A new insight into land use classification based on aggregated mobile phone data
By Tao Pei, Stanislav Sobolevsky, Carlo Ratti, Shih-Lung Shaw, Ting Li, Chenghu Zhou. International Journal of Geographical Information Science, 2013
396 citations recorded by the source corpus.
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 vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume 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 detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.
Subfields: Epidemiology, Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/bcf706804ad24bf9e58f640f32c8a172774a3284
How researchers see this paper
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In Global and Planetary Change
In Transportation
- Depeng Jin
- Yong Li
- Fengli Xu
- Pengyu Zhang
- Huandong Wang
- Song Gao
- Krzysztof Janowicz
- Satish V. Ukkusuri
Citations in the corpus
- From mobile phone data to the spatial structure of cities (2014, neutral): “(eds)] [1–25](ISTE-Wiley, 2011).”
- Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, background): “Remote sensing techniques perform well in extracting physical characteristics, such as land surface reflectivity and texture of urban space but are not good in identifying functional interaction patterns or in helping understand socioeconomic environments (Liu et al., 2015; Pei et al., 2014).”
- Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016, methodology): “Moreover, some function types that were not identified in previous studies have been classified in our result, such as service buildings, medical and public places [28,39].”
- Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015, background): “Other studies combine the CDR, GPS locations, and application traces to investigate the land usage[24, 20], social interactions[7], location-based patterns[5], and web and data access patterns[13, 12].”
- Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, positive): “Moreover, human activity patterns and land use patterns have been studied using CDR data (e.g.,Pei et al., 2014).”