runners. As it became clear that this approach requested further scaling, in the third iteration we explore how we can come to factors that are worth scoring. Creating a larger set of locations with a reduced number of variables allowed for more substantial statistical analysis. This approach helped to provide an initial insight in the relevance of some of the environmental factors for optimised running climates. &lt;br&gt;
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