2b:null 2c:["$","title",null,{"children":"Geographical features imports (gdal3.js) | Matthieu Viry | Observable"}] 31:T44e,Following [@ambassadors/geographical-features-imports](https://observablehq.com/@ambassadors/geographical-features-imports) that demonstrated that several libraries needed to be used when dealing with a variety of geospatial formats on the Web, we now show how [gdal3.js](https://github.com/bugra9/gdal3.js) can be an option to read (almost) any geospatial formats. [gdal3.js](https://github.com/bugra9/gdal3.js) is an awesome effort that port [GDAL/OGR](https://gdal.org/) applications (`gdal_translate`, `ogr2ogr`, `gdal_rasterize`, `gdalwarp`, `gdaltransform`) to [WebAssembly](https://webassembly.org/). In this notebook, we focus on vector formats (and so on using `ogr2ogr`). We use the same data than [@ambassadors/geographical-features-imports](https://observablehq.com/@ambassadors/geographical-features-imports) and we even import a few more formats. See [@tombor/hello-gdal3](https://observablehq.com/@tombor/hello-gdal3) about using gdal3.js with raster formats. > __*Be aware that this notebook requires the loading of a lot of data (gdal3.js and the various data to transform).*__ 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2f:{"id":"cd353718aeb7d86e","slug":"geographical-features-imports-gdal3-js","title":"Geographical features imports (gdal3.js)","thumbnail":"b26b5ece3fa478c81321a5b6cc4f5cd96e1f396cf4a10453f891e94464e27e3f","access_level":"public","create_time":"$D2022-10-05T08:15:20.188Z","update_time":"$D2022-10-31T21:10:34.424Z","view_time":null,"first_public_time":"$D2022-10-10T13:41:23.381Z","fork":{"id":"2790103a64324727","slug":"geographical-features-imports","title":"Geographical features imports","owner":{"id":"11bfeb572ddbe905","login":"ambassadors","name":"Observable Ambassadors"}},"stars":7,"starred":false,"owner":{"type":"team","id":"25213e2b9755731d","login":"mthh","name":"Matthieu Viry","avatar_url":"https://avatars-next.observableusercontent.com/911bd8720f0dbfd44850e291921253c0242ac77c4537d932fde7db5c82ebb267","tier":"starter_2024"},"version":1018,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8e230b124d25dec4","login":"mthh","name":"Matthieu Viry","avatar_url":"https://avatars-next.observableusercontent.com/911bd8720f0dbfd44850e291921253c0242ac77c4537d932fde7db5c82ebb267","tier":null}],"members":[{"type":"individual","id":"8e230b124d25dec4","login":"mthh","name":"Matthieu Viry","avatar_url":"https://avatars-next.observableusercontent.com/911bd8720f0dbfd44850e291921253c0242ac77c4537d932fde7db5c82ebb267","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"Geographical features imports (gdal3.js)","cells":[{"id":0,"value":"# Geographical features imports (gdal3.js)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":194,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":743,"value":"## 0. Wrapping ogr2ogr into a `toGeoJSON` function","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":789,"value":"We write a function that wraps several functions of gdal3.js :\n- open the source dataset file(s) (with *open options* if any),\n- call ogr2ogr by giving it as input the source dataset and the options that say we want a GeoJSON output in the EPSG:4326 CRS,\n- read the result (which comes as an `Uint8Array`),\n- close the input dataset,\n- convert the result to text, parse it to get a JS object and return it.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":733,"value":"toGeoJSON = async (fileOrFiles, params = { opts: [], openOpts: []}) => {\n const openOptions = params.openOpts || [];\n const input = await Gdal.open(fileOrFiles, openOptions);\n const options = [\n '-f', 'GeoJSON',\n '-t_srs', 'EPSG:4326',\n '-lco', 'RFC7946=YES'\n ].concat(params.opts || []);\n const output = await Gdal.ogr2ogr(input.datasets[0], options);\n const bytes = await Gdal.getFileBytes(output);\n await Gdal.close(input);\n return JSON.parse(new TextDecoder().decode(bytes));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":137,"value":"## 1. GeoJSON and TopoJSON","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":772,"value":"Let's load a **GeoJSON** file :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":13,"value":"bhutan = FileAttachment(\"bhutan.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":197,"value":"In Observable, we just have to attach the GeoJSON file.\nIndeed, GeoJSON is nothing more than a valid JSON document. For this reason, it is the most used format on the Web when it comes to describing geospatial features. \n\nBased on this observation, the target of our conversions throughout this notebook will be GeoJSON.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":19,"value":"view(bhutan, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":776,"value":"Let's also load a **TopoJSON** file :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":29,"value":"aus = FileAttachment(\"austria-1.topojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":206,"value":"In Observable, we don't have to install anything. We can get a GeoJSON by using ```topojson.feature()```. As such, we still don't use gdal3.js for now.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":39,"value":"austria = topojson.feature(aus, \"level2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":34,"value":"view(austria, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":81,"value":"## 2. FlatGeoBuf","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":295,"value":"Let's open a file in **[FlatGeoBuf](https://flatgeobuf.org/) format**. The features it contains are in **EPSG:3035** coordinates reference system. We can thus take advantage of the fact that gdal3.js is linked to the PROJ library to obtain the layer in WGS84.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":724,"value":"fgbFile = FileAttachment(\"n2.fgb\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":787,"value":"There is nothing special to do and we can use the function written in section 0 which already includes the instruction to convert the dataset to WGS84 (EPSG:4326) :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":728,"value":"nuts2 = toGeoJSON(fgbFile);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":730,"value":"view(nuts2, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":832,"value":"## 3. GML","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":842,"value":"As shown just before, gdal3.js allows to easily convert from one CRS to another. Here it reads the `srsName` property of the objects in our GML file (our dataset uses EPSG:2154, the official CRS for Metropolitan France) to allow to convert it to WGS84.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":834,"value":"gmlFile = FileAttachment(\"departements_2016_2-2@1.gml\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":836,"value":"frenchDep = toGeoJSON(gmlFile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":839,"value":"view(frenchDep, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":222,"value":"## 4. CSV","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":226,"value":"Sometimes you will find data in CSV (coma separated value) format containing geographical locations in latitude and longitude or even geometries in WKT.\n\nLet's see how we can handle these two cases with gdal3.js.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":238,"value":"citiesFile = FileAttachment(\"cities3.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":667,"value":"cities = citiesFile.csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":240,"value":"Inputs.table(cities)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":651,"value":"### 4.1. with latitude/longitude columns","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":228,"value":"To do this, we need to inform the CSV driver about the possible names for the `x` and `y` columns. This can be done using the **open option** of GDAL.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":963,"value":"citiesGeo = toGeoJSON(\n citiesFile, \n { \n openOpts: ['AUTODETECT_TYPE=YES', 'X_POSSIBLE_NAMES=lng', 'Y_POSSIBLE_NAMES=lat'],\n opts: ['-s_srs', 'EPSG:4326'],\n }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":972,"value":"view(citiesGeo, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":653,"value":"### 4.2. with WKT geometry column","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":655,"value":"Sometimes, geospatial CSV dataset are containing their geometry in WKT, in a dedicated column. \nIt is also possible to inform the CSV driver about this using the `GEOM_POSSIBLE_NAMES` open option. \n\nLet's also use the 'sql' option of OGR to filter the data to be transformed and let's choose to keep only the 'city' column which contains the name of the city.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":996,"value":"citiesFile2 = FileAttachment(\"cities4.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":975,"value":"citiesGeo2 = toGeoJSON(\n citiesFile2, \n { \n openOpts: ['AUTODETECT_TYPE=YES', 'GEOM_POSSIBLE_NAMES=coords'],\n opts: ['-s_srs', 'EPSG:4326', '-sql', 'SELECT city from cities4'],\n }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":986,"value":"view(citiesGeo2, { width: 700, height: 350 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":44,"value":"## 5. Shapefile","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":148,"value":"The shapefile format is a geospatial vector data format developed and regulated by Esri. It is actually as a set of associated files (.shp, .shx, .dbf, .prj, ...). Several tools accept to import directly a Shapefile from a .zip archive (it is the case of QGIS, Magrit or the shp.js library for example).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":48,"value":"archive = FileAttachment(\"cuba.zip\").arrayBuffer()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":213,"value":"With gdal3.js, it looks like we have to open the zip file in JS and feed `Gdal.open` with a list of `File`s instead of feeding it directly with the zip archive. \n\nLet's use JSZip to open our archive :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":602,"value":"jz = (new JSZip.default()).loadAsync(archive);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":642,"value":"Then we read all the files to `Blob` and build an array of `File`s :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":604,"value":"files = (await Promise.all(Object.keys(jz.files).map(async (name) => [name, await jz.file(name).async('blob')])))\n .map(([name, blob]) => new File([blob], name))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":644,"value":"Open with gdal and convert with ogr2ogr :","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":444,"value":"cuba = toGeoJSON(files);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":52,"value":"view(cuba, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":54,"value":"## 6. Geopackage","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":56,"value":"GeoPackage is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information. A GeoPackage (*.gpkg) is constructed as a SQLite database file that contains data and metadata tables containing geometries. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":266,"value":"Here with gdal3.js we can easily extract the table from the GPKG file.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":581,"value":"gpkg = FileAttachment(\"afghanistan.gpkg\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":583,"value":"afg = toGeoJSON(gpkg);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":69,"value":"view(afg, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":73,"value":"## 7. Kmz/kml","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":159,"value":"A KMZ file is actually a zipped KML. We will unzip the KML file and feed it to gdal3.js.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":96,"value":"bol = FileAttachment(\"bolivia.kmz\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":100,"value":"kmlFile = new File([await bol.file(\"gadm41_BOL_2.kml\").blob()], bol.filenames[0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":105,"value":"bolivia = toGeoJSON(kmlFile);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":112,"value":"view(bolivia, { width: 700, height: 350 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":890,"value":"## 8. Conclusion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":892,"value":"In the end most formats can be opened with little or no differences, allowing to write a simple routine (like our `toGeoJSON` function) to accept various formats in order to convert them to GeoJSON for webmapping.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":921,"value":"The main disadvantages of using gdal3.js is its weight: here a `.wasm` file of 25Mb and a `.data` file of 10Mb.\nOne must then evaluate if all the power and robustness offered by GDAL is necessary in the developed application or if less full-featured libraries (but also with a much more reasonable weight) can be sufficient.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":904,"value":"- Thanks to [@tombor](https://observablehq.com/@tombor) and [@mootari](https://observablehq.com/@mootari) (see [@tombor/hello-gdal3](https://observablehq.com/@tombor/hello-gdal3) and [#6732](https://talk.observablehq.com/t/unable-to-import-gdal3-js-a-gdal-port/6732/4)) for how to load gdal3.js library in Observable.\n- Thanks to [@neocartocnrs](https://observablehq.com/@neocartocnrs) for the original notebook and for the [geoverview](https://github.com/neocarto/geoverview) library.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":15,"value":"## (Appendix)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":814,"value":"### Geoviewer","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":17,"value":"view = require(\"geoverview@1.2\").then((f) => f.view)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":565,"value":"### Stuffs for GDAL3.js","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":803,"value":"dev = false;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":809,"value":"useWorker = false;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":414,"value":"pathSuffix = dev ? '.dev' : ''","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"path = 'https://cdn.jsdelivr.net/npm/gdal3.js@2.4.0/dist/package';","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":417,"value":"initGdalJs = require.alias({fs: {}, path: {}, string_decoder: {}})(`${path}/gdal3${pathSuffix}.js`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":418,"value":"// See https://talk.observablehq.com/t/unable-to-import-gdal3-js-a-gdal-port/6732/4 for an explanation\nGdal = new Proxy(await initGdalJs({paths, useWorker}), {\n get(target, prop, receiver) {\n if(prop === 'next' || prop === 'then' || prop === 'return') return;\n return Reflect.get(target, prop, receiver);\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":416,"value":"paths = ({\n js: await makeLocal(`${path}/gdal3${pathSuffix}.js`),\n wasm: `${path}/gdal3WebAssembly.wasm`,\n data: `${path}/gdal3WebAssembly.data`,\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":421,"value":"async function makeLocal(url, {type = 'text/javascript', patch = s => s} = {}) {\n const src = await (await fetch(url)).text();\n return URL.createObjectURL(new Blob([patch(src)], {type: 'text/javascript'}));\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":567,"value":"### JSZip to unzip archives","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":451,"value":"JSZip = import('https://cdn.skypack.dev/jszip');","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W32","stdlib":"1","titling":"auto"}} 30:"$2f:body"