  There are different functional regions in cities such as tourist attractions,
shopping centers, workplaces and residential places. The human mobility
patterns for different functional regions are different, e.g., people usually
go to work during daytime on weekdays, and visit shopping centers after work.
In this paper, we analyse urban human mobility patterns and infer the functions
of the regions in three cities. The analysis is based on three large taxi GPS
datasets in Rome, San Francisco and Beijing containing 21 million, 11 million
and 17 million GPS points respectively. We categorized the city regions into
four kinds of places, workplaces, entertainment places, residential places and
other places. First, we provide a new quad-tree region division method based on
the taxi visits. Second, we use the association rule to infer the functional
regions in these three cities according to temporal human mobility patterns.
Third, we show that these identified functional regions can help us deliver
data in network applications, such as urban Delay Tolerant Networks (DTNs),
more efficiently. The new functional-regions-based DTNs algorithm achieves up
to 183% improvement in terms of delivery ratio.
