  The emergence of large stores of transactional data generated by increasing
use of digital devices presents a huge opportunity for policymakers to improve
their knowledge of the local environment and thus make more informed and better
decisions. A research frontier is hence emerging which involves exploring the
type of measures that can be drawn from data stores such as mobile phone logs,
Internet searches and contributions to social media platforms, and the extent
to which these measures are accurate reflections of the wider population. This
paper contributes to this research frontier, by exploring the extent to which
local commuting patterns can be estimated from data drawn from Twitter. It
makes three contributions in particular. First, it shows that simple heuristics
drawn from geolocated Twitter data offer a good proxy for local commuting
patterns; one which outperforms the major existing method for estimating these
patterns (the radiation model). Second, it investigates sources of error in the
proxy measure, showing that the model performs better on short trips with
higher volumes of commuters; it also looks at demographic biases but finds
that, surprisingly, measurements are not significantly affected by the fact
that the demographic makeup of Twitter users differs significantly from the
population as a whole. Finally, it looks at potential ways of going beyond
simple heuristics by incorporating temporal information into models.
