Abstract:Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised "third places", which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.3/49

This research employs novel techniques to examine older learners' journeys, educationally and physically, in order to gain a 'threedimensional' picture of lifelong learning in the modern urban context of Glasgow. The data offers preliminary analyses of an ongoing 1500 household survey by the Urban Big Data Centre (UBDC). A sample of 1037, with 377 older adults aged 60+, was examined to understand older learner engagement in formal, in-formal, non-formal and family-learning contexts. Preliminary findings indicate that all forms of older learning participation are lower than younger and middle-age counterparts. However, there is a subset of 'actively ageing' , socially and technologically engaged older adult 'learner-citizens' , participating in educational, physical, cultural, civic and online activities (including online political discussions and boycotts). These older learners were more likely to be working, caretakers and report better health overall. Long-term disabilities were associated with less engagement in non-formal learning activities. Additionally, engaged older learners' GPS trails show more city activity than their matched non-learningengaged counterparts. Place-based variables, such as feeling safe and belonging to the local area, moderated adult participation in learning activities. The full data-set will be accessible to researchers and the general public via UBDC, providing a complex data source to explore demographically diverse learners' within an urban context.

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