Underground Resilience & Retail Modelling

Python, Technical Writing, Statistical Analysis, Data Visualization, Data Analysis, Statistical Modeling, Simulation, Predictive Analytics, Data Management, Urban Planning · I need a clear, two-part analytical workflow that I can run and update myself.

Part I – London Underground resilience Using network science methods, quantify how individual and multiple station closures alter overall connectivity, with passenger flows taking priority in the metrics you develop. I am especially interested in before-and-after comparisons that highlight critical stations and show where flows are most vulnerable.

Part II – Retail location modelling Build a spatial interaction model that draws on census-based population counts, current supermarket locations and inter-zonal distances. Run two alternative location scenarios for a proposed new store and report predicted demand share for each. An accessibility check must round off the analysis, focusing on a 15-minute walking catchment and related walking-distance indicators.

Deliverables • Clean, well-commented code (Python) and any GIS project files (jupiter notebook) • A concise technical report that explains data sources, methods, key assumptions and findings, with figures that can drop straight into a slide deck • Step-by-step instructions so I can reproduce results on my own machine

Please keep external