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bookcases-boite-a-lire

This repository host public bookcases exported from: https://www.boite-a-lire.com/

Note: boite-a-lire.com publishes those data under CC-BY-NC-SA license.

Those bookcases are mainly located in France and french speaking countries. You can display them in OsmAnd, JOSM, uMap etc.

View bookcases on a map

In this map:

  • OSM bookcases are displayed in green on zoom 14+ with 1 hour cache
  • boite-a-lire.com bookcases are displayed in red

Repo hierarchy

  • YYYY-MM-DD: date I export data from the website.
    • bookcases.gpx: favourite file to use in OsmAnd.
    • bookcases.geojson: file to use in JOSM.

WARNING! DO NOT IMPORT IN OPENSTREETMAP!

⚠️ DO NOT IMPORT THOSE DATA IN OPENSTREETMAP! ⚠️

Those "supposed bookcases" needs to be checked by a survey in real life, on the field.

You need to go on each "supposed bookcases", check if it exists, then you can add this bookcase in OSM.

How to use data ?

The main goal of those data is to find potential bookcases. So you can grab/exchange some books. You may also want to use those data to improve OpenStreetMap. But you need to check if those bookcases exists in real life before adding it in OpenStreetMap

With OsmAnd ?

NB: do not import "bookcases.gpx" because OsmAnd can be lagguy. Instead use one of the gpx in "Régions Françaises" folder.

  1. Download bookcases.gpx from the latest export
  2. Copy bookcases.gpx to your device
  3. Open OsmAnd on your device
  4. Go to Favorites
  5. Use the button + (import button)
  6. Select file bookcases.gpx
  7. It's done, bookcases are displayed on the map

Bookcases in OsmAnd

With JOSM ?

  1. Download bookcases.gpx from the latest export
  2. Use button Open File in JOSM
  3. Select file bookcases.gpx
  4. It's done, bookcase are displayed on the map

How to update data ?

Extract data from web site

  1. Visit https://www.boite-a-lire.com/
  2. Wait until the map is fully loaded
  3. Open debugger console of your web brower and run following script
// Initialize an array to hold non-null JSON objects
var jsonArray = [];

// Initialize a counter for consecutive null JSONs
var nullCount = 0;

// Variables to track the min and max indices of non-null JSONs
var minIndex = null;
var maxIndex = null;

// Loop through json0 to jsonXXXXX
for (var i = 0; i <= 99999; i++) {
    var jsonVar = window['json' + i];

    if (jsonVar !== null && jsonVar !== undefined) {
        jsonArray.push(jsonVar);
        nullCount = 0; // Reset null counter

        // Update min and max indices
        if (minIndex === null) {
            minIndex = i;
        }
        maxIndex = i;
    } else {
        console.log('Found null JSON at json' + i);
        nullCount++;
    }

    // Stop the loop if 10 consecutive null JSONs are found
    if (nullCount >= 10) {
        break;
    }
}

// Log the min and max indices of non-null JSONs
console.log('Min non-null JSON index: json' + minIndex);
console.log('Max non-null JSON index: json' + maxIndex);
// Log the length of the array
console.log('Total non-null JSON objects: ' + jsonArray.length);
// Convert JSON array to string
var jsonString = JSON.stringify(jsonArray, null, 2);
// Create a Blob from the JSON string
var blob = new Blob([jsonString], { type: "application/json" });
// Create a link element
var link = document.createElement("a");
// Set the download attribute with a filename
link.download = "data.json";
// Create a URL for the Blob and set it as the href attribute
link.href = window.URL.createObjectURL(blob);
// Append the link to the body
document.body.appendChild(link);
// Programmatically click the link to trigger the download
link.click();
// Remove the link from the document
document.body.removeChild(link);

Wait until the script finish. It will propose you a file to download. Save this file in a new folder with current date. Name the file data.json

Parse data

  1. Run step1ParseDataJson.py to produce file bookcases.geojson
  2. Run step2FilterBookcasesByRegions.py to split the bookcases in one geojson file for each french region
  3. Run step3ConvertGeojsonToOsmandGpx.py to convert each geojson file into a OsmAnd favorite GPX file

Stats

Region 2019-12-01 2022-09-17 2024-10-03
Total 5551 8756 9885
Duplicates 185 199
Uniques 8571 9686
Out France Metro 343 346
France métro 5291 8228 9340
Nouvelle Aquitaine 871 1307 1538
Ile de France 671 1050 1197
Auvergne Rhone-Alpes 646 1174 1356
Occitanie 436 736 877
Grand Est 448 673 767
Hauts de France 361 565 597
Pays de la Loire 361 519 559
Bretagne 359 504 555
Bourgogne Franche-Comte 288 437 482
Normandie 311 430 464
Centre Val de Loire 300 412 466
PACA 240 412 470
Corse 9 9 12