Using Big Data Sets to Analyse Human Behaviour Patterns and Urban Spatial Structure - A Case Study of the Spatial-Temporal Dynamics in Shanghai
Big Data is changing the traditional way of data collection. As an example of Big Data, mobile phone signal data can be used in geography and urban planning analysis. The approximate spatial position of the mobile-phone user can be calculated according to the signal data sent by a mobile-phone and the location of the base station. This research demonstrates that it is possible to use the mobile-phone signal data to calculate the spatial distribution of urban population. It also tests the suitability of existent metrics and tries to evaluate change by creating new metrics. In Shanghai, by using existent metrics, this study finds that the spatial distribution of the city’s population calculated by mobile-phone signal data generally corresponds to the demographic census. Moreover, this research also employs mobile phone signal data to analyse the spatial correlation between facilities, housing price and other variables and population density distribution in Shanghai. Finally, this study calculates the individual’s origin-destination (OD) flows of Shanghai, gets the job housing distance, and identifies the difference of job-living spatial movement pattern of Shanghai residents. This research also intends to figure out the factors that cause the difference in job-housing spatial relationships between four groups of people in different types of urban functional areas. This research corroborates David Harvey’s time-space compression theory and spatial fix theory by using Big Data set. Going beyond that, it extends these theories by arguing the interchangeability between exploitation in the time dimension, and that in the spatial dimension, resulting in dual exploitation. This study has important policy implications for optimizing the urban spatial system of Shanghai, as it advocates that in addition to strengthening the enforcement of labour law, the government also needs to improve the public service such as strengthening the public transportation systems capacity, and construct affordable houses, so as to alleviate the employees’ sufferings caused by temporal and spatial exploitation.
Big Data, Urban Spatial Structure, Mobile Phone Signal Data, Shanghai, Activity Patterns, OD, Dynamic Matrices, Housing Price
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