Human-Centric Data Science for Urban Studies
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 142429
Special Issue Editors
2. Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
Interests: human-centred geoinformatics; geospatial machine learning; urban geoinformatics; fusion of human and technical sensors; people as sensors and collective sensing (VGI); real-time and smart cities; crowdsourcing; digital health
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the past decade, the concept of smart cities has been greatly driven by the idea of an IT-infused city, that is, an urban system enriched with a number of different information technologies to support urban management and planning. However, most previous smart city research initiatives have promoted techno-positivistic approaches, which oftentimes do not account enough for the citizens' needs. Thus, this Special Issue focuses on a more human-centric view of smart cities. A variety of large-scale datasets, sensing technologies, geo-participation initiatives, collaborative mapping tools, and data science approaches have emerged that have the potential to help us in gaining a better understanding of urban processes and how to convert them into concrete urban planning and management actions. These new developments have led to a previously unknown situation in urban science, namely the transformation from data-scarce to data-rich research environments. To optimally leverage these new datasets and technologies, the GIScience community is currently developing innovative methods that go well beyond traditional geospatial analysis, including multidisciplinary approaches combining methods from GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o. This Special Issue encourages the submission of both basic research papers and application-oriented contributions in the area of urban data science, dedicating a particular focus to human-centric approaches.
DATA SOURCES
- Human sensing technologies
- Social media and VGI
- Mobile phone networks
- OSM and OGD
- Participatory geo-technologies
METHODS
- Spatio-temporal analysis of urban processes
- Geo-infused self-learning systems and machine learning approaches
- Statistical analysis of urban processes and structures
- Sentiment analysis and emotion extraction
- Dynamic, spatio-temporal geovisualisation
- Multidisciplinary research (GIScience, computer and data science, urban science, sociology, computational linguistics, complex systems and networks, a.o.)
APPLICATION AREAS
- Urban planning and management
- Mobility and transportation
- Wellbeing, quality of life and livability
- Energy infrastructure planning and management
Assist. Prof. Bernd Resch
Assist. Prof. Michael Szell
Guest Editors
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