On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density

Area of Science:

• Computational Social Science
• Mobility Data Analysis
• Statistical Physics

Background:

• Human mobility generates complex data patterns.
• Entropy measures quantify uncertainty and randomness in systems.
• Mobile phone data offers a rich source for studying collective human behavior.

Purpose of the Study:

• To develop an analytical framework for entropy-based mobility metrics.
• To establish statistical relationships between mobility variables and entropy measures.
• To demonstrate the utility of entropy measures for understanding social dynamics and population density.

Main Methods:

• Utilizing mobile phone data to derive entropy-based mobility metrics (random entropy, uncorrelated Shannon entropy).
• Employing a collisional model to link collective mobility variables (movement trends, population density) with entropy measures.
• Validating the analytical framework and exploring correlations with economic indicators.

Main Results:

• Established statistical relationships between entropy measures and human mobility variables.
• Demonstrated the utility of entropy measures for estimating effective population density.
• Showcased a more realistic understanding of social interactions by considering movement regularities and intensity.

Conclusions:

• The proposed analytical framework effectively interprets entropy-based mobility from mobile phone data.
• Entropy measures provide valuable insights into human mobility patterns and social interactions.
• The methodology offers a realistic population density estimation, particularly relevant during events like the COVID-19 pandemic.