Bengaluru Water Quality & Contamination Risk Dashboard
A spatial data pipeline and dashboard scoring and mapping contamination risk across Bengaluru's water-supply and sewage infrastructure, combining citizen audits, lake water quality reports, and spatial proximity models.
Interactive map — scroll and click to explore.
Context
Bengaluru's rapid urbanization has strained its water-supply and sewage networks, resulting in frequent contamination of stormwater drains (SWDs) and lakes. Traditional monitoring is slow and lacks spatial resolution, making proactive risk mitigation difficult.
Problem Statement
How can heterogeneous public datasets — including BWSSB sewer and water lines, citizen water audits, monthly lake reports, and natural terrain slopes — be integrated to identify sewage-drain intersections, quantify contamination risk, and visualize hotspots for environmental planning?
Methodology
Built a spatial data pipeline in Python using GeoPandas, reprojecting to UTM Zone 43N. Scored drain segments using a 0-100 composite risk index based on sewage line proximity, manhole density, citizen audit reports (black water, odor, foam), drain-edge typology (property-adjacent or lake-adjacent), and public-health exposure metrics (water supply proximity). Integrated monthly lake water quality PDF data and slope classes from KML files.
Analysis
Data engine developed in Python (Pandas, GeoPandas, NumPy, PyPDF2, PDFplumber). Pipeline exports clean GeoJSON layers to a standalone GIS dashboard. Frontend built with MapLibre GL, featuring layers for primary/secondary drains, valley systems, sub-basin boundaries, stream order, slope classes, lake/groundwater quality, and interactive contamination heatmaps.
Insights
Spatial analysis revealed high-risk clusters at property-adjacent drain segments where informal sewage connections are common. Natural drainage stream orders highlighted how contamination propagates down-valley into existing lakes, while slope class overlays demonstrated that low-lying areas suffer from chronic stagnation and higher risk.
Outcome
A reproducible spatial analysis pipeline and an interactive MapLibre GL dashboard showing ward-level risk, high-risk pipeline segments, lake water quality grades, and groundwater contamination samples, enabling citizens and environmental planners to locate infrastructure vulnerabilities.