Turf vs PostGIS: which should I use?

Turf is best for JavaScript or TypeScript applications needing geospatial analysis in the browser or Node.js, offering easy integration and modularity. PostGIS is superior for server-side, database-driven applications with SQL queries and large-scale data processing. Choose Turf for full-stack JS apps and PostGIS for heavy backend GIS with PostgreSQL.

How to calculate distance between two points in Turf?

Use the @turf/distance module. Install it via npm, then import and call the distance function with two GeoJSON points and optional units. For example, `distance(point1, point2, {units: 'kilometers'})` returns the distance in kilometers, leveraging Turf's spatial utilities.

Can Turf handle 3D geometry operations?

Turf primarily focuses on 2D geospatial analysis with GeoJSON, which supports 2D coordinates. While some functions might work with elevation data, it lacks dedicated 3D spatial operations compared to specialized GIS software, so check the documentation for specific capabilities.

What browsers are supported by Turf?

Turf supports browsers that match the Browserslist criteria: >0.25% market share, last 2 versions, fully supports ES5, and not dead. It uses Babel for transpilation to ensure compatibility with most modern browsers, as detailed in the README.

How to integrate Turf with React and Mapbox?

Install Turf modules via npm and use them alongside React and Mapbox GL JS. Perform spatial calculations on the client-side and update map layers dynamically—ideal for features like real-time proximity analysis or data visualization in interactive web maps.

Does Turf work well with large datasets?

Turf is written in JavaScript, so performance can degrade with very large datasets compared to native libraries. For optimal results, process data in chunks or on the server-side with Node.js, and consider its modular imports to minimize overhead in browser applications.

Turf.js — Geospatial Analysis JavaScript Library

What is Turf.js?

Turf is a modular geospatial engine written in JavaScript and TypeScript that provides a comprehensive library for spatial analysis. It solves the problem of performing complex geospatial operations—like calculating distances, creating buffers, or analyzing spatial relationships—directly within JavaScript applications, eliminating the need for external GIS servers or complex backend setups.

Target Audience

JavaScript and TypeScript developers building mapping applications, location-based services, or any software requiring geospatial analysis, including web developers, GIS professionals, and data scientists working with spatial data.

Value Proposition

Developers choose Turf because it offers a pure JavaScript solution for geospatial processing, with a modular design that allows for lightweight imports, broad browser and Node.js support, and a rich set of functions that rival traditional GIS tools, all within a familiar ecosystem.

A modular geospatial engine written in JavaScript and TypeScript

Use Cases

Best For

Adding advanced spatial analysis to web mapping applications
Processing GeoJSON data in Node.js backends
Building custom GIS tools without external dependencies
Performing real-time geospatial calculations in the browser
Creating data visualizations with statistical classification of spatial data
Developing location-based services with routing or proximity analysis

Not Ideal For

Projects exclusively using Deno or Bun runtimes, as Turf does not officially support them
Applications requiring real-time, high-performance geospatial processing on massive datasets, where native or compiled languages might be better suited
Teams needing an all-in-one GIS platform with graphical interfaces, since Turf is strictly a programmatic library for analysis
Systems that primarily use spatial data formats other than GeoJSON, as Turf's utilities are optimized for GeoJSON manipulation

Pros & Cons

Pros

Modular Architecture

Functions are packaged as independent modules, allowing lightweight, targeted imports that reduce bundle size and improve performance in client-side applications, as highlighted in the README's emphasis on modularity.

Cross-Platform Compatibility

Runs seamlessly in modern browsers and on Node.js servers, enabling consistent geospatial logic across full-stack JavaScript applications, with broad runtime support per the README.

Comprehensive GeoJSON Support

Provides extensive helper functions for creating, validating, and manipulating GeoJSON data structures, making it easy to work with standard spatial formats, as noted in the key features.

Rich Spatial Analysis

Includes a wide range of traditional operations like buffering, intersection, and distance calculations, rivaling traditional GIS tools for advanced mapping and location-based features.

Cons

Limited Runtime Support

Officially supports only Node.js and browsers, with no guarantees for emerging runtimes like Deno or Bun, which could hinder adoption in some environments, as the README explicitly states.

Performance Constraints in JavaScript

Being a JavaScript library, it may not match the performance of native or compiled geospatial libraries for CPU-intensive operations on large datasets, potentially slowing down data-heavy applications.

Dependency Management Complexity

The modular design requires careful management of individual function imports, which can lead to versioning issues and increased setup overhead compared to monolithic libraries.

What is Turf.js?

Target Audience

Value Proposition

Use Cases

Best For

Adding advanced spatial analysis to web mapping applications
Processing GeoJSON data in Node.js backends
Building custom GIS tools without external dependencies
Performing real-time geospatial calculations in the browser
Creating data visualizations with statistical classification of spatial data
Developing location-based services with routing or proximity analysis

Not Ideal For

Projects exclusively using Deno or Bun runtimes, as Turf does not officially support them
Applications requiring real-time, high-performance geospatial processing on massive datasets, where native or compiled languages might be better suited
Teams needing an all-in-one GIS platform with graphical interfaces, since Turf is strictly a programmatic library for analysis
Systems that primarily use spatial data formats other than GeoJSON, as Turf's utilities are optimized for GeoJSON manipulation

Pros & Cons

Pros

Modular Architecture

Cross-Platform Compatibility

Runs seamlessly in modern browsers and on Node.js servers, enabling consistent geospatial logic across full-stack JavaScript applications, with broad runtime support per the README.

Comprehensive GeoJSON Support

Provides extensive helper functions for creating, validating, and manipulating GeoJSON data structures, making it easy to work with standard spatial formats, as noted in the key features.

Rich Spatial Analysis

Includes a wide range of traditional operations like buffering, intersection, and distance calculations, rivaling traditional GIS tools for advanced mapping and location-based features.

Cons

Limited Runtime Support

Officially supports only Node.js and browsers, with no guarantees for emerging runtimes like Deno or Bun, which could hinder adoption in some environments, as the README explicitly states.

Performance Constraints in JavaScript

Dependency Management Complexity

The modular design requires careful management of individual function imports, which can lead to versioning issues and increased setup overhead compared to monolithic libraries.

Turf.js

What is Turf.js?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

Turf.js

What is Turf.js?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?