Skip to main content
SimilarExport ZIP
100%

Multiple approaches to solve question at https://lists.osgeo.org/pipermail/gdal-dev/2022-June/055949.html

✓ Published0🌍 Public
TThomasG77
Last edited Jun 18, 2022
Created on Jun 17, 2022

This example demonstrates three equivalent methods for converting a CSV file with latitude and longitude columns into a GeoPackage with point geometry using GDAL’s `gdal.VectorTranslate`, GeoPandas’ `read_file` and `to_file`, and Fiona’s manual schema definition and feature writing. The CSV contains a single data point (61, -150), and each approach specifies the coordinate column names via `X_POSSIBLE_NAMES` and `Y_POSSIBLE_NAMES` options. The code shows how to set the output CRS to EPSG:4326 and assign a layer name (`layername`) in the resulting GeoPackage.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 1aa1bf66d847be043a246df8bf74793e

This example demonstrates how to load multiple CSV files containing Chinese-character field names as spatial layers in QGIS. The script uses the PyQGIS API, specifically `QgsVectorLayer` with the `delimitedtext` provider, to parse longitude and latitude columns named 經度 and 緯度. It iterates through all CSV files in a directory using Python’s `glob` and `os` modules, constructs a URI with URL encoding via `urllib.parse.quote`, and adds each resulting point layer to the project with `QgsProject.instance().addMapLayer`. The sample data shows simple occurrence records with coordinates.

TThomasG77
65% match
Loading thumbnail…

OGR and QGIS MySQL recipes

This example demonstrates how to convert non-spatial MySQL/MariaDB tables with x/y coordinates into viewable vector layers in QGIS. It shows three approaches: using OGR’s SQLite dialect with the `MakePoint` function, using MySQL’s native `ST_PointFromText` function, and creating a geometry column directly in the database. The code uses `ogrinfo`, `ogr2ogr`, and VRT files to bridge GDAL with MySQL, while the final workflow includes loading CSV data through the command line before exporting to the database.

TThomasG77
63% match
Loading thumbnail…

Load GTFS as GPKG with GDAL 3.7

This example demonstrates converting an IDFM GTFS transit feed into a GeoPackage database using GDAL’s new GTFS driver. The Bash script first checks that GDAL 3.7 is installed, then uses `ogrinfo` with `-json` to list all available layers. For each layer, it runs `ogr2ogr` with the GeoPackage format, appending layers incrementally with the `-update` flag. The source data is loaded directly from a URL via `/vsicurl/`, and the script allows customizing input and output names through environment variables.

TThomasG77
63% match
Loading thumbnail…

Reprojecting CSV with ogr2ogr

This example demonstrates how to convert a CSV file containing projected coordinates (EPSG:2263) into unprojected longitude and latitude values (EPSG:4326). The conversion relies on a virtual dataset defined in a VRT file, which specifies the source CSV, its geometry type, and the source coordinate system. The command-line utility `ogr2ogr` from the GDAL library performs the reprojection, outputting a new CSV with the transformed coordinates. The VRT uses the `PointFromColumns` geometry encoding to interpret the X and Y columns as spatial points.

Mmbostock
61% match
Loading thumbnail…

Play with gdal.vectorTranslate in Node.js

This example demonstrates a complete geospatial data processing pipeline using the Node.js `gdal-async` library. It downloads a Natural Earth countries dataset, extracts the ZIP archive, and converts the shapefile to GeoPackage format with `gdal.vectorTranslate`, then transforms the GeoPackage into GeoJSON. The script uses the `got` library for HTTP streaming with progress reporting and `adm-zip` for extraction. This showcases how GDAL's vector translation capabilities can be invoked programmatically in a Node.js environment for format conversion between common geospatial file types.

TThomasG77
60% match
Loading thumbnail…

Gist 56db21b335e25668ff86c2e8359bf3d1

This example visualizes tree inventory data from a CSV file, mapping each tree’s location as a point based on longitude and latitude coordinates. The data includes attributes such as species, planting date, height, and diameter. The implementation uses a VRT file to define the CSV’s geometry, with the QGIS XML configuration specifying the point layer’s symbology and coordinate system. The visualization is rendered through QGIS, leveraging the OGR provider to read the spatial data and display it as a map layer with a single symbol renderer.

TThomasG77
60% match
Loading thumbnail…

Gist f2c843666c84195a872efebcc21f1f9f

This PyQGIS script exports the column metadata of an active PostgreSQL layer to a CSV file. It shows the structure of every column, including data type, nullability, precision, defaults, and comments from the `information_schema.columns` view. Working within QGIS, it uses `QgsProviderRegistry` to create a PostgreSQL connection, executes the query via `executeSql`, sanitizes null `QVariant` values, and writes the results with Python’s `csv` module. The final CSV file is then added back to the map as a vector layer.

TThomasG77
59% match
Loading thumbnail…

VRT recipe for CSV

This example demonstrates how to convert a CSV file containing building bounding box coordinates into a polygon vector layer using a VRT (Virtual Dataset) recipe. The visualization shows a map of building footprints as rectangles derived from the xmin, xmax, ymin, and ymax columns in the CSV. It works by using an OGRVRTDataSource with SQLite dialect in the `demo-sql-csv.vrt` file, where the `ST_PolygonFromText` function constructs spatial geometries from the coordinate data. The technique is presented as a lightweight alternative to processing GeoJSON files, and can be viewed in QGIS or inspected via GDAL command-line tools like `ogrinfo`.

TThomasG77
59% match