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GabonNamibiaNigerSenegalRep CongoCôte d'IvoireNigeriaMaliComorosBeninCameroonEquatorial…GuineaLiberiaZimbabweUgandaSierra LeoneAngolaDRCMozambiqueEthiopiaFigure 8-3. Percentage of households with mobile phones,2011-2014100%80%60%40%20%92% 89% 89% 88%82% 81%75% 74% 73% 72% 67% 67%65% 64%62% 59%55% 52%39%34%25%estimate larger phenomena using people’s reaction as aproxy. For instance, increased social media conversationsabout work-related anxiety and confusion provided a threemonthearly warning indicator of an unemploymentspike. 828Figure 8-4. Food expenditures and “top-up” expenditures(>0.7 correlation)0%Source: DHS surveys. 827Box 8-5. Using mobile phone data and airtime creditpurchases to estimate food security and povertyAs mobile phone handsets have become more ubiquitousacross Africa, the data generated by the use of mobilespresents a unique new opportunity for policy makers tounderstand vulnerable populations. UN Global Pulse andthe UN World Food Programme, together with UniversitéCatholique de Louvain in Belgium and Real ImpactAnalytics, conducted a study to assess the potential use ofmobile phone data as a proxy for food security and povertyindicators. Data extracted from airtime credit purchases (or“top-ups”) and mobile phone activity in an East Africancountry was compared to a nationwide household survey.Results showed high correlations between top-upexpenditures and consumption of several food items, suchas vitamin-rich vegetables or meat. These findingsdemonstrated that spending on top-ups could serve as aproxy indicator for food spending in market-dependenthouseholds. In addition, models based on both anonymisedmobile phone calling patterns and top-ups were shown toaccurately estimate multidimensional poverty indicators.This preliminary research suggested that proxies derivedfrom mobile phone data could provide real-time, granularinformation on the level of several food security andpoverty indicators. This framework could be integrated intoearly warning and monitoring systems, filling data gapsbetween survey intervals, and in situations where timelydata is not possible or accessible.Another known source of big data is social media – a set ofinternet-based applications and websites that allow usersto communicate directly with friends and strangers alike.Social media have been increasingly used worldwide tomonitor human behaviour and opinions as well as to157Source: UN Global Pulse.Social media analysis “for development” is not common inAfrica, but there are already a few examples: attitudeanalysis using Facebook data towards contraception inUganda 829 or accessibility of finance for small businessesusing twitter data in Kenya. 830 One of the challenges ofusing social media, data online searches and onlinetransactions for monitoring sustainable development inAfrica is the low internet penetration rate: only 38 internetusers per 100 people in Northern Africa and, even fewer,15 internet users per 100 people in sub-Saharan Africa as of2012. 831 No country in Africa has internet penetration ratesabove 50 internet users per 100 inhabitants. However,social media data, often of limited use due to groupselection biases, can still be of value when targeted atspecific sub-populations which more commonly use socialmedia, like the youth or businessmen.8.2.2. Satellite dataSatellite imagery has been around for a few decades but, inthe early 21st century, became widely available whenaffordable, easy to use satellite imagery databases wereoffered by several companies and organizations. 832 Becausethe images are routinely taken by satellites in orbit, satelliteimages are a vast, comprehensive and continuing source ofinformation.This prompted an explosion of innovative applications usingsatellite imagery, some covering African countries, fromestimating GDP 833,834 to crop productivity. 835 Satellite