Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya

Ron Mahabir, Anthony Stefanidis, Arie Croitoru, Andrew Crooks, Peggy Agouris
2017 ISPRS International Journal of Geo-Information  
With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries.
more » ... n this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google's Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations. ambient geographical information (AGI, [5] ), have also been used to make the distinction between explicitly and implicitly crowd-contributed geographic content. Crowdsourcing activities such as these are the product of digital and civic engagement [6, 7] . As such, they reflect convoluted social and psychological processes, and the mechanisms that drive participation in various crowdsourcing projects have only recently begun to be studied (e.g., [7] [8] [9] [10] [11] ). When it comes to VGI in particular, advancing our understanding of user motivations to participate in such activities will improve our ability to take full advantage of the value of such crowd-contributed information. Research by Neis and Zielstra [12] suggests that people contribute for intrinsic (e.g., altruism, fun/recreation, learning/personal enrichment, unique ethos and self-expression/image) or extrinsic (e.g., social reward/relations, career, personal reputation, community/project goal and system trust) reasons. Each community is expected to be unique in the combinations of factors that drive the contribution of VGI because of differences, for example, in mapping interest, culture and socio-economic standing. Such differences make the process used to generate VGI different from one group to the next. For instance, while some developed countries may be motivated by personal reputation or VGI as a fun or recreational event (e.g., [13] ), most of the literature on VGI in supporting developing countries (e.g., [14] [15] [16] ) has suggested its use for mainly addressing the often lack of available and updated geographic datasets at these locations. When it comes to developing countries, VGI contributions tend to be made in spurts, for example in response to a country entering the global spotlight, as may be the case in the aftermath of a natural disaster (e.g., [17] [18] [19] ), rather than as a regular, continuous process. Most large-scale instances of VGI for developing countries, at least those often reported in the literature, occur during time of disasters (e.g., the 2010 Haiti earthquake) or for other humanitarian purposes (e.g., Map Kibera [20]). Within recent years, there has also been various map drives (i.e., mapping events/mapathons) by organizations, such as the Humanitarian OpenStreetMap Team and MapGive (an initiative of the U.S. Department of State's Humanitarian Information Unit) to increase the penetration of VGI mapping activities in developing countries. However, the sustainability and utility of these types of activities is still yet to be proven. This is in addition to the various factors reported in the literature that influence the overall low contribution of VGI in developing countries (which will be discussed in more detail in Section 2). For example, a recent national mapping event in the small tropical and Caribbean island of Saint Lucia [21] suggests that some governments in developing countries do recognize the value of VGI, in the sense that it can be used to monitor the public's perception [22] , along with providing new ways to interact with and capture information on cities [23, 24] and strengthening civil society [25] . Developing countries are currently facing challenges with rapid and unsustainable population growth [26] , rapid urbanization and massive infrastructure development, which often lead to issues, such as the proliferation and expansion of slums. To adequately address such issues, there is a critical need for up-to-date and reliable spatial information [27] . However, for many developing countries, the cost of creating and maintaining an updated national geographical database is very high. Some developing countries are unable to make the financial commitments necessary to support these types of large-scale projects [28] . This makes the availability and potential use of VGI an attractive alternative due to its low cost compared to more traditional spatial data collection methods. However, little is known about the quality of VGI data sources and their suitability for such application domains given the spatial data needs in the developing world. This paper presents one of the first studies to assess the quality of VGI data in developing countries. Our aim is to contribute to the assessment of the coverage of authoritative and non-authoritative sources of road data in a developing country in Africa, Kenya in this case, in order to advance our understanding of the value of VGI for the developing world. Towards this goal, we build upon earlier work (see Section 2) to highlight how such methods can be utilized in order to gain insights into the potential of VGI as a complementary data source for addressing the spatial data needs of such countries.
doi:10.3390/ijgi6010024 fatcat:dpymbj5z7zbpdlixq4me6twq4i