Dar es Salaam suffers from biannual flooding caused by various factors including climate change and rapid unplanned urban growth. Receiving a ESRC-NERC-DFID grant for “Big Data for Flood Resilience in East Africa”, this project investigates how digital footprint data can predict, identify and inform decisions during flood and other natural hazard events. The research informed DFID’s Big Data for Climate Change and Disaster Resilience Report.
Tanzania is rapidly urbanising – the share of the urban population increased from 18 to 27 percent from 1990-2012, with half of the population expected to be living in urban areas by 2030. Similar to trends in most developing countries, Tanzania has a dominant primary city – Dar es Salaam – which accounts for about 40% of the urban population. Dar is the fastest growing city in Africa, growing at an average rate of 5.8 percent annually from 2002-2012 and is expected to achieve megacity status within the next 15 years.
This upheaval brings with it new risks. In April 2014, Dar es Salaam suffered yet another periodic flooding event that affected 20,000 people, killing at least 19 and causing major infrastructural damage, this was just the social cost, the economic and infrastructural cost is hard to estimate with severe implications for public infrastructure and the livelihoods of people.
Receiving a grant from the NERC/ESRC/DFID program “Science for Humanitarian Emergencies and Resilience” (SHEAR) program, this project investigated how non traditional data sources can be leveraged to inform first responders, policy and decision makers on behaviour during floods.
Outcome and Impact
The outputs of this project were multifaceted and included:
- Development of an information dashboard, on top of the Taarifa platform, showing how historical information on flooding could be presented, both from a historical perspective and for real time data analysis (see left for an example of the interface).
- Analysis of transaction data allowing us to explore insights into population behaviour before, during and after floods. This indicated that transactional data has the potential to geolocate flood and hazard events./li>
Ultimately, results can be used to both identify flood locations, as well as to identify expected consequences – providing tangible data for governments and, development agencies a priori, while aiding resilience and relief/reconstruction agencies after floods occur.
Work is currently ongoing to formalise the method of detection, leading to in-field testing with our partners in Tanzania.
These results were disseminated back to stakeholders in Dar es Salaam and Nairobi and to DFID in London, informing the Big Data for Climate Change and Disaster Resilience policy.