Major cities of the world

core

Files Size Format Created Updated License Source
2 10MB csv zip 2 months ago [object Object] 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] 852kB world-cities [csv] world-cities [json] (852kB)
world-cities_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 816kB world-cities_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

world-cities_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

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/world-cities/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"world-cities")
View(datapackage$data$"world-cities_zip")

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/world-cities/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/world-cities/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 dataset in JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use code snippet below:

  const {Dataset} = require('data.js')

  const path = 'https://pkgstore.datahub.io/core/world-cities/latest/datapackage.json'

  const dataset = Dataset.load(path)

  // get a data file in this dataset
  const file = dataset.resources[0]
  const data = file.stream()

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/world-cities/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