Latent patterns of urban mixing in mobility analysis across five global cities

1. 1Collect large-scale travel surveys from multiple cities
2. 2Apply socioeconomic status inference from residential neighborhoods
3. 3Compare social mixing levels using different data sources
4. 4Analyze mobility patterns to reveal latent urban mixing patterns
5. 5Visualize and interpret results to inform policy decisions

Data scientists and urban planners can benefit from this study to inform policy decisions and understand human mobility patterns

💡 Socioeconomic status inference from residential neighborhoods can yield social mixing levels 16% lower than using individual-level data

🗺️ Uncover hidden patterns of urban mixing in 5 global cities 🚶‍♀️💡