City Population Annual Timeseries (UN Statistics Division)

core

Files Size Format Created Updated License Source
2 18MB csv ODC-PDDL-1.0 UNData: UNSD Demographic Statistics
UNSD Demographic Statistics: City population by sex, city and city type. Data Source: UNData. UNSD Demographic Statistics. Contains two CSV datasets: unsd-citypopulation-year-both.csv. Size: 2.4 MB unsd-citypopulation-year-fm.csv. Size: 3.7 MB Final 222 lines in both datasets contain read more
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Data Files

unsd-citypopulation-year-both  

Field information

Field Name Order Type (Format) Description
Country or Area 1 string
Year 2 string
Area 3 string
Sex 4 string
City 5 string
City type 6 string
Record Type 7 string
Reliability 8 string
Source Year 9 string
Value 10 string
Value Footnotes 11 string

unsd-citypopulation-year-fm  

Field information

Field Name Order Type (Format) Description
Country or Area 1 string
Year 2 string
Area 3 string
Sex 4 string
City 5 string
City type 6 string
Record Type 7 string
Reliability 8 string
Source Year 9 string
Value 10 string
Value Footnotes 11 string

Read me

UNSD Demographic Statistics: City population by sex, city and city type.

Data

Source: UNData. UNSD Demographic Statistics.

Contains two CSV datasets:

  1. unsd-citypopulation-year-both.csv. Size: 2.4 MB
  2. unsd-citypopulation-year-fm.csv. Size: 3.7 MB

Final 222 lines in both datasets contain original notes.

Updates

Last update in UNdata: 22 Dec 2014

Next update in UNdata: Jun 2015 (est.)

About the United Nations Statistics Division

The United Nations Statistics Division collects, compiles and disseminates official demographic and social statistics on a wide range of topics. Data have been collected since 1948 through a set of questionnaires dispatched annually to over 230 national statistical offices and have been published in the Demographic Yearbook collection. The Demographic Yearbook disseminates statistics on population size and composition, births, deaths, marriage and divorce, as well as respective rates, on an annual basis. The Demographic Yearbook census datasets cover a wide range of additional topics including economic activity, educational attainment, household characteristics, housing characteristics, ethnicity, language, foreign-born and foreign population. The available Population and Housing Censuses' datasets reported to UNSD for the censuses conducted worldwide since 1995, are now available in UNdata.

Preparation

No special preparation needed.

License

This data package is licensed under a ODC Public Domain Dedication and Licence (PDDL).

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/population-city/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"unsd-citypopulation-year-both")
View(datapackage$data$"unsd-citypopulation-year-fm")

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/population-city/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/population-city/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/population-city/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/population-city/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')