Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 178 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 38 tok/s Pro
GPT-5 High 40 tok/s Pro
GPT-4o 56 tok/s Pro
Kimi K2 191 tok/s Pro
GPT OSS 120B 445 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Scaling laws of human mobility persist during extreme floods (2511.02783v1)

Published 4 Nov 2025 in physics.soc-ph

Abstract: Although a number of studies have investigated human mobility patterns during natural hazards, mechanistic models that capture mobility dynamics under large-scale perturbations, such as extreme floods, remain scarce. Leveraging mobile phone data and building upon recent insights into universal mobility patterns, we assess whether the general structure of population flows persists during the extreme floods that struck Emilia-Romagna, Italy, in 2023. Our analysis reveals that the relationship between visitor density, distance, and visitation frequency remains robust even under extreme flooding conditions. To disentangle the effects of distance and visitation frequency, we define two aggregated visitor densities: the marginal density over frequency and the aggregated density over distance. We find that the marginal density over frequency exhibits a time-invariant power-law exponent, indicating resilience to flooding disturbances. In contrast, the aggregated density over distance displays more complex behavior: an exponential decay over biweekly periods and a power-law decay over a monthly interval. We propose that the observed power law emerges from the superposition of exponential distributions across shorter timescales. These findings provide new insights into human mobility scaling laws under extreme perturbations, highlighting the robustness of visitation patterns and suggesting avenues for improved mechanistic modeling during natural disasters.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 3 tweets and received 3 likes.

Upgrade to Pro to view all of the tweets about this paper: