Pred-Pol-Pov: Visibility, Data Flows, and the Predictive Policing of Poverty

Abstract

Predictive and data-driven policing systems continue to proliferate around the world, enticing police forces with promises of improvements in efficiency and the ability to offer various ways of addressing the future to pre-empt, predict, or prevent crime. As more of these systems become operationalised in England and Wales, this paper takes up Duarte’s (2021) observation that there is a lack of description as to what such systems actually are. This paper adapts a social network methodology to explore what is a data-driven policing system. Using a police force in England, UK, as a case study, we provide a visualisation of a data-driven policing system based on the data flows it requires to operate. The paper shows how a disparate network of affiliate organisations act as collators of specific data types that are then used in a range of policing applications. We make visible how data travels from its source through various nodes and the various potential points of translation that occur. We show, as others have argued before us, the data points used are proxies for poverty, making certain groups and sections of society highly visible to the digital system whilst other groups and crimes become less visible—and sometimes even hidden.