Mobility Data Foundation | Bikash Sapkota

Context: GPS-scale geospatial pipelines

Company: LocationMind

Problem: Raw mobility inputs are noisy, high-volume, and difficult to reuse across research, consulting, analytics, and product workflows.

Solution: Designed reproducible ingestion and transformation layers that validate, normalize, aggregate, enrich, and prepare location records for downstream analytics.

Impact: Reduced repeated preprocessing work by standardizing analytical datasets. Improved confidence in outputs through validation, documentation, and repeatable transformations. Created foundations for maps, dashboards, CSV exports, and model features.

Architecture: Raw GPS -> Validation -> Spatial enrichment -> Feature tables -> Analytics outputs

Stack: Python, Spark, AWS S3, AWS Glue, GeoPandas, H3, Data quality