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Published on: February 25, 2013
Patterns, entropy, and predictability of human mobility and life
Shao-Meng Qin1, Hannu Verkasalo, Mikael Mohtaschemi
1Aalto University, Department of Applied Physics, Espoo, Finland.
Plos One
|January 10, 2013
Summary
Scientists used smartphone data to analyze human mobility patterns. They found that individual daily routines exhibit predictable patterns, allowing for short-term location forecasting.
Area of Science:
- Computational social science
- Human mobility analysis
- Complex systems
Background:
- Cellular phones provide ubiquitous data for observing human behavior, actions, and responses.
- Smartphone data reveals complex spatiotemporal tracks of individuals, reflecting social context and interactions.
Purpose of the Study:
- To extract and analyze typical human locations (e.g., home, work) from smartphone data.
- To classify daily patterns and measure randomness using 'life' entropy.
- To determine the predictability of individual mobility based on identified patterns and entropy.
Main Methods:
- Extraction of important locations from fixed-length time-slots in smartphone data.
- Classification of days into regular, personal patterns and superimposed daily fluctuations.
- Measurement of randomness using 'life' entropy, calculated before and after pattern clustering.
- Exploration of predictability through typical behavior and transition probabilities between locations.
Main Results:
- The set of typical places identified follows a heavy-tailed, power-law distribution (exponent ≈ -1.7).
- Individual daily mobility exhibits regular patterns and stochastic features, quantifiable by entropy.
- Identified patterns enhance mobility predictability for a few hours into the future.
- Fixed habits are most evident in daily working patterns.
Conclusions:
- Human mobility, as observed through smartphone data, displays predictable patterns superimposed on daily randomness.
- The extracted patterns and entropy measures offer insights into individual predictability.
- Smartphone data analysis can reveal and leverage regularities in human behavior for forecasting.
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