TRANSLATING (WITH) FRANK. LAS TEMPORALIDADES ASINCRÓNICO-ACELERADAS DE UN ALGORITMO URBANO

Abstract

On any street corner in the city, thousands of algorithms intersect, optimising product deliveries, processing CCTV cameras, geolocating people, ingesting data from environmental sensors, measuring noise levels or street cleanliness, calculating the best area to open a new shop or assisting urban planners in their decision making. Most of these algorithms, linked to the digital economy, are processed on online servers, while the consequences of their calculations materialise in physical spaces, increasing the complexity of understanding the urban environment in general.

This research explores the socio-spatial impact of food delivery platforms as urban actors in their own right. These platforms use their algorithms under asynchronous temporal logics, prioritising efficiency and acceleration, which generates tensions with other urban temporalities. The study focuses on Deliveroo's ‘Frank’ algorithm, developed between 2018-2021, which optimised efficiency through predictive calculations and machine learning models, and investigates the implications of these operations, highlighting how current urban infrastructures, based on data and algorithms, are reshaping the way we experience and live time in the city.

Translated with DeepL.com (free version)