Comprehensive Analysis of Key Spatial Parameters for Characterizing Human Mobility Patterns

Abstract

This paper conducts a thorough analysis of human mobility patterns using commercially available cellular location data from October 2020. The study focuses on six fundamental spatial mobility parameters. These parameters were chosen based on their prominence in existing literature and their importance for understanding human mobility. Our analysis spans daily, weekly, and monthly time scales and draws upon data from the New York-Newark, NJ City Statistical Area. Our findings highlight the predictability of human mobility, suggesting that accurate predictive models could be developed through broader studies across various time frames and geographic regions. We also demonstrate that the choice of appropriate sampling thresholds is not arbitrary but depends on the mobility parameters being investigated, the dataset size, and available computational resources. The research emphasizes the complex relationship between spatial resolution, temporal granularity, and human mobility patterns. Even though the mean and standard deviation values vary based on different sampling threshold combinations, the distribution patterns of mobility parameters remain remarkably consistent. The insights gained from this study lay the groundwork for more comprehensive future research on human mobility.

Keywords

• Big data
• data mining
• mobility patterns
• cellular data
• spatial parameters