Gist d64af23db4c1bd6bbacbe72ef137865e

100%

Gist d64af23db4c1bd6bbacbe72ef137865e

✓ Published0🌍 Public

Last edited Nov 14, 2022

Created on Nov 10, 2022

This visualization shows the extraction and tabularization of French Business Tax Service (SIE) locations. It first filters the full public service dataset, downloaded as a JSON tarball via `wget`, using `jq` to select only SIE entries. It then validates the filtered results against the official website's paginated listing, scraped with `pup`. Finally, the code converts the filtered JSON into a simple CSV file using `jq` to output fields like id, name, and postal address, working within the Linux command-line environment.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 349fc7fda8932252cbcb4ee8a0b605c8

This example tracks the availability and licensing of the French IGN’s BD TOPO® departmental datasets by organizing URLs for both December 2021 and March 2021 releases. It shows a structured index of geographic data files, with each entry linking to a specific departmental Shapefile resource. The visualization uses a data-driven list format, likely rendered with JavaScript’s `fetch` to parse the provided CSV or JSON and display the sorted URLs. It highlights the variation in resource IDs across departments and time periods, emphasizing the dataset’s scope without mapping or spatial rendering.

TThomasG77

70% match

Loading thumbnail…

Gist c8b3e33ae90180e985646e1ef4876dfe

This example demonstrates how to convert tabular JSON data containing longitude and latitude fields into a GeoJSON FeatureCollection using the command-line tool jq, making it ready for map-based visualization. The dataset covers digital mediation locations in the Auvergne-Rhône-Alpes region of France, sourced from data.gouv.fr. The transformation maps each original record into a GeoJSON Feature, preserving properties like name, address, services, and accessibility conditions, while constructing Point geometry from the coordinate pair. The code includes a preview image showing the resulting spatial distribution of these locations, though the visualization itself uses no specific JavaScript mapping library or framework beyond the jq conversion process.

TThomasG77

68% match

Loading thumbnail…

Gist edaa46d0ee945829befb88a9e719f0e0

This visualization maps the number of certified organic food operators across French administrative departments, using a choropleth-style representation to reveal regional disparities in organic agriculture density. The data is sourced from the Agence Bio public API, which is queried via a shell script that iterates through department codes. The script uses `curl` and `jq` to extract the `nbTotal` count for each department, storing results in a semicolon-delimited text file. The final map likely employs `d3.geoPath` for rendering the department boundaries, with color intensity or a similar visual encoding driven by the fetched counts.

TThomasG77

67% match

Loading thumbnail…

Gist 32329a8557135f11cb5656e3bfd4d35c

A WebGL map visualizes the locations of French tobacco shops, using points to show their distribution. The code geocodes addresses from an official customs dataset via the data.gouv.fr API, then processes the CSV with the `requests` library. It uses a Vue.js front-end to render the points in a 3D space, allowing users to explore the spatial patterns of the shops.

TThomasG77

66% match

Loading thumbnail…

Consommation CSW Observatoire des territoires

This visualization catalogs the datasets available from the French government’s Observatoire des Territoires geospatial service. It displays a table of metadata—titles like “ZRR - Zone de revitalisation rurale” and “QPV - Quartiers prioritaires”—paired with their identifiers and direct URLs. Built with a Python script using the OWSLib library’s `CatalogueServiceWeb`, it queries the CSW endpoint, paginates through records, and exports the results to a CSV. The output is then presented in a Vue.js interface, showing the structured list of available territorial indicators.

TThomasG77

65% match

Loading thumbnail…

Gist 12513350e4462cdfb258efe4cb8df145

This map visualizes the locations of tobacco retailers in the Ain department of France, with each point geocoded from a public directory of licensed shops. A marker shows the position of every vendor, with details available for each location. The visualization likely uses a geographic rendering library such as D3.js with `d3.geoPath` to display the map, while the underlying data comes from the `annuaire-des-debits-de-tabac-deb.geocoded.geojson` file, which contains coordinates and metadata for each establishment.

TThomasG77

64% match

Loading thumbnail…

Gist 9ea54f64f11961c494767a8384892e83

This example demonstrates how to debug a geocoding failure by comparing two CSV files sent to the French government's geocoding API (api-adresse.data.gouv.fr). It shows that renaming the first column from `geocoder__line` to `geocoder__line_number` prevents rows from being silently dropped during the batch geocoding request. The shell script (`debug-geocoder-related-issue.sh`) uses `curl` to POST both files to the API, then displays the first eight geocoded results. The output files include latitude, longitude, and match scores, revealing how column naming affects API behavior.

TThomasG77

63% match

Loading thumbnail…

Lien Adresse et parcelles

This example shows the relationship between French addresses and land parcels, producing flat CSV files per department and for the whole country from the IGN BAN PLUS dataset. The workflow uses a bash pipeline combining Python with `xml.etree.ElementTree` to fix VRT files, GDAL’s `ogr2ogr` and `ogrinfo` for data conversion and indexing, `jq` to parse layer names, and `xsv` to concatenate departmental rows into a nationwide file. Results are gzip-compressed and organized into `france` and `departements` directories.

TThomasG77

63% match