Major cities of the world

core

Files Size Format Created Updated License Source
2 6MB csv zip 1 month ago Geonames
List of major cities in the world Data The data is extracted from geonames, a very exhaustive list of worldwide toponyms. This datapackage only list cities above 15,000 inhabitants. Each city is associated with its country and subcountry to reduce the number of ambiguities. Subcountry can be the read more
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Data Files

File Description Size Last changed Download Other formats
world-cities [csv] 875kB world-cities [csv] world-cities [json] (2MB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 816kB datapackage_zip [zip]

world-cities  

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

Field information

Field Name Order Type (Format) Description
name 1 string English name of the city
country 2 string Common name of the country, in english
subcountry 3 string Name of the major administrative area
geonameid 4 integer id from geonames

datapackage_zip  

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

Read me

List of major cities in the world

Data

The data is extracted from geonames, a very exhaustive list of worldwide toponyms.

This datapackage only list cities above 15,000 inhabitants. Each city is associated with its country and subcountry to reduce the number of ambiguities. Subcountry can be the name of a state (eg in United Kingdom or the United States of America) or the major administrative section (eg ‘‘region’’ in France’’). See admin1 field on geonames website for further info about subcountry.

Notice that :

  • some cities like Vatican city or Singapore are a whole state so they don’t belong to any subcountry. Therefore subcountry is N/A.
  • There is no guaranty that a city has a unique name in a country and subcountry (At the time of writing, there are about 60 ambiguities). But for each city, the source data primary key geonameid is provided.

Preparation

You can run the script yourself to update the data and publish them to github : see scripts README

License

All data is licensed under the Creative Common Attribution License as is the original data from geonames. This means you have to credit geonames when using the data. And while no credit is formally required a link back or credit to Lexman and the Open Knowledge Foundation is much appreciated.

All source code is licensed 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 <- "http://datahub.io/core/world-cities/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/world-cities/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/world-cities/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/world-cities/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/world-cities/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