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Open AccessEditorial

Human-Centric Data Science for Urban Studies

by Bernd Resch 1,2,*,† and Michael Szell 3,4,5,*,†
1
Department of Geoinformatics, Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
2
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
3
NEtwoRks, Data, and Society (NERDS), IT University of Copenhagen, 2300 Copenhagen, Denmark
4
ISI Foundation, 10126 Turin, Italy
5
Complexity Science Hub Vienna, 1080 Vienna, Austria
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 584; https://doi.org/10.3390/ijgi8120584
Received: 2 December 2019 / Accepted: 9 December 2019 / Published: 12 December 2019
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens’ needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use. View Full-Text
Keywords: urban data science; smart cities; geoinformatics urban data science; smart cities; geoinformatics
MDPI and ACS Style

Resch, B.; Szell, M. Human-Centric Data Science for Urban Studies. ISPRS Int. J. Geo-Inf. 2019, 8, 584.

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