osmnx vs prettymaps
Side-by-side comparison of features, pricing, ratings, and alternatives.
osmnx is a Python library for downloading, modeling, analyzing, and visualizing street networks and other geospatial features from OpenStreetMap. It allows users to easily retrieve and manipulate street network data, perform network analysis, and visualize the results. osmnx is designed to be easy to use and provides a simple, intuitive API for working with street network data.
Prettymaps is a software that allows users to create visually appealing maps from OpenStreetMap data. It utilizes osmnx, matplotlib, and shapely to generate maps that are both informative and aesthetically pleasing. Prettymaps is ideal for users who want to create maps for various purposes, such as urban planning, research, or simply for visualizing geographic data.
- Easy to use and intuitive API
- Support for multiple street network formats
- Extensive documentation and example code
- Free and open-source
- Easy to use
- High-quality map visualizations
- Customizable
- Open source
- Limited support for non-geospatial data
- Dependent on OpenStreetMap data quality
- Steep learning curve for advanced features
- Limited documentation
- Steep learning curve for advanced features
- Dependent on OpenStreetMap data quality
More alternatives & similar tools
Alternatives to osmnx
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The Verdict
AI-generated from listing dataosmnx is the safer default for comprehensive street‑network analysis, while prettymaps is best if you only need high‑quality, customizable map visualizations.
Key differences
- •Scope: osmnx provides full network analysis (shortest paths, centrality) whereas prettymaps focuses on map rendering.
- •Documentation: osmnx lists extensive docs and examples; prettymaps notes limited documentation.
- •Deployment model: osmnx is cloud/SaaS‑oriented; prettymaps is a desktop‑app library.
- •Integrations: osmnx includes NetworkX, Folium, Geopandas; prettymaps adds shapely but lacks those network‑analysis tools.
Pricing & value
Both are free and open‑source, offering comparable cost advantage.
Ease of use / learning curve
Prettymaps is described as easy to use, while osmnx has a steep learning curve for advanced features.
Features & depth
osmnx supports network analysis, multiple formats, and extensive geospatial functions; prettymaps is limited to visualization.
Integrations & ecosystem
osmnx integrates with NetworkX, Folium, Geopandas, Fiona; prettymaps integrates mainly with matplotlib and shapely.
Collaboration / support
Both rely on GitHub Issues; prettymaps adds GitHub Discussions, but support depth is not specified.
Scalability
osmnx is deployed as cloud/SaaS, suggesting better scalability than prettymaps' desktop‑app model.
Security & privacy
No security or privacy details provided for either tool.
Choose osmnx if…
Researchers needing full street‑network analysis and programmable data pipelines.
Choose prettymaps if…
Planners who only require attractive, customizable OSM map visualizations.
Common questions
Is there any cost to use either tool?
Both osmnx and prettymaps are free and open‑source.
Which tool offers more documentation?
osmnx lists extensive documentation and example code; prettymaps notes limited documentation.
Can I perform network analysis (e.g., shortest paths) with prettymaps?
No; network analysis is a core feature of osmnx, not of prettymaps.