CrowdAnalyser: PhD students
My fascination of working with 3D geodata started with the first time I turned a self-captured point cloud on the screen and was thus catapulted into the third dimension (for own flying lessons see, e.g., the point cloud of the Dechen Cave). 3D geodata are increasingly used for capturing and modelling natural objects and spatial processes. With a growing toolbox of sensors and methods, there are more and more possibilities of working with data from different sources. My PhD project aims at exploring the value and usage of crowdsourced 3D geodata with respect to different applications in various thematic domains.
Real-time social sensor data could be used directly or indirectly to derive spatiotemporal human mobility- and motion patterns on a city scale level. My research objective is to develop novel methods and approaches towards the quality-oriented analysis and exploration of crowd-sourced social-media data. I'm focused on the overall question how spatiotemporal patterns in ubiquitous sensor networks and heterogeneous data streams can be explored, extracted, validated and aggregated in order to be able to sense urban geo-processes and to gain knowledge about urban dynamics. The identification of mobility hubs and the extraction of movement trajectories could be used to understand, enrich and improve mobility and intelligent transportation systems (ITS).
My research deals with investigating scale-driven effects that affect the analysis of social media data. Social media captures a variety of real-world phenomena. Each of the captured phenomena takes place on a certain spatial scale range. Therefore, these data are particularly susceptible to scale effects, due to their mixed-scale nature. The goal of my research is to discover new geostatistical methods for scale-sensitive dealing with social media data. A specific focus of this research is on the investigation of Twitter Tweets.