Urban Mobility Analytics — Wi‑Fi Log Framework
I published research proposing a hierarchical framework for Wi‑Fi log data processing to analyze human mobility, with spatial-temporal analysis across communities using public Wi‑Fi data.
Work type
Data product · Analytics framework
Methods
Hierarchical processing · spatio-temporal analysis
Outputs
Framework + findings + publication
Link
Problem
Public Wi‑Fi networks generate event logs that can be useful for understanding mobility patterns, but raw data is noisy, inconsistent, and hard to translate into reliable movement insights without a clear processing approach.
What I did
- Proposed a hierarchical processing framework for Wi‑Fi log data to enable consistent mobility analytics.
- Performed statistical comparison and correlation analysis to understand how network settings impact results.
- Conducted spatial-temporal analysis across multiple communities using public Wi‑Fi datasets.
Outputs & impact
- Published the approach and results through Elsevier / ScienceDirect.
- Provided an end-to-end structure teams can reuse for data ingestion, cleaning, aggregation, and analysis.
- Demonstrated how configuration choices can influence mobility interpretation from Wi‑Fi logs.
What I’d do next
- Create a repeatable pipeline (ETL + validation) to operationalize the framework for new cities.
- Add dashboard-ready KPIs (flows, dwell-time proxies, peak periods) for stakeholder consumption.
- Evaluate privacy-preserving aggregation approaches for broader deployment.