43Land use classification is essential for urban planning. Urban land use types can be 44 differentiated either by their physical characteristics (such as reflectivity and texture) 45 or social functions. Remote sensing techniques have been recognized as a vital 46 method for urban land use classification because of their ability to capture the 47 physical characteristics of land use. Although significant progress has been achieved 48 in remote sensing methods designed for urban land use classification, most techniques 49 focus on physical characteristics, whereas knowledge of social functions is not 50 adequately used. Owing to the wide usage of mobile phones, the activities of residents, 51 which can be retrieved from the mobile phone data, can be determined in order to 52 indicate the social function of land use. This could bring about the opportunity to 53 derive land use information from mobile phone data. To verify the application of this 54 new data source to urban land use classification, we first construct a time series of 55 aggregated mobile phone data to characterize land use types. This time series is 56composed of two aspects: the hourly relative pattern, and the total call volume. A 57 semi-supervised fuzzy c-means clustering approach is then applied to infer the land 58 use types. The method is validated using mobile phone data collected in Singapore. 59Land use is determined with a detection rate of 58.03%. An analysis of the land use 60 classification results shows that the accuracy decreases as the heterogeneity of land 61 use increases, and increases as the density of cell phone towers increases. 62

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual's segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial-ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.

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