Introduction

The advent of big data and algorithmic decision making has contributed to an overextended faith in the power of numbers. Referring to the sustainable development goals (SDGs), Kaika (2017) discusses the limitations of indicators in giving true representations of socio-economic well-being and progress in cities. Signals of ‘dissensus’, identifiable in social mobilization, are perhaps more adept at capturing ‘what is not working’ through insight afforded by conflict and disagreement (Kaika, 2017). By focusing on what lacks, the spotlight potentially shines on the dysfunction of urban systems and governance, allowing the cracks to show.

I have not been explicit about data in the vignettes discussed thus far, but the nature of knowledge exchange enabled through platforms has emerged as a point of focus. Part of the aim of this chapter is to explore the nature of data and the qualities of knowledge generated through digitally enabled activism. Here, I am particularly drawn to the critique of big data, much of this work inspired by Rob Kitchin and others. The tendency to equate data with a true representation of the city is problematic given the messy work of deliberative democracy and the need for problem solving that is contextually appropriate (Kitchin and McArdle, 2016). Chapter 4 showed how misguided policy around the food system, for example, can impact livelihoods. Knowing the messy city through indicators and aggregating its complexity into an infographic is the ultimate in scientific rationalization and objectification (Kitchin and McArdle, 2016). Ultimately, data are disassociated from identifiable subjects/objects and stripped of context.

Algorithmic governance is productive of subjects and spaces (Kitchin and McArdle, 2016). Indicators are ‘assemblages’ of human and inanimate agency, rather than objective ‘truths’ (Kitchin et al, 2015). Reducing and oversimplifying complex relationships into measurable variables decontextualizes the city and assumes replicability across places.