Towards a Comparative Science of Cities: Using Mobile Traffic Records in New York, London, and Hong Kong

Abstract

This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London and Hong Kong. By comparing whole cities typical patterns, we provide insights on how cultural, technological and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.

Prediction limits of mobile phone activity modelling

“…To reduce noise, we apply a spatial smoothing, averaging data in 3×3 blocks of pixels. In total, we have 2373 pixels with measurements in them after leaving out the bottom 5% in terms of activity volume as a further means to reduce noise. Clusters : We use six functional clusters of London obtained by employing methods presented in [23] to group pixels together based on the similarity of their normalized activity time series. The clusters represent areas which we classify as core business, commercial, mixed (commercial/residential), residential, commuter and residential/leisure; clusters and their representative typical time series are displayed in electronic supplementary material, figure S12.…”

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

“…Mobile phone logs -so-called CDR (Call Detail Records)-have been used to map the density of mobile phone activity at different times of the day as an indicator of spatial-temporal changes in the population density in the city (Ratti et al 2006;Reades et al 2009). As the density of calls varies in different time bands and reflects the changes in population densities, each area of the city has its own signature; that is to say, a time profile of mobile phone use, which is very frequent in areas of activity in the central hours of the day, whereas in residential areas it is higher in the afternoon and early evening (Reades et al 2009;Louail et al 2014;Grauwin et al 2015).…”

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

“…So-called opportunistic sensing which is the use of data that is collected for one purpose but can be reused for another one (Campbell et al, 2008), has proved useful in many research studies. Examples include using various anonymized or aggregated spatio-temporal datasets created by different aspects of human activity, such as cell phone data (Gonz alez et al, 2008;Sobolevsky et al, 2013;Hoteit et al, 2014;Kung et al, 2014;Pei et al, 2014;Grauwin et al, 2014) or vehicle GPS traces (Kang et al, 2013). One such example of opportunistically utilizing vehicle GPS traces is a recent study by Santi et al (2014) where the economic and environmental benefits of vehicle pooling in New York were quantified based on the analyses of a taxi GPS dataset consisting of 150 million trips.…”