European NUTS boundaries as GeoJSON at 1:60m

core

Files Size Format Created Updated License Source
4 2MB zip geojson 1 month 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 Other formats
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 282kB datapackage_zip [zip]
nuts_rg_60m_2013_lvl_1 [geojson] 198kB nuts_rg_60m_2013_lvl_1 [geojson]
nuts_rg_60m_2013_lvl_2 [geojson] 319kB nuts_rg_60m_2013_lvl_2 [geojson]
nuts_rg_60m_2013_lvl_3 [geojson] 792kB nuts_rg_60m_2013_lvl_3 [geojson]

datapackage_zip  

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

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.

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.

Import into your tool

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

install.packages("jsonlite")
library("jsonlite")

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

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))
print(data)

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/core/geo-nuts-administrative-boundaries/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

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 = 'http://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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

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

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json