S. Bosse, Long-term Longitudinal data collection and analysis in highly dynamic systems using mobile Crowd Sensing and mobile Agents, 9th European Congress of Methodology EAM 2021, 21-23.7.2021,
Presentation EBOOK Online
Typical data surveys as human-centred data source reflect only snapshots of dynamical systems on the time-scale. Commonly, in social science surveys are performed in participatory way and by well designed (static) surveys. But crowd sensing gains attraction to collect either supplementary data or aiming to replace traditional survey formats moving towards ad-hoc opportunistic micro-surveys . The data quality of such crowd sourced data is varying and often questionable with high bias and missing values . Ubiquitous and mobile devices gain attraction as data sources with a high spatial and temporal coverage, e.g., smart phones. Continuous sampling of data streams can improve quality of statistical data analysis, generalisation of predictive modelling, and simulation significantly. We present an unified agent-based data collection, aggregation, analysis, and tightly coupled simulation methodology, providing valuable contribution to Computational Social Science (CSS), at least theoretically. Mobile computational agents (mobile software processes) are used for self-organising data collection and aggregation by using machine data and user data via scriptable dialogues. This approach extends the data collection process in the spatial and temporal domain providing a high data coverage and quality, required, e.g., by accurate ML methods. The issues and challenges of long-term self-organising mobile crowd sensing are discussed and analysed with some practical demonstrations in comparison with theoretical expectations.