Connecting the Unconnected.

High-Resolution Demographic Estimates and Base Station Data Reflect Worldwide Digital Inequality.

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Measuring Digital Inequality

High-resolution population estimates across 147 countries and base station distributions all over the world reveal worldwide digital divide.

We propose a fine-grained telecommunication service inequality index for any given geographical region, by using only the population and number of base station information.

Take the Republic of Tunisia as an example, the calculated region-wise inequality indexes are displayed in the below figure. From this figure, one can clearly and vividly observe the inequality index of each region across the whole country. Some regions such as those near the capital Tunis have very low inequality, which indicates good connectivity. Efforts of mobile network operators enrapt in improving the connectivity of these regions are unavailing. On the contrary, they should be attentive to regions with high inequality as few resource deployment are likely to bring huge social and economic benefits.

Inequality Index visualization for Tunisia (Grid-wise).

Inequality Index visualization for USA (Grid-wise).

Inequality Index visualization for France (Grid-wise).

Inequality Index visualization for Saudi Arabia (Grid-wise).


Inequality Index visualization for Uganda (Grid-wise).

We validate the effectiveness of our inequality index by comparing it with a coarse-grained measurement, i.e., the GSMA mobile connectivity index. The derived Pearson correlation coefficient is 0.92, indicating a high correlation exists between our index and the GSMA index. The achieved statistical p value is 2.2e-16, which is small enough to reject the null hypothesis (without correlation) and embrace the alternative hypothesis (with true correlation).

We can conclude that our index is consistent with coarse-grained measures, indicating the effectiveness of our proposed telecommunication service inequality. However, compared with the GSMA index that provides only country-wise statistics, our index is much more fine-grained and can provide inequality statistics for any given geographical segment.

Manuscript

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Abstract: The digital divide restricting the access of people living in developing areas to the benefits of modern information and communications technologies has become a major challenge and research focus. To well understand and finally bridge the digital divide, we first need to discover a proper measure to characterize and quantify the telecommunication service imbalance. In this regard, we propose a fine-grained and easy-to-compute imbalance index, aiming to quantitatively link the relation among telecommunication service imbalance, telecommunication infrastructure, and demographic distribution. The mathematically elegant and generic form of the imbalance index allows consistent analyses for heterogeneous scenarios and can be easily tailored to incorporate different telecommunication policies and application scenarios. Based on this index, we also propose an infrastructure resource deployment strategy by minimizing the average imbalance index of any geographical segment. Experimental results verify the effectiveness of the proposed imbalance index by showing a high degree of correlation to existing congeneric but coarse-grained measures and the superiority of the infrastructure resource deployment strategy.

Code and Data

We used open available data from Facebook's High Resolution Population Density Maps and Demographic Estimates and the OpenCellid Project.

Data and code will be available at Github.

Countries by Digital Inequality

We rank countries by digital inequality where lower values correspond to a good balance between telecommunication infrastructure and population.

Rank Country GSMA Inclusive Our
1 United States 84.99388 85.4 0.00098
2 Austria 84.21427 81.4 0.04193
3 Netherlands 85.02611 79.9 0.04684
4 United Kingdom 85.86669 83.4 0.05947
5 Hong Kong 83.43147 81.6 0.08233
6 Germany 83.06297 80.7 0.08392
7 Poland 76.09167 82.3 0.08668
8 Korea 80.76833 84 0.09799
9 Belgium 82.56152 80 0.10479
10 Singapore 89.26637 81.5 0.10702
11 Qatar 75.71883 78.5 0.11521
12 Australia 90.50563 84.2 0.12818
13 Kuwait 70.35302 79.1 0.14952
14 France 80.70973 83.3 0.15918
15 Italy 76.14640 80 0.18269
16 Greece 70.55883 74.4 0.19205
17 Spain 80.26051 83.1 0.20543
18 Hungary 76.56934 77.3 0.20950
19 Croatia 75.29430 75.1 0.21576
20 Bahrain 71.22565 73.8 0.21643
21 Slovakia 74.75194 78.5 0.24357
22 Thailand 70.89730 74.8 0.25478
23 Malaysia 69.16652 75.4 0.30264
24 Bulgaria 70.37289 77.4 0.30822
25 Japan 83.39861 80.9 0.33725
26 Chile 73.23459 81.7 0.34264
27 Saudi Arabia 72.57571 73.3 0.34543
28 Portugal 76.54616 79.1 0.35998
29 Romania 72.44761 80.3 0.43519
30 Jamaica 59.20655 60.5 0.45040
31 Oman 68.73608 72 0.47438
32 Turkey 67.12601 71.7 0.49162
33 Argentina 67.16469 73.8 0.53419
34 Botswana 51.31293 52.5 0.53778
35 Brazil 63.51903 75.9 0.55695
36 Iran 59.66196 67.7 0.57304
37 Laos 45.68697 52.3 0.58244
38 South Africa 60.14176 76.2 0.58244
39 Lebanon 59.52624 64.4 0.58516
40 Egypt 55.70530 62 0.60044
41 Paraguay 64.01819 66.1 0.60555
42 Guatemala 55.61676 57.7 0.61470
43 El Salvador 55.35760 63.5 0.62655
44 Mexico 67.55758 70.3 0.63043
45 Colombia 63.74438 72 0.65499
46 Morocco 59.88687 65.5 0.68759
47 Algeria 53.23075 56.7 0.69467
48 Venezuela 57.43407 56.3 0.71103
49 Namibia 40.22530 49.5 0.71477
50 Nicaragua 52.51740 52 0.71757
51 Sri Lanka 56.54102 66.7 0.73463
52 Zambia 35.34367 45.4 0.73994
53 Cambodia 49.13647 57 0.74324
54 Gabon 48.70591 51.9 0.74956
55 Honduras 50.20736 51.2 0.75678
56 Peru 66.61678 67.9 0.76530
57 Indonesia 62.90039 66.4 0.76759
58 Viet Nam 64.60360 71 0.77669
59 Azerbaijan 58.28458 68.7 0.78952
60 Ghana 52.01176 57.2 0.79958
61 Zimbabwe 36.56517 36.8 0.80633
62 Senegal 41.30933 51.5 0.82746
63 Philippines 62.79242 63.1 0.85443
64 Nigeria 49.12154 61.2 0.85515
65 Cameroon 44.14501 49.3 0.86611
66 Bangladesh 46.47414 58.4 0.86757
67 Myanmar 52.58755 60.8 0.90185
68 Tanzania 40.10575 49.4 0.91605
69 Uganda 40.90499 49.6 0.92033
70 Mozambique 34.94512 41.1 0.92731
71 Burkina Faso 32.44149 34 0.93099
72 Liberia 34.42750 26.9 0.93866
73 Papua New Guinea 47.80867 42.7 0.95006
74 Sudan 35.12561 43.7 0.95316
75 Madagascar 31.32539 30.3 0.95924
76 Rwanda 42.78829 46.8 0.96173
77 Ethiopia 35.94241 41.8 0.96458
78 Burundi 26.16048 26.7 0.99511

Team

Check our Team

Chuanting Zhang

Postdoc Fellow

Shuping Dang

Postdoc Fellow

Basem Shihada

Associate Professor