Natural Earth Admin1 Polygons as GeoJSON

core

Files Size Format Created Updated License Source
2 9MB geojson zip 6 months ago 2 months ago Natural Earth
Geodata data package providing geojson polygons for the largest administrative subdivisions in every countries. Data Note : this dataset and its source are still in BETA. The data comes from Natural Earth, a community effort to make visually pleasing, well-crafted maps with cartography or GIS read more
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Data Files

File Description Size Last changed Download
admin1 29MB geojson (29MB)
geo-ne-admin1_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 9MB zip (9MB)

admin1  

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

Read me

Geodata data package providing geojson polygons for the largest administrative subdivisions in every countries.

Data

Note : this dataset and its source are still in BETA.

The data comes from Natural Earth, a community effort to make visually pleasing, well-crafted maps with cartography or GIS software at small scale.

Admin1 is the biggest administrative subdivision of countries. Note that it is very heterogeneous among countries : in the United States of America, admin1 represents states, whereas they don’t represent the inner countries in the United Kingdom. For more information, please see official documentation or https://en.wikipedia.org/wiki/Table_of_administrative_divisions_by_country.

The shape of the admin1 have four fields :

  • name : the common name for this admin1 subdivision
  • id : code for the subdivision inside the country. Documentation is not clear what this code is, but it could be FIPS. Note that some countries like Vatican are so small they don’t have inner administrative subdivision. In that case code could be null and in any way it is irrelevant.
  • country : name of the country
  • ISO3166-1-Alpha-3 : three letters iso code of the country

Preparation

To run the script in order to update the data : see scripts README

License

All data is licensed under the Open Data Commons Public Domain Dedication and License.

Note that the original data from Natural Earth is public domain. While no credit is formally required a link back or credit to Natural Earth, Lexman and the Open Knowledge Foundation is much appreciated.

All source code is licenced under the MIT licence.

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-ne-admin1
data info core/geo-ne-admin1
tree core/geo-ne-admin1
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/geo-ne-admin1/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/geo-ne-admin1/r/0.geojson

curl -L https://datahub.io/core/geo-ne-admin1/r/1.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-ne-admin1/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-ne-admin1/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-ne-admin1/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-ne-admin1/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)
    }
  }
})()
Datapackage.json