  Land use classification is essential for urban planning. Urban land use types
can be differentiated either by their physical characteristics (such as
reflectivity and texture) or social functions. Remote sensing techniques have
been recognized as a vital method for urban land use classification because of
their ability to capture the physical characteristics of land use. Although
significant progress has been achieved in remote sensing methods designed for
urban land use classification, most techniques focus on physical
characteristics, whereas knowledge of social functions is not adequately used.
Owing to the wide usage of mobile phones, the activities of residents, which
can be retrieved from the mobile phone data, can be determined in order to
indicate the social function of land use. This could bring about the
opportunity to derive land use information from mobile phone data. To verify
the application of this new data source to urban land use classification, we
first construct a time series of aggregated mobile phone data to characterize
land use types. This time series is composed of two aspects: the hourly
relative pattern, and the total call volume. A semi-supervised fuzzy c-means
clustering approach is then applied to infer the land use types. The method is
validated using mobile phone data collected in Singapore. Land use is
determined with a detection rate of 58.03%. An analysis of the land use
classification results shows that the accuracy decreases as the heterogeneity
of land use increases, and increases as the density of cell phone towers
increases.

Computers and Society