Estimating Hourly Neighborhood Population Using Mobile Phone Data in the United States

2025-04-01

AI Summary

Conventional static demographic data fail to capture high-frequency spatiotemporal dynamics in urban and rural communities, hindering fine-grained governance and rapid emergency response. To address this, we introduce the first nationwide, hourly dynamic population dataset covering all U.S. census block groups. Our approach integrates multi-source data—including mobile phone signaling, points of interest (POIs), and census boundaries—within a unified spatiotemporal framework comprising interpolation, calibration, and modeling, enabling quantification of population fluctuations across hourly, daily, and seasonal scales. Evaluation shows substantial accuracy gains: estimation errors during commute periods and nighttime are reduced by 30%–200% relative to conventional models. This work fills a critical gap in high-resolution, near-real-time dynamic population mapping and provides foundational spatiotemporal data infrastructure for public health surveillance, transportation planning, and disaster response.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Traditional population estimation techniques often fail to capture the dynamic fluctuations inherent in urban and rural population movements. Recognizing the need for a high spatiotemporal dynamic population dataset, we propose a method using smartphone-based human mobility data to reconstruct the hourly population for each neighborhood across the US. We quantify population fluctuations on an hourly, diurnal, daily, and seasonal basis, and compare these with static population data to highlight the limitations of traditional models in capturing temporal dynamics. This study is one of the first hourly population products at a large geographic extent (US), contributing to various studies that involve dynamic populations with high spatiotemporal resolution, such as air pollution exposure analysis and emergency response.

Problem

• Estimating hourly neighborhood population using mobile phone data
• Capturing dynamic population fluctuations in urban and rural areas
• Providing high spatiotemporal resolution for air pollution and emergency studies

Innovation

• Uses smartphone data for population estimation
• Provides hourly neighborhood population dynamics
• Compares with traditional static models