European NUTS boundaries as GeoJSON at 1:60m

core

Files Size Format Created Updated License Source
3 1MB geojson 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

nuts_rg_60m_2013_lvl_1  

nuts_rg_60m_2013_lvl_2  

nuts_rg_60m_2013_lvl_3  

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

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/core/geo-nuts-administrative-boundaries/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"nuts_rg_60m_2013_lvl_1")
View(datapackage$data$"nuts_rg_60m_2013_lvl_2")
View(datapackage$data$"nuts_rg_60m_2013_lvl_3")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

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

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

# to see datasets in this package
storage.buckets

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

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

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

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/core/geo-nuts-administrative-boundaries/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this Data Package in JavaScript, please, follow instructions below:

Install datapackage using npm:

$ npm install [email protected]

Once the package is installed, use code snippet below


const Datapackage = require('datapackage').Datapackage

async function fetchDataPackageAndData(dataPackageIdentifier) {
  const dp = await new Datapackage(dataPackageIdentifier)
  await Promise.all(dp.resources.map(async (resource) => {
    if (resource.descriptor.format === 'geojson') {
      const baseUrl = resource._basePath.replace('/datapackage.json', '')
      const resourceUrl = `${baseUrl}/${resource._descriptor.path}`
      const response = await fetch(resourceUrl)
      resource.descriptor._values = await response.json()
    } else {
      // we assume resource is tabular for now ...
      const table = await resource.table
      // rows are simple arrays -- we can convert to objects elsewhere as needed
      const rowsAsObjects = false
      resource.descriptor._values = await table.read(rowsAsObjects)
    }
  }))

  // see the data package object
  console.dir(dp)

  // data itself is stored in Resource object, e.g. to access first resource:
  console.log(dp.resources[0]._values)

  return dp
}


fetchDataPackageAndData('https://pkgstore.datahub.io/core/geo-nuts-administrative-boundaries/latest/datapackage.json');

Our JavaScript is written using ES6 features. We are using node.js v7.4.0 and passing --harmony option to enable ES6:

$ node --harmony index.js

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/core/geo-nuts-administrative-boundaries/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

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

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')
Datapackage.json