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, 10 (11), e0142076
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Quantifying and Mapping Global Data Poverty

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Quantifying and Mapping Global Data Poverty

Mathias Leidig et al. PLoS One.

Erratum in

Abstract

Digital information technologies, such as the Internet, mobile phones and social media, provide vast amounts of data for decision-making and resource management. However, access to these technologies, as well as their associated software and training materials, is not evenly distributed: since the 1990s there has been concern about a "Digital Divide" between the data-rich and the data-poor. We present an innovative metric for evaluating international variations in access to digital data: the Data Poverty Index (DPI). The DPI is based on Internet speeds, numbers of computer owners and Internet users, mobile phone ownership and network coverage, as well as provision of higher education. The datasets used to produce the DPI are provided annually for almost all the countries of the world and can be freely downloaded. The index that we present in this 'proof of concept' study is the first to quantify and visualise the problem of global data poverty, using the most recent datasets, for 2013. The effects of severe data poverty, particularly limited access to geoinformatic data, free software and online training materials, are discussed in the context of sustainable development and disaster risk reduction. The DPI highlights countries where support is needed for improving access to the Internet and for the provision of training in geoinfomatics. We conclude that the DPI is of value as a potential metric for monitoring the Sustainable Development Goals of the Sendai Framework for Disaster Risk Reduction.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Data sets input to calculate the Data Poverty Index.
Fig 2
Fig 2. Map showing global Data Poverty for 2013, by nation states.
The locations of the 50 most populous cities are also shown. The base map (world borders) was obtained from http://diva-gis.org/data.
Fig 3
Fig 3. The Data Poverty Index in relation to World Banks Income classification.
The ends of the whisker are set at 1.5*Interquartile Range (IQR) above the third quartile (Q3) and 1.5*IQR below the first quartile (Q1).
Fig 4
Fig 4. Spider plot indicating the average contribution of each factor to the DPI score of the corresponding World Bank income class.

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References

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Publication types

Grant support

This study was supported by the Leverhulme Trust.
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