A new insight into land use classification based on aggregated mobile phone data

Abstract

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

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

“…The characteristics of MPPD used in this study are similar to the data used in the research by Pei et al and Fan et al [25,27], and we adopted a classification system similar to theirs. However, because Beijing is famous for its Royal Garden and other tourist attractions, we additionally incorporated a scenic area classification.…”

“…MPPD are a reliable indicator of the patterns of human mobility, and they have been shown to be remarkably beneficial in land use mapping [25,30]. However, an inherent weakness of MPPD is the inability to distinguish several land use classes, e.g., grassland and bare-land.…”

“…Pei et al demonstrated that the modes of human activity from Monday to Thursday tend to be similar [25]. Therefore, the hourly MPPD from Monday to Thursday were averaged by hour, i.e., data from the same hour of each of the four days were averaged.…”

“…Specifically, Pei at el. used mobile phone data collected from base transceiver stations in Singapore and fuzzy c-means to obtain a land use map of Singapore [25]. Yuan et al proposed a parcel-based method for land use identification, where POIs and taxi trajectories are integrated to attribute parcel types using Dirichlet multinomial regression [26].…”

“…Of the various sources of auxiliary social information that are available for the extraction of urban land use information, mobile phone data, points of interest (POIs), and human trajectories have been used for functional urban area mapping [25][26][27][28]. Specifically, Pei at el.…”