PyrOSM is a package that allows for efficient geospatial manipulations of Open Street Map (OSM) data. It uses Cython and faster libraries to process OSM data quickly. The package supports features like buildings, points of interest, street networks, custom filters, and exporting as networks. PyrOSM also provides better filtering options and allows for network processing and integration with other analysis libraries. Overall, PyrOSM bridges the gap between large OSM datasets and the analytical questions that can be answered with them.
Efficient geospatial manipulations for OSM map data
If you’ve ever worked with OSM data before, you know that it can be challenging to extract and analyze. But with PyrOSM, you can make the process much more efficient. PyrOSM is built on Cython and uses faster libraries for processing OSM data, allowing you to work with large datasets much faster than before.
PBF Data
OSM data is stored in a file format called PBF, which stands for “Protocolbuffer Binary Format”. This format is very efficient for working with OSM data. The data is organized into fileblocks, which contain thousands of OSM entities like nodes, ways, and relations.
PyrOSM Basics: reading in datasets
PyrOSM is a package that reads in OSM data based on two main data distributors: Geofabrik and BBBike. It allows you to access various types of features such as buildings, points of interest, street networks, and custom filters.
To get started, you simply initialize an OSM reader object and load in the data you want. For example:
reader = pyrosm.OSM("./data.osm.pbf")
From there, you can interact with the OSM data and perform various analyses. For example, you can extract a street network for a specific area:
street_network = reader.get_network(network_type="driving")
The resulting street network is stored in a GeoPandas GeoDataFrame, which contains attributes like length, highway, and maxspeed, making it easy to perform further analysis.
Better Filtering
If you want to focus on a specific area, you can use bounding box filtering. This allows you to define a smaller or more specific area of interest. You can specify the bounding box coordinates manually or use Shapely geometries. For example:
bbox = [minx, miny, maxx, maxy]
network = reader.get_network(network_type="walking", bounding_box=bbox)
Exporting and Working with Graphs
PyrOSM allows you to save street networks as GeoDataFrames, as well as extract nodes and edges into separate dataframes. This makes it easy to work with other network analysis libraries and save the data in different formats like OSMnx, igraph, and Pandana.
Conclusion
PyrOSM is a powerful tool for working with OSM data. It provides efficient geospatial manipulations, better filtering options, and the ability to export and work with graphs. With PyrOSM, you can unlock the potential of OSM data and leverage it to improve your business processes.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- PyrOSM: working with Open Street Map data
- Towards Data Science – Medium
- Twitter – @itinaicom
