Country Polygons as GeoJSON

core

Files Size Format Created Updated License Source
2 7MB zip geojson 5 days ago Natural Earth
Geodata data package providing geojson polygons for all the world's countries. Perfect for use in apps and visualizations. Data The data comes from Natural Earth, a community effort to make visually pleasing, well-crafted maps with cartography or GIS software at small scale. The shape of the read more
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Data Files

File Description Size Last changed Download
geo-countries_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 7MB zip (7MB)
countries 23MB geojson (23MB)

geo-countries_zip  

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

countries  

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

Read me

Geodata data package providing geojson polygons for all the world’s countries. Perfect for use in apps and visualizations.

Data

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

The shape of the countries have two fields :

  • name : the common name for the country
  • ISO3166-1-Alpha-3 : three letters iso code of the country

More info about countries can be get from datapackage https://github.com/datasets/country-codes by a join on field ISO3166-1-Alpha-3

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

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

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

json_file <- 'https://datahub.io/core/geo-countries/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)
  }
}

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

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/geo-countries/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-countries/datapackage.json')

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

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        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-countries/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