Estimating hourly neighborhood population based on smartphone-based human mobility data

Abstract

The high-resolution dynamic population map serves as a fundamental dataset for socio-economic analysis and research, including urban planning and hazard exposure estimation. In the United States, the Census Bureau provides static population estimates, with the most detailed level being the neighborhood (block group) level. Each year, the LandScan dataset offers gridded population maps that include daytime and nighttime estimates at approximately 90-meter resolution. However, few population maps provide high temporal resolution, like daily or hourly, to facilitate fine-scale and dynamic analyses. An example is estimating air pollution exposure in urban areas during weekdays. In this study, we propose a method using smartphone-based human mobility data (Advan Patterns) to reconstruct the hourly population for each neighborhood across the U.S. This is one of the first hourly population maps, contributing to various studies that involve dynamic populations at precise spatiotemporal scales.