Bernd Resch
University of Salzburg, Z_GIS Department of Geoinformatics, Faculty Member
Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People... more
Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between eac...
Research Interests:
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks... more
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r=0.73), traffic incidents (r=0.59) and hazard disruptions (r=0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.