A visitor-enriched census in the U.S. cities using large-scale mobile positioning data

Area of Science:

• Socio-demographics
• Human Mobility
• Data Science

Background:

• Traditional census data offers valuable socio-demographic insights but often treats units as static.
• Existing data products overlook the interactions and mobilities of individuals within census units.

Purpose of the Study:

• To introduce a novel
• visitor census
• dataset by integrating human visitations from mobile positioning data.
• To enrich traditional census data with dynamic socio-demographic information based on observed mobilities.
• To bridge the gap between aggregated census data and individual-level analysis using digital traces.

Main Methods:

• Identification and validation of potential home locations for 3.58 million anonymous mobile phone users across seven U.S. metropolitan areas in July 2021.
• Enrichment of visited place socio-demographic profiles using home detection results from mobile positioning data.
• Development of an adaptive data generation framework for integrating diverse socio-demographic features at multiple scales.

Main Results:

• A semantically enriched
• visitor census
• dataset incorporating human visitations and spatial interactions.
• Demonstration of enriching place-based socio-demographic profiles with mobility data.
• Validation of a framework for integrating conventional census and mobile phone data.

Conclusions:

• The
• visitor census
• enhances traditional resident-based census knowledge by incorporating mobilities and spatial interactions from digital traces.
• This approach bridges the gap between aggregated and individual analysis.
• The framework supports future integration of diverse socio-demographic features at varying spatial and temporal scales.