Measuring the Global Broadband Divide Using Aggregated Crowdsourced Big Data
Alfonso Rivera-Illingworth, Richard Heeks & Jaco Renken
Abstract
Growing broadband connectivity is central to development strategies of all countries. Thus measurement of that connectivity and associated broadband divides is an essential foundation for telecommunications and digital policy. Yet traditional broadband measures face challenges of data completeness, accuracy, relevance, timeliness and accessibility.
This paper therefore investigates the potential for measuring broadband connectivity and broadband divides between countries using aggregated crowdsourced big data, based on online survey of connectivity speeds from many millions of users. This data source exposes broadband divides between high-, middle- and low-income countries. While such divides are well known, we demonstrate that this form of big data provides a more complete and accurate picture, alongside other benefits compared to traditional data sources.
We show how this aggregated form of big data offers new insights: into divides between fixed and mobile networks, and download and upload speeds; and we show how it can be used to calculate new broadband indices and to measure readiness for broadband. Acknowledging the limitations of this type of big data to guide broadband divide measurement and related policy decisions, we argue it should be at least a complement to traditional measures given, unlike many big datasets, that it is free to access.
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