  Location-based social network data offers the promise of collecting the data
from a large base of users over a longer span of time at negligible cost. While
several studies have applied social network data to activity and mobility
analysis, a comparison with travel diaries and general statistics has been
lacking. In this paper, we analysed geo-referenced Twitter activities from a
large number of users in Singapore and neighbouring countries. By combining
this data, population statistics and travel diaries and applying clustering
techniques, we addressed detection of activity locations, as well as spatial
separation and transitions between these locations. Kernel density estimation
performs best to detect activity locations due to the scattered nature of the
twitter data; more activity locations are detected per user than reported in
the travel survey. The descriptive analysis shows that determining home
locations is more difficult than detecting work locations for most planning
zones. Spatial separations between detected activity locations from Twitter
data - as reported in a travel survey and captured by public transport smart
card data - are mostly similarly distributed, but also show relevant
differences for very short and very long distances. This also holds for the
transitions between zones. Whether the differences between Twitter data and
other data sources stem from differences in the population sub-sample,
clustering methodology, or whether social networks are being used significantly
more at specific locations must be determined by further research. Despite
these shortcomings, location-based social network data offers a promising data
source for insights into activity locations and mobility patterns, especially
for regions where travel survey data is not readily available.
