  The emergence of big data enables us to evaluate the various human emotions
at places from a statistic perspective by applying affective computing. In this
study, a novel framework for extracting human emotions from large-scale
georeferenced photos at different places is proposed. After the construction of
places based on spatial clustering of user generated footprints collected in
social media websites, online cognitive services are utilized to extract human
emotions from facial expressions using the state-of-the-art computer vision
techniques. And two happiness metrics are defined for measuring the human
emotions at different places. To validate the feasibility of the framework, we
take 80 tourist attractions around the world as an example and a happiness
ranking list of places is generated based on human emotions calculated over 2
million faces detected out from over 6 million photos. Different kinds of
geographical contexts are taken into consideration to find out the relationship
between human emotions and environmental factors. Results show that much of the
emotional variation at different places can be explained by a few factors such
as openness. The research may offer insights on integrating human emotions to
enrich the understanding of sense of place in geography and in place-based GIS.
