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Land use classification is essential for urban planning. Urban land use typescan be differentiated either by their physical characteristics (such asreflectivity and texture) or social functions. Remote sensing techniques havebeen recognized as a vital method for urban land use classification because oftheir ability to capture the physical characteristics of land use. Althoughsignificant progress has been achieved in remote sensing methods designed forurban land use classification, most techniques focus on physicalcharacteristics, whereas knowledge of social functions is not adequately used.Owing to the wide usage of mobile phones, the activities of residents, whichcan be retrieved from the mobile phone data, can be determined in order toindicate the social function of land use. This could bring about theopportunity to derive land use information from mobile phone data. To verifythe application of this new data source to urban land use classification, wefirst construct a time series of aggregated mobile phone data to characterizeland use types. This time series is composed of two aspects: the hourlyrelative pattern, and the total call volume. A semi-supervised fuzzy c-meansclustering approach is then applied to infer the land use types. The method isvalidated using mobile phone data collected in Singapore. Land use isdetermined with a detection rate of 58.03%. An analysis of the land useclassification results shows that the accuracy decreases as the heterogeneityof land use increases, and increases as the density of cell phone towersincreases.

A New Insight into Land Use Classification Based on Aggregated Mobile Phone Data