Our own work with big data focuses on cities. This guest editorial in the UN Global Pulse blog provides as excellent rationale for the focus on cities. In addition, it raises some areas for caution.
Placing algorithms at the forefront (or even in the front-row seat) of decision-making may have potentially severe drawbacks. It’s indeed us who program algorithms, and we are exposed to a variety mistakes while programming. Moreover, basing algorithms on historical data – i.e. the world as it was (or it is, according to currently available data) – makes algorithms predict the future as very similar to the past, which can be bad for many reasons.
Building and/or strengthening resilience in urban systems is a comprehensive eco-socio-technical issue shaped by politics and policies, rather than a mere technical challenge. When trying to make cities disaster-proof, it is also tricky to define what a disaster is – since many small events (e.g. death for malnutrition or untreated disease) that have huge figures if cumulated tend not to be included in common resilience-oriented initiatives.
Finally, just like digital divide characterized the first age of the Information Society, a divide does and will exist in the capacity to gather and analyze data for informed decision-making. This is something all organizations working in the global development field should seriously take into account.