Individuals using the Internet (% of population) from 1990 to 2017¶

The digital and information revolution has dramatically changed the way the world communicates, learns, does business and treats disease. Indeed, the new information and communications technologies (ICTs) offer vast possibilities for advancement in all fields in all countries, from the most to the least developed.

Comparable statistics on access, use, quality and affordability of ICT are essential for formulating policies favorable to the growth of the sector and for monitoring and evaluating the impact of this sector on the development of each country. Although basic access data are available for many countries, in most developing countries little is known about ICT users, including their usage, and how they affect people and businesses. The Global Partnership on Measuring ICT for Development is there to help set standards, harmonize information and communications technology statistics, and build the statistical capacity of developing countries. However, despite significant improvements in developing countries, the gap remains.

Hereafter, we will use Plotly library to spatially visualize the time evolution of the individuals using the Internet through the world.

Import required libraries¶

In [1]:

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.io as pio
from IPython.display import Javascript

Javascript(
"""require.config({
 paths: { 
     plotly: 'https://cdn.plot.ly/plotly-latest.min'
 }
});"""
)

pio.renderers.default = 'notebook_connected'

Data pre-processing¶

In [2]:

df = pd.read_csv('Data/Individuals_using_the_Internet.csv', 
                 header=0,
                 names=['year', 'time_code', 'country_name', 'country_code', 'percentage_internet_users'],
                 usecols=['year', 'country_name', 'country_code', 'percentage_internet_users'],
                 parse_dates=True,
                 dtype={'percentage_internet_users': float}, 
                 na_values='..')

df.head()

<ipython-input-2-8c7ef5338f76>:6: DeprecationWarning:

`np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

Out[2]:

	year	country_name	country_code	percentage_internet_users
0	1960	Argentina	ARG	NaN
1	1960	Australia	AUS	NaN
2	1960	Brazil	BRA	NaN
3	1960	China	CHN	NaN
4	1960	France	FRA	NaN

Cleaning¶

Our dataset extends from 1960 to 2018. The Internet has started to be developped in the 1960's, but has really started to be popularized in the 1990's, there has therefor been few or no users between the 1960's and the 1990's.

To prepare our mapping, we begin by dropping all "not a number (NaN)" values. Then, because there is no real interest to map years with all data to 0 - typically years from 1960 to 1990 - we look for and exclude each year where the sum of the percentage of internet users for the whole countries is 0. This process brings us to 1990 as first year with significant values. We also choose to exclude 2018 from the dataset because it still lacks some not negligible values for this year.

In [3]:

df.dropna(inplace=True)

group = df.groupby('year')
df = group.filter(lambda x: x['percentage_internet_users'].sum() > 0)
df = df.drop(df[df['year']=='2018'].index)
df.reset_index(drop=True, inplace=True)
df

Out[3]:

	year	country_name	country_code	percentage_internet_users
0	1990	Argentina	ARG	0.000000
1	1990	Australia	AUS	0.585095
2	1990	Brazil	BRA	0.000000
3	1990	China	CHN	0.000000
4	1990	France	FRA	0.052778
...	...	...	...	...
4935	2017	Virgin Islands (U.S.)	VIR	64.377494
4936	2017	West Bank and Gaza	PSE	65.200000
4937	2017	Yemen, Rep.	YEM	26.718355
4938	2017	Zambia	ZMB	27.852579
4939	2017	Zimbabwe	ZWE	27.055488

4940 rows × 4 columns

Mapping¶

In [4]:

fig = px.choropleth(df, 
                    locations='country_code',
                    color='percentage_internet_users',
                    hover_name='country_name',
                    animation_frame='year', 
                    range_color=[0,100],
                    scope='world',
                    labels={'percentage_internet_users':'% of population<br>using Internet'},
                    title="<b>Individuals using the Internet from 1990 to 2017</b><br>" + 
                    "<i>Source : International Telecommunication Union</i>",
                    color_continuous_scale=px.colors.sequential.deep)

# Style
fig.update_layout(
    font_family='Helvetica',
    font_color='grey',
    font_size=12,
    title_font_size=20
)

fig.show()

In [5]:

fig = px.choropleth(df, 
                    locations='country_code',
                    color='percentage_internet_users',
                    hover_name='country_name',
                    scope='world',
                    labels={'percentage_internet_users':'% of population<br>using Internet'},
                    color_continuous_scale=px.colors.sequential.deep,
                    title="<b>Individuals using the Internet in 2017</b><br>" + 
                    "<i>Source : International Telecommunication Union</i>"
                   )

# Style
fig.update_layout(
    font_family='Helvetica',
    font_color='grey',
    font_size=12,
    title_font_size=20,
)

fig.show()

Limitations of the dataset¶

Operators have traditionally been the main source of telecommunications data, since subscription information is readily available for most countries. This provides a general idea of access, but a more precise measure is the penetration rate, that is, the share of households with access to telecommunications.

Data on actual use of telecommunications services is also important. In recent years, household and business surveys have made more information available on the use of ICTs. Ideally, statistics on telecommunications and other ICTs should be compiled for the three measures: subscriptions, access and use.

Finally, the quality of the data collected varies according to the reporting country due to differences in regulations concerning the supply and availability of data.

Sources¶

International Telecommunication Union, World Telecommunication/ICT Development Report and database under CC BY-4.0 License.

For additional or latest information on sources and country notes, please also refer to International Telecommunication Union website.

Previous Post Next Post