How to install osm2geojson with pip?

Run 'pip install osm2geojson' from the command line, as specified in the README's installation section. It's available on PyPI and compatible with Python 3.8+ environments.

osm2geojson vs geopandas for converting OSM data?

osm2geojson is specialized for direct OSM/Overpass format conversion to GeoJSON with built-in API support, while geopandas is a broader GIS library that may require additional steps to handle raw OSM data. Choose osm2geojson for focused conversion tasks.

Can osm2geojson handle large OSM XML files efficiently?

It processes data in memory, so performance may degrade with very large files. The README doesn't provide streaming or chunking options, so for big datasets, consider splitting inputs or using alternative tools.

How to use osm2geojson in a Flask web app?

Import the library in your Flask routes, call functions like json2geojson on Overpass query results, and return GeoJSON responses. The README's examples show basic integration, but you'll need to handle API calls and error management.

What are the default polygon features in osm2geojson?

It uses predefined lists from external files like polygon-features.json, which can be customized via the polygon_features parameter. The README includes an update script to refresh these definitions from upstream sources.

Is osm2geojson good for real-time geospatial applications?

Not ideal for real-time needs due to potential latency in Python processing and Overpass API calls. It's better suited for batch conversions or offline workflows where speed isn't critical.

Open-Awesome

osm2geojson

MITPythonv1.0.0rc1

A Python library for converting OpenStreetMap and Overpass API XML/JSON data into GeoJSON format.

GitHub

113 stars18 forks0 contributors

What is osm2geojson?

osm2geojson is a Python library that converts OpenStreetMap (OSM) and Overpass API data from XML or JSON formats into GeoJSON. It solves the problem of integrating raw OSM data into modern mapping applications and GIS tools that require the GeoJSON standard, providing a clean and programmable interface for this transformation.

Target Audience

Geospatial developers, data scientists, and GIS professionals who work with OpenStreetMap data and need to convert it into GeoJSON for visualization, analysis, or integration with other mapping libraries.

Value Proposition

Developers choose osm2geojson because it offers a dedicated, well-tested Python solution for OSM-to-GeoJSON conversion with support for both XML and JSON inputs, direct Overpass API querying, and the flexibility of intermediate Shapely geometry objects.

Overview

Convert OSM and Overpass XML/JSON to GeoJSON

Use Cases

Best For

Converting OSM XML exports to GeoJSON for web mapping
Processing Overpass API query results into GeoJSON features
Integrating OpenStreetMap data into Python-based GIS workflows
Creating custom geospatial data pipelines that require GeoJSON output
Educational projects teaching OSM data processing and conversion
Building tools that need to manipulate OSM geometries with Shapely

Not Ideal For

Projects built on non-Python stacks (e.g., JavaScript/Node.js) that need client-side OSM conversion
High-performance batch processing pipelines requiring optimized C++ or Rust backends for large datasets
Teams needing GUI-based tools for non-technical users to convert OSM data without coding

Pros & Cons

Pros

Dual Format Support

Handles both OSM/Overpass XML and JSON inputs, offering flexibility for various data sources as highlighted in the README's key features.

Shapely Geometry Integration

Provides intermediate Shape objects with Shapely geometries, enabling advanced geometric operations and custom processing before GeoJSON conversion, demonstrated in the examples.

Direct Overpass Querying

Includes a helper function to execute Overpass API queries with automatic retries, simplifying the workflow from data fetching to conversion, as shown in the usage section.

Customizable Conversion Options

Offers parameters like filter_used_refs and polygon_features for fine-tuned control, allowing developers to tailor output based on specific needs, detailed in the API reference.

Cons

Development Stage Limitations

Explicitly marked as 'under development' in the README, with a ToDo list indicating missing features like support for complex multipolygons and comprehensive CLI tests, which could affect stability.

Performance Scalability Issues

As a Python library, it may struggle with very large OSM datasets due to in-memory processing, and the README lacks performance benchmarks or streaming options for efficiency.

External Dependency Management

Relies on external data files for polygon features and area keys that require manual updates via scripts, potentially introducing maintenance overhead and compatibility risks.

Frequently Asked Questions

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