Weather Effects on the Patterns of People's Everyday Activities: A Study Using GPS Traces of Mobile Phone Users

Teerayut Horanont · Santi Phithakkitnukoon · Tuck Wah Leong · Yoshihide Sekimoto · Ryosuke Shibasaki

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Human Mobility and Location-Based Analysis Urban Design and Spatial Analysis Data Management and Algorithms

Abstract

This study explores the effects that the weather has on people's everyday activity patterns. Temperature, rainfall, and wind speed were used as weather parameters. People's daily activity patterns were inferred, such as place visited, the time this took place, the duration of the visit, based on the GPS location traces of their mobile phones overlaid upon Yellow Pages information. Our analysis of 31,855 mobile phone users allowed us to infer that people were more likely to stay longer at eateries or food outlets, and (to a lesser degree) at retail or shopping areas when the weather is very cold or when conditions are calm (non-windy). When compared to people's regular activity patterns, certain weather conditions affected people's movements and activities noticeably at different times of the day. On cold days, people's activities were found to be more diverse especially after 10AM, showing greatest variations between 2PM and 6PM. A similar trend is observed between 10AM and midnight on rainy days, with people's activities found to be most diverse on days with heaviest rainfalls or on days when the wind speed was stronger than 4 km/h, especially between 10AM-1AM. Finally, we observed that different geographical areas of a large metropolis were impacted differently by the weather. Using data of urban infrastructure to characterize areas, we found strong correlations between weather conditions upon people's accessibility to trains. This study sheds new light on the influence of weather conditions on human behavior, in particular the choice of daily activities and how mobile phone data can be used to investigate the influence of environmental factors on urban dynamics.

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References

2008Understanding individual human mobility patterns 2010Limits of Predictability in Human Mobility 2010Modelling the scaling properties of human mobility 1993Stock prices and the Wall Street weather 2010Network Diversity and Economic Development 2009Understanding individual human mobility patterns 2011Identifying Important Places in People’s Lives from Cellular Network Data 2010Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

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