Spatiotemporal mapping of urban trade and shopping patterns: A geospatial big data approach

A geospatial big data approach reveals that the Tabriz Historic Bazaar Complex and wealthy suburbs like Valiasr and Elguli serve as major trade hotspots, highlighting the city's unbalanced urban development. The study leveraged machine learning, kernel density estimation, space–time pattern mining, and spatiotemporal coupling to analyze 5200 stores' transaction data, demonstrating the potential of GIScience methods for explicit spatial mapping of trade patterns. These findings provide actionable insights for sustainable urban planning in Tabriz.

Feizizadeh, B., Omarzadeh, D., & Blaschke, T. (2024). Spatiotemporal mapping of urban trade and shopping patterns: A geospatial big data approach. International Journal of Applied Earth Observation and Geoinformation, 128, 103764. https://doi.org/10.1016/j.jag.2024.103764