Exploratory Data Analysis | Krutarth Ghuge

Overview

About this project

A two-part environmental data analysis project combining global emissions tracking through the Climate Trace API with satellite-based land-use analysis via Google Earth Engine.

Both projects translate raw environmental datasets into actionable visual insights — from sector-level emission breakdowns to geospatial maps of deforestation and urban expansion.

Python Pandas NumPy Matplotlib GeoPandas Cartopy Google Earth Engine Climate Trace API

50%

Processing time reduction

2

Major data sources

GEE

Satellite platform

API

Climate Trace

View on GitHub

Projects

Analysis breakdown

Climate Trace Project

Emissions · API

Analyzed global emissions data using the Climate Trace API, identifying pollution sources and tracking emission trends at country and industry level.

Steps

Data Collection: Gathered multi-country, multi-sector emissions data via the Climate Trace API
Analysis: Applied sector-level breakdowns and tracked high-pollution regions across time
Geospatial Mapping: Mapped emissions globally using GeoPandas and Cartopy
Reporting: Built visualizations of gas types, sectors, and top contributors

Outcomes

Detailed emissions profiles Leading global polluters identified Interactive plots and maps Sector-wise trend dashboards

Google Earth Engine Project

Satellite · GEE

Analyzed environmental change over time using satellite imagery from Google Earth Engine, focusing on temperature shifts, air quality, deforestation, and urbanization trends derived from MODIS and NDVI datasets.

Steps

Set up Earth Engine API and authenticated access via Google Cloud
Analyzed MODIS, NDVI, and related satellite data sources
Mapped changes in land cover, surface temperature, and atmospheric pollution
Generated time-series visualizations of vegetation and urban growth

Outcomes

Temperature shift maps Vegetation index trends Deforestation visualization Urban expansion mapping Environmental risk areas identified

Technical

Setup details

Climate Trace

Registered for API access and obtained keys
Installed requests, pandas, and visualization libraries
Developed scripts to extract and format emissions data
Built plotting pipeline for multi-sector analysis

Google Earth Engine

Enabled GEE API via Google Cloud Console
Authenticated user token and installed Python SDK
Analyzed satellite data through the GEE code editor
Exported processed datasets for downstream visualization