  The patterns of life exhibited by large populations have been described and
modeled both as a basic science exercise and for a range of applied goals such
as reducing automotive congestion, improving disaster response, and even
predicting the location of individuals. However, these studies previously had
limited access to conversation content, rendering changes in expression as a
function of movement invisible. In addition, they typically use the
communication between a mobile phone and its nearest antenna tower to infer
position, limiting the spatial resolution of the data to the geographical
region serviced by each cellphone tower. We use a collection of 37 million
geolocated tweets to characterize the movement patterns of 180,000 individuals,
taking advantage of several orders of magnitude of increased spatial accuracy
relative to previous work. Employing the recently developed sentiment analysis
instrument known as the 'hedonometer', we characterize changes in word usage as
a function of movement, and find that expressed happiness increases
logarithmically with distance from an individual's average location.
