Towards pervasive geospatial affect perception

Due to the enormous penetration of connected computing devices with diverse sensing and localization capabilities, a good fraction of an individual’s activities, locations, and social connections can be sensed and spatially pinpointed. We see significant potential to advance the field of personal activity sensing and tracking beyond its current state of simple activities, at the same time linking activities geospatially. We investigate the detection of sentiment from environmental, on-body and smartphone sensors and propose an affect map as an interface to accumulate and interpret data about emotion and mood from diverse set of sensing sources. In this paper, we first survey existing work on affect sensing and geospatial systems, before presenting a taxonomy of large-scale affect sensing. We discuss model relationships among human emotions and geo-spaces using networks, apply clustering algorithms to the networks and visualize clusters on a map considering space, time and mobility. For the recognition of emotion and mood, we report from two studies exploiting environmental and on-body sensors. Thereafter, we propose a framework for large-scale affect sensing and discuss challenges and open issues for future work.