This tool could be especially powerful when combined with investments in building a digital administrative dataset that would provide information on the crashes attended by police. The answer to the seemingly simple question of where and when crashes occur has profound implications for public policy response that can save lives. And while official data deprivation can be an impediment to economic development, data generated by private operators can be transformed and placed in the hands of policy makers as a resource for policy making. By expanding the amount of data, we can generate more input to help resource-constrained countries prioritize policy action where it is most needed.

This example of geolocating crash data from mining twitter data can help to guide infrastructure redesign or enforcement policies to reduce RTCs. Nairobi comprises an extensive road network of 6200 km; with the city’s limited resources, addressing road safety across the whole network is difficult. By using this type of geolocated data, urban planners and policy makers can narrow down the problem to the areas with the highest number of crashes. This has been proven to work in developed countries where targeting risky locations led to reductions in the concentration of crashes [48]. As shown in the results, crashes reported on Twitter are highly concentrated, with the top 15% of locations spread across 20 km of road having 50% of the crashes reported on Twitter.

It should be noted that there are some limitations to the approach. The data generated are limited by the coverage of the crowdsourced data. Users are more active on social media at particular times, and it is necessary to possess a smartphone and have access to internet to be able to use the service. This can lead to bias in the reports generated via the crowdsourced data. Only 7.5% of tweets are sent between the hours of 9 p.m. and 6 a.m., and as a result only 12% of the crash reports from Twitter are during this time. There could also be geographic bias if there are areas of the city where people with smartphones are more likely to be present or passing by, and therefore more likely to report. Our real-time motorcycle validation exercise demonstrates the internal validity of the crowdsourced data and the improved algorithm. External validity is more difficult to assess because we do not know what the universe of crashes is. Additionally, we do not know the severity of the crashes reported on Twitter. Therefore, we have no way of knowing if the areas where crashes happen are the most dangerous, which is what policy makers likely would want to target. These caveats should be considered by policy makers when using crowdsourced data to inform policies and targeting.

Despite the limitations, our improved geoparsing algorithm discussed in this paper can begin filling some of the gaps in data in low-capacity and data-scarce settings. And while the crash cluster areas identified by the algorithm may not be the most dangerous or may not represent all crash areas, they nevertheless highlight problem areas. All crashes, minor or severe, have important economic consequences in terms of property damage and lost time and productivity due to the traffic generated (which is one of the reasons the 10