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With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012–2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (

Volunteer crisis mappers are a recent phenomenon driven by technological advances, online communities, and volunteerism. With the rise of interactive websites, social media, and online mapping tools, individuals across the world are able to participate in collecting data in response to a crisis. Although these volunteers provide needed technical assistance to humanitarian efforts, their lack of affiliation with “formal” actors, such as the United Nations, and the very fact that they are volunteers, makes them a dubious data source. Concerns about the quality of amateur mapping and data efforts are raised in the neogeography literature, while questions related to the use of data and new technology have come out in several recent publications by humanitarian actors. Most of these concerns assume that volunteers have no professional training related to their work as volunteers. By investigating both the volunteers and their organizations, this article presents evidence of a more professional cadre of volunteers and a means for distinguishing types of volunteer organizations. As volunteer crisis mapping organizations play a larger and more integrated role in humanitarian responses, it is crucial that their differences are understood and these concerns about the volunteers are answered.