TECHNICAL PROJECT INTERNSHIP · INTEL · MAY–JUL 2023
Turning raw accident datainto mapped insights.
Building a data collection and geospatial mapping pipeline to identify and visualise accident black spots across India as part of Intel's IRASTE road-safety initiative.
01 / THE CONTEXT
Making road accident data easier to understand geographically.
During my internship at Intel, I worked on a data-focused project supporting the IRASTE road-safety initiative. The objective was to collect and process information about identified road accident black spots from publicly available government sources.
The information existed across different websites and formats. The challenge was to extract relevant locations, convert them into geographic coordinates and organise them for mapping.
02 / THE APPROACH
From scattered information to geospatial data.
I independently worked through the data collection and mapping process, starting with publicly available information from the Ministry of Road Transport and Highways and state police websites.
Source identification
Identified government websites containing information about road accidents and accident black spots.
Data extraction
Used Python-based web scraping to collect raw information and extract relevant place and location names.
Geocoding
Used the Google Maps API to convert extracted location names into geographic coordinates.
Data cleaning and processing
Processed and cleaned the collected data to prepare it for geographic visualisation.
Geospatial mapping
Used QGIS to map the processed coordinates and create a geographic representation of accident black spots.
03 / THE OUTPUT
Building a nationwide view of accident black spots.
The resulting geospatial dataset provided a mapped view of identified accident black spots across India. The output was used by Intel in the broader IRASTE project and its AI-based road-safety model work.
04 / WHAT I LEARNED
Technology becomes useful when data becomes actionable.
This project gave me hands-on experience in working with unstructured public data and taking it through an end-to-end processing pipeline. I learned how web scraping, APIs, data cleaning and geospatial tools can work together to solve a real-world problem.
It also shaped how I think about technology: the value is not just in collecting data, but in making it structured, interpretable and useful for the people working with it.