# **Introduction**

The World Bank has declared that data are the next deprivation to end; they argue that the lack of data causes many of the world’s poorest populations to be overlooked when resources are allocated to address their essential needs [1]. Data deprivation is a pressing challenge with as many as 74% of the global and 97% of the Sub-Saharan African population living in countries without adequate vital registration [2]; one-third of countries lack any poverty statistics [1]; and only 17% of the estimated road traffic deaths are reported in official figures of low-income countries [3]. Without data to inform national and urban policies, the gap between low- and high-income countries will worsen [4]. However, while official statistics are poor, data in the hands of private providers are plentiful, populated by the rapid expansion of mobile phones and social media. Globally, phone penetration reached 67% in 2019 [5], and social media penetration is almost 50% [6]. This provides an opportunity for using crowdsourced data to study major urban and development policies [7–11].

In this project we test the hypothesis of whether privately maintained data can be transformed into a resource to better understand development challenges. Private data have been used to characterize populations from determining poverty to understanding public emotions [12–17]. Here, we use private data to describe the urban environment that affects those populations, specifically analyzing events reported on social media that affect people’s safety such as road traffic crashes, crime or floods. We focus on road traffic crashes (RTCs). Despite being the number one cause of death for children and young adults aged 5-29 years, the lack of adequate data on RTCs is a recognized and unmet challenge <mark>[18]</mark> . The objective is to improve RTC data for urban planners so they can contribute to addressing the high toll of road deaths, estimated globally at 1.35 million a year [3]. Our case study is Kenya, a country with high road mortality, where the official figures are said to underestimate the number of fatalities by a factor of 4.5 [3].

The United Nations’ Sustainable Development Goal (SDG) 3 sets a target to halve road mortality by 2020; progress has been slow, and the target moved to 2030. The Stockholm Declaration by the Third Global Ministerial Conference on Road Safety “Achieving Global Goals 2030” reiterated the call for country investments in road safety–from legislation and regulation, safe urban and transport design, safe modes of transport and vehicles, to modern technologies for crash prevention, trauma care, and urban management. However, resource constraints make it unlikely that countries will be able to meet all of these goals. Instead, countries should strategically invest for the greatest impact. This requires knowing where and when crashes happen, so that resources can be targeted to risky locations and times.

Social media data, with all their biases, can contribute to filling some of the data gaps for urban analysis, planning and management [19]. In this study, we create an algorithm that classifies transport-related tweets into geolocated RTCs for Nairobi. This is done by building on existing literature to test two natural language 2