Project Detail

Public Transit Experiment Analytics (Lagos, Nigeria)

Built daily analytics pipelines for an RCT using public transport e-ticketing API data and geospatial analysis for field and evaluation design.

Problem

The project required reliable daily tracking of public transport usage for an RCT, plus robust spatial preparation of supporting datasets for research operations.

Approach

I used a public transport e-ticketing API to scrape and process daily transport records, then produced recurring usage and performance metrics for the RCT. I also supported the pricing workstream by helping design and analyze the public transport ticket-pricing experiment. In parallel, I built geospatial datasets for research design, including optimal sampling areas around bus stops stratified by population and income indicators, and delivered additional technical analyses in Python for the broader study.

Results

Supported day-to-day RCT monitoring with reproducible Python analytics and provided analysis inputs to the broader research effort, which was later published by NBER (I supported analysis but am not a co-author).