Working paper – Walking as method in data studies

Maranke Wieringa & Karin van Es (d.d. 17 August 2018)

Walking is ingrained in human, and especially urban (Amato 2004), culture. Recently, there has been a wave of structured, reflective walks in the city aimed at contributing to the agenda of “data studies” (Kennedy 2018). In this paper we seek to parse out the design strategies employed by in these different walks and how they relate to critical data studies. It explores how these walks overlap and diverge in their goals, process, and make-up. We begin by briefly sketch data studies as a field. Following, we explore the historical indebtedness of these walks to earlier work done in the divergent fields of urban (culture) studies/ethnography/ethnomethodology and geography. Subsequently, we juxtapose the several forms of walking concerned with (critical) data studies. The embeddedness of these walks in the everyday urban environment lend divergent “bottom up” (Couldry and Powell 2014) perspectives on the mundane pervasiveness of datafication and thereby contribute to the emerging field of data studies in different ways. The inventory we sketch here has been a first step in articulating our own walking method within the Datafied Society.

What is (critical) data studies?
The scholarly field which interrogates data and datafication has been around for a while now, and numerous scholars have busied themselves with various aspects of the phenomenon. Aside from more general works which discuss the phenomenon of data (e.g. Kitchin 2014) and datafication (Schäfer and Van Es 2018), there are numerous inquiries which deal with more specific aspects of datafication. For instance, the problematics of (big) data (research) have been articulated (e.g. boyd and Crawford 2011; Rieder and Röhle 2012; Rieder and Röhle 2017; Van Schie, Westra and Schäfer 2017; Van Es, Lopez and Boeschoten 2017). The underlying algorithms used for parsing data have been questioned (e.g. Ananny and Crawford 2018; Paßmann and Boersma 2017; Pasquale 2015; Kitchin 2017), and their (potential) bias has been highlighted (e.g. Noble 2018). More fundamentally, the impact of datafication on our society and public values has been questioned (e.g. Van Dijck, Poell and De Waal 2016; Gillespie 2018).

The scholarly investigation of data, as described above, has been termed ‘critical data studies’. Helen Kennedy (2018) rightly critiqued the name of the field with regard to the term “critical”. So far research has focused primarily on the more political economy of data, but following the lead of Couldry and Powell (2014), and Pink et al. (2017) she finds more focus should be granted to everyday life. Kennedy (2018) argues that the everyday experience of the non-expert is a crucial element of the scholarly inquiry of the data. Data is pervasive, and mediates all lives, not those of experts. In light of engagement with data walks, we share this emphasis on the everyday experience of data.

Walking in the city
The urban environment has traditionally been best to traverse while walking (Amato 2004). Over time, new breeds of everyday walkers emerged, for instance “the disciplined pedestrian, the speeding commuter, [and] the idling window shopper” (ibid,153). Walking was not just a way of traversing the city, it was ingrained in public life. Walter Benjamin, with his discussions of the flâneur (A Lyric Poet in the Era of High Capitalism), and his Arcades Project, was one of the influential figures to set walking on the scholarly agenda. The flâneur was, however, but one of the many types of reflective walking to be introduced over the course of time. The Dadaist ‘event’, Surrealist déambulations, the Situationists/Lettrist dérives, the Italian ‘Stalkers’, and psychogeographical explorations of the city are but a few of these reflective walking practices (Solnit 2014; Bassett 2004).

What is striking is that each of these modes of walking produces different kinds of results. While the déambulations practiced by the Surrealists were meant to trigger unconscious wandering, the dérives of the Situationists/Lettrists were more about mapping the ambiances present in the urban environment (Landon 2010). Such walking attitudes have “aesthetic and critical potential” (Bassett 2004), which can provide valuable insights for students, residents, or domain experts. Here, walking becomes a method for research.

There are a number of fields in which walking is used as a method. Below, we sketch three projects in geography/anthropology which draw upon reflective walking, but this overview is by no means exhaustive. The geographer Keith Bassett takes his students on a psychogeographical field trip to Paris. Another geographer, Noora Pyyry, sent ninth-grade students on a dérive-like ‘photo-walk’ to document their engagement with specific places in the city where they would hang out. Through this exercise, students were invited to rethink the places they frequent, and see them in a different light by reflecting on their engagement with the spaces. Anthropology has also been paying attention to the intricacies of walking. Tim Ingold and Jo Lee Vergunst, for instance, organized a walking seminar for peers, to pay attention to the diversity of “what is being purposefully done in walking (arriving somewhere, say)” and “how it is done (by getting there, step by step along a path)”. As such, walking is already an important tool for various disciplines, among which data studies, as we will discuss below.

Walking as a method in data studies
Adam Greenfield and Nurri Kim are seen as a precursor to the data walk phenomenon. They held walkshops on networked urbanism as early as 2010. In these walkshops, a group of 15 participants (a mix of locals and domain experts) would go walking for 90 minutes in a pre-selected area in the city of two-by-two kilometers delimited on a map (called “the box”), looking for “the digital in the physical and vice versa” (Greenfield and Kim, np). The walkshops for Greenfield and Kim are “equal parts urban walking tour, group discussion, and spontaneous exploration” (ibid.). The goal of these walkshops are to create an understanding of the “layer [of information on top of the city], the systems that make it up, and its implications for the freedom to move and act is vital to full citizenship in the congested, contested urban spaces of the 21st century” (ibid.). Their goal is thus very much oriented towards increasing awareness (or literacy) among their participants. After the walkshop, the participants would gather over food and drinks to reflect and discuss their experiences.

More recently, Alison Powell, assistant professor at London School of Economics, has sparked a new interest for data walks. Her data walk practice was very much inspired by networked urbanism walkshops of Greenfield and Kim, and Laura Forlano’s ‘flash mob ethnography’ format. Powell’s process has evolved over time. Additionally, Powell takes the idea of working in small groups with assigned roles (map-maker, photographer, collector, navigator and note-taker), and looking for and documenting tensions from Forlano. Originally, Powell used the data walks as a teaching tool, to help students counter the optimistic rhetoric surrounding big data. Later, in a collaboration with artist/geographer Paula Crutchlow and geographer Ian Cook, the walks acquired a more performative and critical frame. In its present-day form the data walkshop is a way for people to create shared definitions of data in a non-hierarchical fashion through the distribution of expertise.

At Erasmus University of Rotterdam (the Netherlands), Liesbet van Zoonen, professor of sociology, and her team has been taking city employees on walks through their own smart city. The data walkshop developed by Powell is explicitly identified as a source of inspiration for these walks, but Van Zoonen et al. differentiate themselves in that they design the walks in a more directive way. Van Zoonen et al. describe that the researcher plots the route and does some research concerning points of interest beforehand. Before embarking on the walk, they discuss with groups of 4-6 people the concept and goals of the walk. The focus of this kind of data walk is identifying big data in the city and connecting it to political and ethical issues. Van Zoonen et al. articulate their version of the data walk as a form of participatory action research; while they use the walks as a means to raise awareness and a critical attitude in civil servants towards data, the researchers simultaneously get access to the knowledge and beliefs of the participants about datafication of the city.

A different kind of data walk can be identified in the work of David Hunter. Hunter is a designer and educator, and this heavily influences his approaches to the data walk format. Rather than raising critical questions about data per se, the aim of his research project is gathering environmental data of a specific area (often through multiple walks over time) and  building a picture of the multilayered “dataspace”. Having given workshops for a series of different publics and timeframes, his process varies slightly per case. The goal of these walks is creating awareness about how data is gathered, analysed and communicated. In other words, participants learn about data literacy through exchange with others and by doing (and sometimes failing).

Affiliated with the topic of data, Malte Ziewitz, assistant professor in Science and Technology at Cornell University, conducts ‘algorithmic walks’, often within an educational context. In these walks the objective is not to define what an algorithm is, but rather to focus on the work that our reasoning with algorithms does. The walk allows for seeing and experiencing the world through the lens of an algorithm (or at least our common-sense understanding of the algorithm). This algorithmic walk has 2-3 participants devise an algorithm beforehand that guides their walk. The recursive set of rules they write will guide their traverse of the city. As the group walks, they will notice moments in which the algorithm inevitably falls short. Such moments are then documented through field notes and photographs.
Table 1. Inventory of different data walks

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