How to Use the Command 'ogrmerge.py' (with Examples)

ogrmerge.py is a utility from the Geospatial Data Abstraction Library (GDAL) suite, designed to handle and merge multiple vector datasets seamlessly. This command allows users to consolidate various geospatial files into a single output, which can then be used for further geospatial analysis or visualizations. By supporting numerous formats, ogrmerge.py is quite versatile, fulfilling tasks ranging from file conversion and consolidation to enhancing geospatial workflows.

Use Case 1: Create a GeoPackage with a Layer for Each Input Shapefile

Code:

ogrmerge.py -f GPKG -o path/to/output.gpkg path/to/input1.shp path/to/input2.shp ...

Motivation:

In geospatial projects, data often comes fragmented across different shapefiles, each reflecting different aspects of the same geographic area. To streamline data processing, it’s beneficial to consolidate these shapefiles into a single, robust GeoPackage. A GeoPackage is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information. Thus, merging input shapefiles into one GeoPackage with separate layers improves data management and eases the subsequent analysis in GIS applications.

Explanation:

• -f GPKG: This specifies the format of the output, setting it to GeoPackage. GeoPackages are efficient for storing spatial data and allow for hosting multiple layers in a single file.
• -o path/to/output.gpkg: This defines the path and name for the output GeoPackage file.
• path/to/input1.shp path/to/input2.shp ...: These are the paths to the input shapefiles that will be merged into the GeoPackage. Each shapefile will become a layer in the output GeoPackage.

Example output:

A single GeoPackage file named output.gpkg containing individual layers, each named after the respective input shapefile, consolidating all the spatial data provided into one file. For GIS analysts, this single file is more manageable and efficient to use for spatial analysis or map design.

Use Case 2: Create a Virtual Datasource (VRT) with a Layer for Each Input GeoJSON

Code:

ogrmerge.py -f VRT -o path/to/output.vrt path/to/input1.geojson path/to/input2.geojson ...

Motivation:

For large datasets and computational analyses that do not necessitate physical file amalgamation, a Virtual Datasource (VRT) serves as an ideal solution. It provides a way to virtually represent different datasets under one umbrella without actually merging them into a single physical file. This is particularly useful for applications requiring immediate data usage without the overhead of file conversions or duplications, especially with formats like GeoJSON that are often used for web applications and APIs.

Explanation:

• -f VRT: This sets the format of the output to be a Virtual Datasource. VRTs allow for referencing multiple datasets in a single file without physically merging them, facilitating their use as a single dataset in applications.
• -o path/to/output.vrt: This specifies the destination and name of the resulting VRT file.
• path/to/input1.geojson path/to/input2.geojson ...: These indicate the paths to the input GeoJSON files to be included in the VRT. Each GeoJSON is represented as a layer within the virtual file.

Example output:

A .vrt file named output.vrt that virtually links to layers from each input GeoJSON file. This format is ideal for applications involving large datasets or when on-the-fly data analysis is needed without the requirement to duplicate or convert dataset formats.

Use Case 3: Concatenate Two Vector Datasets and Store Source Name of Dataset in Attribute ‘source_name’

Code:

ogrmerge.py -single -f GeoJSON -o path/to/output.geojson -src_layer_field_name country source_name path/to/input1.shp path/to/input2.shp ...

Motivation:

When dealing with multiple datasets representing different geographic or categorical elements, there may be a need to concatenate them into one singular dataset. In certain analytic scenarios, it’s beneficial to log the origin of each feature to maintain data integrity and traceability. By merging these datasets while adding an attribute, you maintain valuable metadata about the dataset origins, assisting in organizing by source, area coverage, or data type.

Explanation:

• -single: This option tells the command to merge all input datasets into a single layer within the output file.
• -f GeoJSON: Specifies GeoJSON as the output format, which is a widely-recognized format for web-based geographic applications.
• -o path/to/output.geojson: Specifies the output file location and name.
• -src_layer_field_name country source_name: This command option creates a new attribute named country and assigns it the value of source_name, which is automatically populated with the input dataset’s name for each feature.
• path/to/input1.shp path/to/input2.shp ...: These input shapefiles are the datasets that will be concatenated into the single output GeoJSON file.

Example output:

A seamless GeoJSON file named output.geojson where all input vector data is combined into one layer. Each feature now includes a country attribute with the dataset’s name from which it originated. This comprehensive file is valuable for large-scale mapping projects where understanding data lineage and maintaining attribute consistency is imperative.

Conclusion:

The ogrmerge.py command is a powerful tool in the GIS professional’s arsenal, simplifying data management through efficient merging and file handling capabilities. Whether you’re consolidating shapefiles into a GeoPackage, linking datasets virtually using VRT, or concatenating datasets while tracking their sources, ogrmerge.py offers a solution tailored to various geospatial data needs.