A First Look At Mobile Internet Use In Township .

A First Look at Mobile Internet Use in TownshipCommunities in South AfricaABSTRACT1. INTRODUCTIONThis paper presents a study of mobile data usage in South Africantownships. In contrast to previous studies, which have studiedmobile data usage in developing regions (including South Africa),we focus our study on two townships in South Africa; theextremely resource-constrained nature of townships sheds light,for the first time, on how people in these communities use mobiledata. We perform a mixed-methods study, combining quantitativenetwork measurements of mobile app usage with qualitativesurvey data to gain insights about mobile data usage patterns andthe underlying reasons for user behavior concerning mobile datausage. Due to the limited availability of public free WiFi anddespite the relatively high cost of mobile data, we find that atypical township user's median mobile data usage is significantlymore than WiFi usage. As expected, and consistent withobservations of mobile data usage in parts of South Africa withbetter resources, users tend to favor using WiFi for steamingvideo applications, such as YouTube. Interestingly, however,unlike users in less resource-constrained settings, township usersalso consume significant mobile data to update mobileapplications, as opposed to relying on WiFi networks forapplication updates. These behaviors suggest that network andmobile application designers must pay more attention to datausage patterns on cellular networks to provide mobile networkarchitectures that provide more cost-effective mechanisms fortasks such as application update.Despite the tremendous growth in Internet-capable mobile deviceadoption [1], Internet usage and access to data is limited in SouthAfrica by prohibitive costs and unequal coverage [2]. Yet, thehigh cost of communication has not deterred the growth of mobiledata usage in the less-privileged areas such as in the SouthAfrican townships. In fact, mobile data usage growth in townshipareas has outpaced the average usage growth across the whole ofSouth Africa [3].CCS Concepts Network measurements Network types Human ComputerInteraction (HCI)KeywordsSmartphone usage; mobile application popularity; usage patternsYet, mobile data is expensive relative to the incomes of townshipresidents. The World Bank estimates that half of South African’surban population lives in townships and informal settlements,accounting for 38% of working age citizens, and home of nearly60% of unemployed [4]. In Khayelitsha, one of the poorest areasof Cape Town, the median average monthly income of a family offive is approximately ZAR 1600 (USD 110) [5]. Our studysuggested an average monthly expenditure of ZAR 100 to 200 peruser. At such a high cost per user, many users may find mobiledata unaffordable; understanding the nature of the usage patternsin lower-income townships is important, both to understand theeconomic consequences of mobile Internet penetration, as well asto suggest opportunities for network and application architecturesto better optimize data use in these settings.We analyze the data usage patterns of mobile Internet users livingin township1 communities in South Africa. To this end, seven highschool students and seven knowledge workers from two differenttownship communities (Ocean View and Masiphumelele) in CapeTown were recruited to participate in our research study. Weperformed the study with a mixed-method approach comprisingtwo parts: (1) quantitative measurements of the usage of differentmobile applications using the MySpeedTest application [6]; and(2) a survey examining users’ behavior concerning mobileInternet usage. With these two methods, we aimed to crossvalidate behaviors of mobile usage collected from themeasurement application with the responses received from thesurvey.In 2015, Mathur et al. found that, in contrast to more developedregions, when data is expensive or limited, users have thetendency to be extremely cost-conscious and would employvarious strategies to optimize mobile data usage [7]. This situationobviously does not encourage the extensive use of Internettechnologies, which could enable resource-constrainedcommunities to share information, communicate, generate content1“Township” refers to urban informal settlements in South Africa,where people were historically displaced during Apartheidperiod based on their ethnicity. They are the poorest urbancommunities in South Africa.

and make use of online educational material for their own benefit.It leaves open the complementary but important question of howusers in more resource-constrained communities such astownships use mobile applications and consume mobile data. Aprevious study on broadband measurements in South Africa alsorevealed interesting data on performance bottlenecks [8]. Yet incontrast, very little is known about Internet connectivity intownship communities. By characterizing mobile Internet usage,we attempt to build a solid understanding of the need of cellularnetworks users from township communities in South Africa.We also studied the extent to which mobile data traffic isexchanged with users who reside in the same geographic region.Because we do not have access to mobile operators’ traffic traces,it is difficult to accurately measure this characteristic. Instead, westudied this question using a survey, which revealed that most ofthe interactions on social networks are targeted to “friends” wholive roughly the in the same locality. This means that users areactually using their expensive and limited data packages to sendand receive data to peers living relatively nearby.The quantitative measurements allow us to investigate how muchtraffic is being generated for social media, communications,software updates, video streaming, and other applications, as wellas how usage is influenced by economic factors such aspromotional data packages and zero-rated services. By gatheringand analyzing empirical data on how mobile Internet is consumedin township areas, our results can ultimately guide researchers onthe needs of mobile phone users, especially in the resourceconstrained regions. The outcome of this research can provideimportant input for the design and deployment of alternativenetwork architectures [9] that could reduce the cost ofinterconnectivity.Our study reveals the following findings, several of whichcontrast with previous studies in South Africa in higher-incomecommunities in unintuitive ways: In contrast to communities with higher incomes, mediandaily data usage across users is more on cellular datanetworks than on than Wi-Fi. Qualitative survey resultssuggest that the relative inaccessibility of public WiFimay induce this behavior.In contrast to communities in South Africa with moreresources and higher incomes, township users consumesignificant mobile data on cellular networks to updatemobile applications.As in other communities, streaming video usage islower on cellular data networks than on Wi-Fi.2.1 Qualitative studiesThere is a small set of literature found on mobile Internet usage intownship communities in South Africa. In 2009, Kreutzer made astudy of 66 secondary school grade-11 students in a low-incomearea in Cape Town [10]. The study revealed that more than 97%of respondents actually owned a mobile phone or used one on aregular basis. The study also suggests that mobile Internet wasquite popular with 83% of the respondents accessed the web on atypical day.In 2011, Donner, Gitau and Marsden studied mobile Internet-onlyusage in an urban setting in South Africa [11]. They used anethnographic action research approach to study the challenges andpractices of mobile data usage in a resource-constrained setting.Research subjects were observed after being given training andthey found out that most of them were still using the Internet ontheir mobile phones, especially for entertainment andcommunication - months after receiving the training. One of themajor barriers found was the affordability of data packages.No qualitative studies of Internet usage have been conducted since2011. The drop in the price of smartphones and faster mobilebroadband connectivity (3G/LTE) in those areas completelydisrupted the rate at which mobile data is being consumed. Figure1 shows how data usage drastically evolved for Vodacom withconsumption increasing by almost 500% between 2011 and 2015[12]. This gap therefore further motivates our study on mobiledata usage in township communities in South Africa.2.2 Quantitative measurement studiesSeveral other recent studies have performed quantitativeexaminations of mobile Internet usage. In 2013, Chetty et al. usedpassive and active measurement methods to collect performanceand usage data from both home routers and mobile phones [8].One of the objectives was to compare broadband performance ondifferent connection types and see whether users were getting theperformance advertised by their Internet Service Providers (ISPs).A mixed of measurement tools were used: BISmark [13] on homerouters, MyBroadband [14] and MySpeedTest [6]. They foundthat (1) users were not getting the advertised speed from theirrespective ISPs (2) mobile broadband users have a higherthroughput than fixed-line users; and (3) high latency to popularwebsites and services affected performance and quality of service.The rest of the paper is organized as follows. We discuss relatedwork pertaining to Internet measurements for mobile data usage inSection 3. In Section 4, we will talk about the research contextand give a brief description of the township communities inquestion. In Section 5 we will discuss our approach, the metricsused and we will give a description of the MySpeedTestapplication. Finally in Section 6, we present the results of thestudy including both the measurement exercise and the survey andthe semi-structured interviews, before ending with somediscussions and closing remarks.2. BACKGROUND AND RELATED WORKFigure 1: Smartphone data usage[12]In this section, we review related work on mobile Internet usageand mobile data measurements, which provides background andcontext for our study.More recently in 2015, Mathur et al. used a multi-factor approachtriangulating data from three distinct sources: semi-structuredinterviews, surveys and the MySpeedTest application to studycharacteristics of mobile broadband usage of high-income versuslow-income participants across South Africa. Although the study