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Bartosz obtained a master’s degree in human geography, with aspecialization in GIS, at the Jagiellonian University in Krakow, Poland in 2009. His thesis focuses on the spatio-temporal patterns of mobile phone activities, using the example of Amsterdam city. Since 2009 he has been working in the CurrentCity Research Foundation as a data specialist. During that period Bartosz participated in different projects exploring the potential of telecom data for the real-time monitoring of population dynamics in a city.
Bartosz's research within the Doctoral College GIScience deals with the geo-spatial modeling for collective sensing. He focuses on understanding and modeling human behaviour based on diverse data sources such as telecom data or social media.
Due to Bartosz’s education and professional experience his scientific background includes: GIS&T, data mining, pattern recognition and the handling of huge databases.
For more information about Bartosz' previous work please refer to the CurrentCity website.
Research Cluster: Time and Process
PhD Thesis Topic: Geo-Spatial Models for Collective Sensing
Although collective sensing is not yet well established discipline, we can describe it as models and methods that derive the descriptors of a community behaviour, or features of the community, based on the digital fragments that community leave behind while using digital system. These data can originate from different types of probes e.g. telecom network or social media. The potential for describing the society behaviour has been already scientifically explored. However most works so far focused only on single type of probe at the time. The holistic view about the potential of multiple collective-sensing probes and a systematic conceptualization of collective sensing remain unexplored. The thesis is designed to fill in this gap with the clarification of common concepts, models and procedures for the discipline, looking from the geospatial perspective. An exploration of the collectively sensed data complexity would be accomplished with a series of case studies. The knowledge acquired will enable a general modelling of collective behaviour patterns and their correlations with certain phenomena’s in everyday life, such as mobility, use of space or communication. The thesis will rely on the methods like visual data analytics, data mining, machine learning, pattern recognition, spatio-temporal clustering, correlation and regression analysis.
Research Areas: Location Based Services, Pervasive Computing, Social Media.