# Modeling Human Spatial Mobility Patterns with the L\'evy Flight Cluster Model

## TL;DR

This paper introduces the Lévý Flight Cluster Model, a hierarchical Bayesian approach for analyzing and simulating human mobility patterns from irregular location data, with applications in understanding overlaps and anonymization.

## Contribution

The paper presents a novel hierarchical Bayesian mixture model for human mobility analysis, capable of capturing movement characteristics and generating synthetic location data.

## Key findings

- Accurately models human movement patterns
- Effectively identifies activity overlaps
- Generates realistic synthetic location data

## Abstract

Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the L\'{e}vy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic overlaps between individuals' activity patterns and serves as an anonymization tool to generate synthetic location data. We present our methodology using real-world human location data, demonstrating its ability to accurately capture the key characteristics of human movement.