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Urban Sci. 2017, 1(2), 15;

Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)

Center for Earth System Research and Sustainability, University of Hamburg, D-20146 Hamburg, Germany
Department of Forest and Water Management, Ghent University, 9000 Ghent, Belgium
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Athens GR-15236, Greece
School of Chemical Engineering, National Technical University of Athens, Athens GR-15780, Greece
Institute of Ecology, Technische Universität Berlin, D-12165 Berlin, Germany
Department of Earth and Environmental Sciences, KU Leuven, 3001 Leuven, Belgium
Institute of Geography, University of Augsburg, D-86159 Augsburg, Germany
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA
School of Geography, University College Dublin, Dublin 4, Ireland
Purdue University, West Lafayette, IN 47906, USA
Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Author to whom correspondence should be addressed.
Academic Editors: Elizabeth Wentz, Lindsey Conrow and Heather Fischer
Received: 13 March 2017 / Revised: 28 April 2017 / Accepted: 6 May 2017 / Published: 9 May 2017
(This article belongs to the Special Issue Crowdsourcing Urban Data)
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The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets. View Full-Text
Keywords: Local Climate Zones (LCZs); urban climate; crowdsourcing; volunteered geographic information; classification; WUDAPT Local Climate Zones (LCZs); urban climate; crowdsourcing; volunteered geographic information; classification; WUDAPT

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Bechtel, B.; Demuzere, M.; Sismanidis, P.; Fenner, D.; Brousse, O.; Beck, C.; Van Coillie, F.; Conrad, O.; Keramitsoglou, I.; Middel, A.; Mills, G.; Niyogi, D.; Otto, M.; See, L.; Verdonck, M.-L. Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX). Urban Sci. 2017, 1, 15.

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