Overview
A geospatial analytics project that blends delivery events, distance matrices, and clustering to identify high-impact route improvements.
Problem
Operations teams lacked visibility into route inefficiencies and demand density by service region.
Solution
Built a reproducible spatial analysis workflow that segments demand zones and highlights delivery-time outliers.
Architecture
How the system is structured
01
Python notebooks for exploratory spatial analysis
02
PostGIS tables for distance and zone calculations
03
Map-based dashboard layers for cluster and SLA inspection
Tech Stack
PythonGeoPandasPostGISKepler.glscikit-learn
Key Features
Demand density clusteringSLA breach mappingRoute outlier detectionScenario-ready zone comparison
Screenshots
Visual walkthrough
Cluster map
SLA layer
Route optimization matrix