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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Telecommunications P...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Telecommunications Policy
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Crowdsourcing mobile coverage

Authors: Pantelis Koutroumpis; Aija Leiponen;

Crowdsourcing mobile coverage

Abstract

Mobile coverage affects social and economic communication and performance. Looking at the determinants of mobile network coverage helps highlight both operators' strategies and individual users' decisions. Subscribers often install small wireless antennas called femto-cells within buildings with poor signal. The location and density of these antennas provides information for areas with significant demand but poor coverage-areas where coverage is effectively crowdsourced. In contrast to the development of fixed-line networks, average income, population, geography, and education are the main drivers of both telecom and femto-cell based mobile coverage. Low-income regions are found to receive almost 15% less coverage compared to their affluent counterparts and have two fewer telecom base stations installed for equal distributions of (potential) subscribers. Base stations tend to be at least three times further from lower-income subscribers. Within poorly-covered areas, wealthier households are able to compensate by investing in femto-cells, but poorer households are often excluded from communication networks and the internet. The results of this research has implications for information policy. Mobile coverage affects socio-economic communication and performance.Using crowdsourced information on mobile coverage and femto-cells the existence of mobile divide in the US is measured.Low-income regions are found to receive 15% less coverage compared to their affluent counterparts.Operators install two fewer mobile antennas per tract in lower income areas for equal distributions of subscribers.These findings have implications for universal service delivery policies.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Top 10%
Average
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