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Analytics Tools for Carbon Footprint Tracking Platform

How we optimized maps, implemented analytics tools, and optimized the platform.


Sustainable production


3 months


Web development



YWS > Works > CaseStudy > Carbon Space > Intro > Content > Lines
YWS > Works > CaseStudy > Carbon Space > Project Idea > Image content > Image

Project Idea

Our client is a global service that allows its users to track CO2 emissions from farms and land plots. The company needed a hand with the platform’s frontend: It required a set of analytics tools and optimized map clustering to upgrade the user experience.

The client had
  • A functional platform

We were responsible for
  • Building analytics tools

  • Optimizing map clustering

  • Integrating new API

Project team
  • Project manager

  • Two frontend engineers (React)

Technology Stack

YWS > Works > CaseStudy > Carbon Space > Technology Stack > Desktop imageYWS > Works > CaseStudy > Carbon Space > Technology Stack > Mobile image

Key features


  • Map clustering algorithm

  • Color schemes for different pollution levels (Red for high CO2 pollution level. Green for low CO2 pollution level)

  • Dynamic side panel with information about clusters (Country/state/city, general area, the number of fields, CO2 pollution index)

YWS > Works > CaseStudy > Carbon Space > Key features > Maps > Image


  • Side analytics for seeing data by fields that are displayed specifically on the map

  • Custom charts

  • The ability to download the report

YWS > Works > CaseStudy > Carbon Space > Key features > Analytics > ImageYWS > Works > CaseStudy > Carbon Space > Key features > Analytics > Lines

API integration

  • Optimized client-side and server-side

  • Data caching for faster loading

YWS > Works > CaseStudy > Carbon Space > Key features > API integration > Image

Challenges and Solutions

Optimizing maps and API integration


We needed to implement statistics screens that would show relevant data about farm territories. Statistics show a detailed report on all polluted and clean zones. This feature should also allow users to create custom widgets with specified presets for the observation period.


We developed map rendering and clustering methods to reduce the number of requests to the server and the load on the user's device when rendering the map. We also implemented custom rendering of React components for dynamic widgets and user statistics screens.


The algorithms we implemented showed high application performance in the Lighthouse test.

The platform became flexible so new functions can be added without damaging app metrics.