European NUTS boundaries as GeoJSON at 1:60m

core

Files Size Format Created Updated License Source
4 309kB geojson zip 7 months ago 3 months ago GISCO (the Geographical Information System at the COmmission)
Geo Boundaries for NUTS administrative levels 1, 2 and 3 edition 2013. If you don't know what NUTS (Nomenclature of Territorial Units for Statistics) are, see the related Wikipedia article Data Data is taken from the GISCO EU website. We choose to deliver data as Shapefiles (SHP) and as read more
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Data Files

File Description Size Last changed Download
nuts_rg_60m_2013_lvl_1 198kB geojson (198kB)
nuts_rg_60m_2013_lvl_2 319kB geojson (319kB)
nuts_rg_60m_2013_lvl_3 792kB geojson (792kB)
geo-nuts-administrative-boundaries_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 284kB zip (284kB)

nuts_rg_60m_2013_lvl_1  

This is a preview version. There might be more data in the original version.

nuts_rg_60m_2013_lvl_2  

This is a preview version. There might be more data in the original version.

nuts_rg_60m_2013_lvl_3  

This is a preview version. There might be more data in the original version.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get https://datahub.io/core/geo-nuts-administrative-boundaries
data info core/geo-nuts-administrative-boundaries
tree core/geo-nuts-administrative-boundaries
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/geo-nuts-administrative-boundaries/r/0.geojson

curl -L https://datahub.io/core/geo-nuts-administrative-boundaries/r/1.geojson

curl -L https://datahub.io/core/geo-nuts-administrative-boundaries/r/2.geojson

curl -L https://datahub.io/core/geo-nuts-administrative-boundaries/r/3.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

Geo Boundaries for NUTS administrative levels 1, 2 and 3 edition 2013.

If you don’t know what NUTS (Nomenclature of Territorial Units for Statistics) are, see the related Wikipedia article

Data

Data is taken from the GISCO EU website.

We choose to deliver data as Shapefiles (SHP) and as GeoJSON.

SHP are in data/shp directory.

GeoJSON are in data folder

Datasets are provided for NUTS levels 1, 2 and 3.

The columns are

  • NUTS_ID: String (5.0)
  • STAT_LEVL_: Integer (9.0)

You will also find the original data within data/NUTS_2013_60M_SH.

If you need other related informations to NUTS, you can take a look at PDF file describing relationships between original tables in data/NUTS_2013_60M_SH/NUTS_2013_60M_SH/metadata/NUTS_2013_metadata.pdf

Preparation

This package include the script to automate data retrieving and filtering. As we use NodeJs/Io.js, you need to install the software. Then, install dependencies with:

cd scripts && npm install

To launch all the process, just do (default scale: 60M):

node index.js

Or specify scale and use the following command, where {scale} can be 01M, 03M, 10M, 20M or the default 60M:

node index.js {scale}

We choose to let a lot of comments and you may encounter some minors job unrelated code for learning purpose if you need to use node-gdal library.

License

This Data Package is licensed by its maintainers under the Public Domain Dedication and License (PDDL).

Refer to the Copyright notice of the source dataset for any specific restrictions on using these data in a public or commercial product.

Datapackage.json