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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 (

UPDATED---June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies.

The smartphone exemplifies the rhetorical smart city movement. This paper examines one potential use of smartphone technology—mapping public transportation services in a megacity of the Global South. We examine the potential user impacts of introducing a smartphone-generated and analogue-delivered schematic bus map in Dhaka, Bangladesh. After distributing the map, we used a web-based survey to investigate impacts on users’ knowledge, as measured by their stated understanding, navigation and perceptions of the system. While a small and biased sample, the results suggest that the map fills a knowledge gap, provides useful and valued information and may stimulate ‘exploration’ of the city and its bus system., Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program)