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Download Road Network Shapefiles






Best Websites to Download Road Network Shapefiles for Free

If you are looking for high-quality road network data in Shapefile (.shp) format, several reliable sources provide free and accurate datasets. These datasets are essential for GIS projects, spatial analysis, urban planning, and transportation studies. Below, we highlight some of the best websites where you can download road network Shapefiles easily.

1. OpenStreetMap (OSM) – via Geofabrik & BBBike

📌 Overview:
OpenStreetMap (OSM) is one of the most comprehensive sources for global road network data. Since OSM is an open-source mapping project, it provides freely available road network datasets with frequent updates.

🔹 Geofabrik – Offers OSM data in ready-to-use formats, including Shapefile, categorized by continent, country, and region.
🔹 BBBike – Allows users to extract custom road network data in different formats, including Shapefile, for specific areas.

🔗 Download from Geofabrik:   https://download.geofabrik.de/  
🔗 Download from BBBike:   https://extract.bbbike.org/ 

2. Global Roads Open Access Data Set (gROADS)

📌 Overview:
The Global Roads Open Access Data Set (gROADS) provides a worldwide dataset of major and secondary roads. It is particularly useful for global-scale transportation and environmental research.

🔗 Download from SEDAC (Columbia University):   https://sedac.ciesin.columbia.edu/data/collection/groads 

3. Natural Earth

📌 Overview:
Natural Earth offers a generalized road network dataset that includes highways, primary roads, and secondary roads at a global scale. The data is best suited for cartographic and visualization purposes rather than detailed routing analysis.

🔗 Download from Natural Earth:   https://www.naturalearthdata.com/ 

4. DIVA-GIS

📌 Overview:
DIVA-GIS provides free GIS data, including road networks, for various countries. It is a great resource for researchers, students, and professionals needing quick access to road data in Shapefile format.

🔗 Download from DIVA-GIS:   http://www.diva-gis.org/Data  

 5. from QGIS using QuickOSM Tool

📌 Overview:
QuickOSM is a QGIS plugin that allows you to easily extract OpenStreetMap data, including road networks, directly into QGIS. Here's how you can use it to get road data:

Steps to Extract Road Networks using QuickOSM:

  1. Install the QuickOSM Plugin:

    • Open QGIS and go to the Plugins menu.
    • Select Manage and Install Plugins.
    • Search for QuickOSM, then click Install.
  2. Open QuickOSM Tool:

    • After installation, go to the Vector menu and select QuickOSM > QuickOSM.
  3. Select Data Type:

    • In the QuickOSM window, enter the key (e.g., "highway") and value (e.g., "residential", "primary", etc.) to specify the type of roads you want to extract.
  4. Set the Area of Interest:

    • You can select a specific area by either drawing a bounding box on the map or entering coordinates.
  5. Run the Query:

    • Click the Run Query button. The plugin will download the road data from OpenStreetMap for the selected area and type.
  6. Export the Data:

    • Once the data is loaded into QGIS, you can export the roads layer as a Shapefile (.shp) by right-clicking on the layer and selecting Export > Save Features As.
  7. Save and Use the Data:

    • Save the layer to your desired location and use it for further GIS analysis or mapping.

By following these steps, you can easily extract road networks directly into QGIS using the QuickOSM plugin!


Conclusion

When selecting a road network dataset, consider the level of detail, update frequency, and coverage you need.

For highly detailed and frequently updated road data: OpenStreetMap (via Geofabrik or BBBike) is the best option.
For global-scale road networks: gROADS and HDX provide valuable datasets.
For cartographic visualization: Natural Earth offers simplified road layers.

By using these resources, you can access high-quality road network data for your GIS projects quickly and efficiently.

Do you have a favorite source for downloading road network Shapefiles? Let us know in the comments! 🚀

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