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Algorithmic suturing: platforms, motorcycles and the "last mile" in urban Africa

May 2023
International Journal of Urban and Regional Research 47(817)

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University of Cape Town

The 'last mile' is not only a powerful metaphor of contemporary life, but also the tangible site of a challenge, whether for governments reaching their citizens, or companies their customers. In urban Africa, this challenge is compounded by the fragmented material condition of cities. As a result, a growing number of tech companies have been compelled by the possibility of creating digital platforms that address the unique logistic configurations of African cities, often enrolling informal systems, such as motorcycle taxis, to address spatial and economic fragmentation. Through the perspective of three Nairobi-based startups that incorporate motorcycle taxis into their last-mile platforms, this paper illustrates how processes of 'algorithmic suturing' knit together the loose ends of splintered urban networks thanks to platform business models that visualize the last mile as a site of optimization. In parallel to common understandings of suturing within African infrastructure debates, which foreground makeshift practices of the urban poor, this paper argues that algorithmic suturing is a speculative endeavour through which urban fractures are made legible as sites of value. By stitching city fragments, these platforms envision large data-driven urban economies which interface informal mobility networks and the shifting urban demographic of the lower-middle classes.

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INTERNATIONAL JOURNAL OF URBAN AND REGIONAL RESEARCH

DOI:10.1111/1468-2427.13200

— ALGORITHMIC SUTURING: Platforms,

Motorcycles and the ‘Last Mile’ in Urban Africa

,    ’ 

Abstract

The ‘last mile’ is not only a powerful metaphor of contemporary life, but also the

tangible site of a challenge, whether for governments wanting to reach their citizens or

companies wanting to reach their customers. In urban Africa this challenge is compounded

by the fragmented material condition of cities. As a result, a growing number of tech

companies have been compelled by the possibility of creating digital platforms that

address the unique logistical conﬁgurations of African cities, often enrolling informal

systems such as motorcycle taxis to address spatial and economic fragmentation. Through

the perspective of three Nairobi- based startups that incorporate motorcycle taxis into

their last- mile platforms, this article illustrates how processes of ‘algorithmic suturing’

knit together the loose ends of splintered urban networks thanks to platform business

models that visualize the last mile as a site of optimization. In parallel with common

understandings of suturing within African infrastructure debates which foreground

makeshift practices of the urban poor, this article argues that algorithmic suturing is a

speculative endeavour through which urban fractures are made legible as sites of value.

By stitching together city fragments, these platforms envision large data- driven urban

economies which interface with informal mobility networks and the shifting urban

demographic of the lower- middle class.

Introduction

The ‘last mile’ is a powerful metaphor of contemporary life, shifting meanings

across contexts and among dierent actors. Supply chain managers, for example, deﬁne

the last mile as the ultimate leg of goods reaching their final customers, whether a

transportation hub, a warehouse or an individual person (Hayes,2021). For development

technocrats in Africa the last mile refers to the distance between households and

centres where public services such as healthcare are available. For telecommunication

companies the last mile allows individual users to connect to the backbone of bigger

communication networks. For AI scholars the last mile captures the ‘ghost work’ that

humans need to perform when algorithms fall short (Gray and Suri,2019).

Despite these various meanings, there is a thread running through these

deﬁnitions. Consider, for example, the schematic representation in Figure1, which

accompanies the Wikipedia entry for ‘last mile’ (as of July 2022).1 In the visualization,

the last mile is represented by the loose ends of a networked tree. These loose ends are

not just the ultimate conduits of networked systems; they are also the terminals where

infrastructural grids scatter and break, where they frictionally interface with each other,

and where urban splintering (Graham and Marvin,2001) and suturing (De Boeck and

1 Using a few examples (namely, tree trunks vs. root hairs, rivers vs. drip irrigation, interstate highways vs. back roads,

and intercontinental cables vs. user internet access), the Wikipedia caption illustrates the capillary and peripheral

nature of the last mile in a networked system.

       .

This article would not have been possible without the collaboration of our wider team: Rike Sitas, Prince Guma,

Alexis Gatoni and Alicia Fortuin. A draft of this work was presented at the Beyond Splintering Urbanism workshop

in Autun, where we benefited from Steve Graham and Sophie Schramm’s close readings and the generous

support of Simon Marvin, Jon Silver, Alan Wiig, Jonathan Rutherford and Colin McFarlane. Andrea Pollio’s work

has received funding from the European Union’s Horizon 2020 research and innovation programme under the

Marie Sklodowska- Curie grant agreement No 886772. Liza Rose Cirolia and Jack Ong’iro Odeo received funding

from the Volvo Education and Research Foundation (VREF) under the Mobility and Accessibility in African Cities

programme (MAC).

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,

distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

POLLIO, CIROLIA AND ODEO 2

Baloji,2016) become a matter of laborious coordination between dierent technical

systems. Because of these frictions, reaching the last mile is the costliest and most

challenging part of many businesses.

At a time of emerging forms of what Moritz Altenried(2019) has termed

‘logistical urbanism’, global platform companies are making these ‘problems’ of last- mile

coordination their core business model: Uber with e- hailing; Alibaba with drop-

shipping; Glovo with Q- commerce;2 as well as Amazon (and many others) with their

mechanical Turks.3 In urban Africa

—

the setting of this article

—

these last- mile

challenges are compounded by the fragmented material condition of cities. Low- density

sprawl, under- maintained road networks, lack of addressability and fractured service

2 Q- commerce or ’quick commerce’ (sometimes referred to as ’e- grocery’) is the on- demand outsourcing of grocery

shopping to platform workers.

3 Amazon Mechanical Turk is a platform for hiring remotely located ‘crowdworkers’ to perform discrete on- demand

tasks. Although this is not necessarily an urban phenomenon, there is a distinct urban bias to the geographies of

on- demand work.

  A visual representation of the last mile

—

the thinner lines or loose ends

—

in a

hierarchical network (source: Wikipedia, from user Dycedard; no changes were made to

the image: https://upload.wikim edia.org/wikip edia/en/3/32/The_last_mile_hiera rchy.svg)

High capacity, long distance conduits

Examples:

Tree trunks

Root hairs

Drip irrigation

Capillaries

Appliance cords

Back roads

User Internet access

Rivers

Arteries and veins

Power grid

Interstate highways

Inter

continental ﬁber

Widely share

costs

Locally shar

costs

Lower capacity, short distance conduits

14682427, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1468-2427.13200 by Cochrane Romania, Wiley Online Library on [23/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

3ALGORITHMIC SUTURING

delivery systems express both obstacles and opportunities for the optimization of last-

mile problems through digital platforms.

Building on this insight, the article focuses on startups that employ digital

platforms to develop last- mile economies by incorporating motorcycle taxis into their

algorithms. As in other Southern cities, in large African metropoles such vehicles are

perfectly formatted for the kind of fast- paced and short- distance movements necessary

to overcome spatial and economic fragmentation. They already constitute a lifeline

for the mobility of people and goods where larger vehicles fail to provide sucient

capillarity. And so motorcycles have compelled both global and domestic tech companies

to find ways of subsuming riders into platforms that seek to address the last- mile

problem with dedicated digital solutions.

While most scholarly work on this phenomenon has rightly focused on the

issue of ‘platform labour’ (Van Doorn,2017), highlighting the forms of precarity

and exploitation that are beholden to the digitization of mobility in urban Africa

(see Pollio,2019; 2021; Anwar and Graham,2020; Doherty,2020; Iazzolino,2021;

Odendaal,2021; Anwar et al.,2022; Arubayi,2021), our focus on startups deploys a

dierent entry point. Speciﬁcally, the article charts how these ﬂedgling companies make

the ‘work’ of last- mile coordination legible, by visualizing the opportunities through

which motorcycle taxi networks can be mobilized to address economic and spatial

fractures in African cities. As detailed in what follows, we use the notion of ‘seeing like

a last- mile business model’ (inspired by James Scott’s1998 book Seeing Like a State)

and insights from media studies to show how startups themselves understand and make

legible the last mile, as well as how we, as researchers, can methodologically encounter

the last mile, not (only) as a metaphor, but as an algorithmic practice.

In doing so, we describe ‘algorithmic suturing’ as a key operation at the nexus of

digital technologies and motorcycle taxis. Algorithmic suturing is the knitting together

of the loose ends of splintered urban networks and informal economic activities through

platform business models that visualize the last mile as a site of optimization and value

creation. This conceptual contribution emerges from ongoing research on the

platformization of two- wheel logistics in Nairobi, Kenya (Sitas et al.,2022). Much as in

many African cities, Nairobi’s motorcycle- taxis

—

called boda boda

—

are used to carry

people, goods and parcels. They are one of the fastest ways to move through the city and

form an integral part of everyday life. They generally fall into what is called

‘paratransit’4

—

a blurry category that straddles public and private modes of movement

and includes minibuses (usually 14- to 16- seater vans), tuk tuks (three- wheelers) and

motorcycles. As a prosthesis to splintered network economies, boda boda operate where

other mobility and logistics options are too costly, too cumbersome, or simply not

ﬂexible enough to address the last- mile problem. Increasingly, they do so through data-

driven platforms, as will be shown in our three case studies.

Drawing from this work on Nairobi, the article makes two arguments. First, we

suggest that the last mile renders the loose ends of networks legible as a problem of

coordination, as a potential business opportunity, and as a key site of optimization

—

this is, after all, the most expensive part of global supply chains. Seeing like a last mile,

which we intend here more narrowly as a last- mile business model, thus reveals some

of the hidden scripts that algorithmically suture the city, and some of the behind-

the- scenes processes of coordination through which new urban networks are given

eect and substance thanks to last- mile platforms. This last- mile view also creates new

‘leads’

—

things made visible that otherwise might not be. We highlight some of these in

the conclusion

—

for example, the importance of the Chinese internet industry in urban

Africa

—

in the hope that they will inspire scholars to take these emerging algorithmic

economies seriously.

4 See Pirie(2014).

POLLIO, CIROLIA AND ODEO 4

Second, we argue that algorithmic suturing challenges some of the conventional

notions within Southern infrastructure scholarship concerning how and by whom

material and network improvisation takes place.5 While acts of suturing are often read

as a practice of urban survival at the margins of the city, last- mile business models

extend this story. Platforms stitch together city ‘fragments’ (McFarlane,2018),

developing algorithmic interfaces between precarious workers and the growing

demographic of the lower- middle class in urban Africa. This lower- middle class

demographic is not a discrete category or group, but rather a market both envisioned

and, in part, produced by platformed service delivery. Aspirational processes of ‘middle-

classing’ have been addressed in the financial anthropology of African capitalism

(James,2021), yet their role in shaping urban change (Mercer,2014; 2020) is also evident

in the future- making practices of last- mile algorithms.6

Overall, our contribution to urban scholarship stems from a desire to produce

a meso- level analysis of urban platforms, without dissolving their speciﬁcity into a

frontierist critique of global technocapital’s search for new profit opportunities in

African cities (Ouma,2017), nor seeking to offer a fine- grained investigation of the

infrastructural labour upon which the former depend. While cognizant that these too

are crucial analytical entry points, we suggest that a shift in perspective

—

see Qadri

and D’Ignazio(2022) for a different but complementary approach to this one

—

better, taking for a moment the perspective of last- mile startups, sheds light on some

of the emerging urban geographies of platformization in Africa and their implications.

Before moving on to these last- mile ‘views’, however, the next three sections provide,

respectively, a description of algorithmic suturing, an overview of digitization in the

boda boda sector in Kenya, and a detailed account of our methodological approach.

Algorithmic suturing

The concept of algorithmic suturing draws on the insight that urban life in Africa

is often made possible by practices of infrastructural repair, mending and patchwork.

Specifically, Filip De Boeck and Sammy Baloji use the metaphor of the ‘suture’ to

describe the junctures and seams through which urbanites

—

often against all odds

—

ﬁnd ways to ‘ﬁll the gaps, overcome the hiatus, design realignments and thereby redeﬁne

the zero … into a possibility, a something else, a surplus’ (De Boeck and Baloji,2016:

16). More broadly, it was AbdouMaliq Simone’s ﬁtting yet sometimes misunderstood

notion of ‘people as infrastructure’ that drew attention to the machinic acts of suturing

through which life is reworked and made possible in contexts of extreme disposability

and precarity (Simone,2021).

With ‘algorithmic suturing’ we combine this attention to the ‘mathematics’ of

collective urban life (Simone,2021: 1343) with an expansive deﬁnition of algorithms

(Gillespie,2014). More than just lines of code (Seaver,2017), algorithms have a genealogy

that predates digital technologies (Striphas,2015; Daston,2022). An algorithm is in fact

‘an abstract diagram that emerges from the repetition of a process, an organization of

time, space, labor, and operations’, as well as ‘the division of this process into ﬁnite steps

in order to perform and control it eciently’ (Pasquinelli,2019: 6). Most importantly,

Pasquinelli further writes, ‘an algorithm is an economic process, as it must employ the

least amount of resources in terms of space, time, and energy, adapting to the limits of

the situation’ (ibid.). Moreover, as Lilly Irani explains, conﬁning algorithms to a puriﬁed

notion of software ‘automation’, devoid of the multiple forms of labour intrinsic to it,

risks reinforcing the narrative adopted by platform companies, which essentially makes

5 On a related subject, see Cirolia et al.(2021), Günel(2021), Lemanski(2021), Cirolia and Pollio(forthcoming).

6 In this article we are not concerned with (nor would we have the data for) defining the sociological contours of this

demographic category (the lower- middle class) but we treat it as an emic category that our informants used to

describe an under- served market and therefore an opportunity for processes of algorithmic optimization.

5ALGORITHMIC SUTURING

some forms of work invisible to the benefit of others (Irani,2015). Put differently,

algorithmic suturing also captures the limits of algorithms, in that they are themselves

always in need of spatial and other ﬁxes (Pollio,2021).

The concept of ‘algorithmic suturing’ thus dovetails with two important strands

of scholarship. First, as we have seen, work dedicated to understanding how, in African

cities, hybrid, alternative and incrementally improvised conﬁgurations suture disrupted

urban fabrics both physically and metaphorically (Silver,2014; De Boeck and Baloji,2016;

Baptista,2019). These scholars have also challenged the binary between networked and

post- networked systems (Akallah and Hård,2020; Cirolia et al.,2021), charting the

strategies of ‘technological bricolage’ that exist on the ground (Lemanski,2021) and

stretching the ontological scope of what constitutes infrastructure (e.g. Larkin,2004;

Kimari,2021; Simone,2021). Second, algorithmic suturing speaks to a growing body of

literature that speciﬁcally maps the patchworked nature of digital technologies in urban

Africa, oering a perspective on the incomplete, piecemeal and adaptive remaking of

platforms, and challenging simplistic visions of domination and leapfrogging (Guma

and Mwaura,2021; Guma,2022; Guma and Wiig,2022; Odendaal,2021). This work

ﬁts into a wider body of scholarship on digital transformation and entrepreneurship

in the continent (Friederici,2018; Pollio,2020; 2022a; Guma and Monstadt,2021;

Odendaal,2023).

Motorcycles, algorithms and the materiality of African cities

Our focus on motorcycles as a last- mile device is not incidental. Their

contribution to the movement of people and goods in Africa is vital to the everyday pulse

of cities (Agbiboa,2020).7 Where private car ownership is a relatively new addition to

the ascent of the middle class, reliance on alternative modes of movement has long been

a necessity. In many African cities, motorcycles are indeed the most eective vehicle for

short, fast trips. For a city like Nairobi, conservative estimates by the Boda Boda Safety

Association put the number of motorcycle riders at ﬁfty thousand (Omulo,2021)

—

a total

that doesn’t include unregistered operators.8 Owing to this ubiquity in Kenya’s capital

city (as well as in smaller urban centres) a mixture of internationally imported and

locally assembled motorcycles enables a distributed mobility service that complements

larger vehicles such as minibuses (locally called matatu9 and generally used by passengers

for longer trips) and delivery vans (used for the movement of larger quantities of goods).

The modal choice of the motorcycle, particularly for last- mile transportation, is

a response to the material conditions of the African city.10 In the context of large,

sprawling urban footprints, getting passengers and goods to the peripheral outskirts or

to the crevices of high- density suburbs requires a fuel- ecient, agile vehicle. Such

agility of movement is likewise necessary to bypass the thick trac which clogs highways

and intersections, and to overcome incomplete and under- maintained road networks

(for example, navigating rutted surfaces unsuited for cars). Nairobi is a particularly

ﬁtting vantage point from which to observe this: a city splintered by early colonial plans

(Ese and Ese,2020) and contemporary large- scale bypasses (Guma et al.,2023), and

dotted with leafy, middle- class cul- de- sacs, busy commercial malls, hyper- dense

7 The same is true in other large Southern cities too. See, for example, the work of Claudio Sopranzetti in relation to

Bangkok (2013), Sam Nowak(2021) and Qadri and D’Ignazio(2022) on Jakarta, and Kevin O’Neill for Guatemala

City (2022).

8 Due to the absence of an official database, the exact number is unknown, but it is likely to be much higher. The

National Crime Research Centre (Opondo and Kiprop,2018), for example, reported that 1,393,390 motorcycles

had been registered with the National Transport and Safety Authority as of February 2018. The Kenya National

Bureau of Statistics (KNBS,2022) also reports that the number of motorcycles registered in 2021 rose to 285,203

from 246,705 the previous year. How many of these motorcycles are operating as boda boda in each city/region

remains undocumented, however.

9 For a history ‘from below’ of this unique, embattled transit network, see Kenda Mutongi’s Matatu (2017). An

interesting visualization project of the matatu network is captured in Klopp et al.(2017).

10 Boda boda, as Joyce Nyairo(2023) has brilliantly explained, are also part of the making of citiness itself.

POLLIO, CIROLIA AND ODEO 6

suburbs, master- planned estates, sweeping informal settlements, warehousing precincts,

and ‘plotted’ (Karaman et al.,2020; Maina and Cirolia,forthcoming) peri- urban fringes.

In this context, motorcycles are a vital and rapid undercurrent. They stitch together

fragmented parts of the city, hybridizing service delivery (Jaglin,2014) and overcoming

the brittle and enduring legacy of colonial planning, postcolonial projects of modern city

building, and forced informality (Ese and Ese,2020). Alongside passenger transit,

motorcycles have for decades also been used for last- mile delivery, as they are not only

fast but also aordable to maintain and operate

—

all of which contributes to reducing

the supply- chain costs for logistics companies.

Despite this important role of motorcycles in urban economies, most African

governments have taken punitive measures against the sector (see Goodfellow,2015).

While two- wheelers were initially conceived as a solution to austerity measures

introduced for structural adjustment (Rizzo,2002), today they are seen as an unwieldy

urban industry in need of some form of regulation. However, as the riders are mostly

young men, they also constitute an important political clientele. Central and local

governments have therefore pursued ambivalent and contradictory practices. The most

draconian of these policies have been implemented to address issues of passenger

safety

—

measures which artiﬁcially segregate using motorcycles to move people around

from using them to move and deliver goods. Conversely, eorts to modernize urban

mobility generally include banning motorcycles from particular parts of cities (usually

city centres), and supporting investment in large- scale transport projects such as Bus

Rapid Transit (BRT) systems

—

both of which fail to overcome the incredible need and

demand for distributed, capillary delivery and pillion services. The Kenyan government,

in particular, has long turned a blind eye to the boda boda sector

—

allowing the

emergence of competing and self- regulating voluntary associations

—

but it is now

involved in a regulatory attempt that, if successful, will radically transform the industry.11

In this context, the tech community has been increasingly compelled by

motorcycle mobilities over the last few years. This also aligns of course with a much

larger trend, whereby digital innovation seeks out ‘untapped’ niches in city systems,

looking for sites where platform solutions may be able to solve what appear to be urban

problems. Some of the digital innovation sits in the e- hailing space (allowing end- users

to connect to riders through mobile phone- based applications such as Uber boda or

Safeboda) (see Doherty,2020). However, the invisible majority are focused on improving

the business models of companies that utilize motorcycles for last- mile, express and on-

demand deliveries (Cirolia et al.,2023).

In Nairobi, a city seeking to implement ambitious smart city plans (Guma and

Monstadt,2021) and long dubbed Africa’s Silicon Savannah (Graham and Mann,2013),

a booming startup ecosystem has emerged in the last decade as a melting pot of local

entrepreneurs, diaspora returnees with experience in the global tech industry, foreign

companies, and international venture capital (Rosenberg and Brent,2020). All these

players have thus contributed to an explosion of platform experiments addressing

not just motorcycle mobilities but many facets of the Kenyan economy, from payment

technologies to agriculture (Mann and Iazzolino,2021). While some of these platforms

are international, accessing venture capital to ﬁnance the trial and interaction of their

oerings, several experiments are in fact driven by home- grown, bootstrapped startups,

many of which only last a few months after being launched and then wilt away or

11 Recent legislative amendments to the National Transport and Safety Authority Act seek to give the latter agency

power to establish systems and procedures for the registration, licensing and operations of these categories of

vehicles, just as it does with other motor vehicles. Additionally, county governments are empowered to enact

regulations for boda boda operations within their jurisdictions. In Nairobi, boda boda operators will in future be

registered by the county government according to the cooperative society to which they belong and their zone of

operation; motorcycles will be fitted with digital plates containing information about the riders.

7ALGORITHMIC SUTURING

pivot their business models to ﬁnd more proﬁtable last- mile market niches (Cirolia et

al.,2023).

As we will see, the motorcycle is just one, albeit crucial, component of the

platform arrangements through which last- mile startups ‘see’ the city. It is a vehicle (in

all senses) for us to chart the practices of algorithmic suturing that seek to ﬁll the gaps

of a fragmented city, both spatially and economically.

A note on method

Our approach in this article is usefully summarized by the metaphor of

‘seeing like a last- mile business model’, which explicitly borrows from Scott’s critique

of ‘authoritarian high modernism’(1998). The original phrase, ‘seeing like a state’,

described the multiple acts of ‘bringing into view’ which administrative designs need

in order to simplify, and therefore govern, complexity. While Scott’s insights have

been variously challenged

—

both in their empirical validity and their applicability to

corporate capitalism (e.g. in Ferguson’s Seeing Like an Oil Company[2005])

—

the notion

that the making legible of things (for example, through measurement protocols, cadastral

maps and census taxonomies) is a crucial register of power has generated a wealth of

contributions in various disciplines

—

from Dourish’s Seeing Like an Interface(2007) to

Seaver’s Seeing Like an Infrastructure(2021), to name just two.

In a more science and technology studies (STS)- inspired interpretation of ‘seeing

like’ as a performative act, whereby legibility works both on what sees and on what is

seen, John Law(2009) instead argues that realities are enacted through the methods

that are purportedly meant to simply bring them into view. This insight lies at the centre

of our methodological approach to understanding how last- mile ‘views’ render network

fragmentation legible as a problem of platform coordination, whilst enabling us as

researchers to follow additional leads. We would also argue that this intuition informs

much of the critical scholarship on digital platforms

—

showcasing how the data

processes through which algorithms operate are never just descriptive, but follow

multiple performative logics12 (as described by Fourcade and Healy in Seeing like a

Market[2017]).

We are equally inspired by a body of scholarship in media and cultural studies

which has shown the importance of network imagination, arguing that networks are

material and metaphorical infrastructures of sensibility that mediate and narrow our

experience of the world (Terranova,2004; Galloway and Thacker,2013; Munster,2013).

According to Patrick Jagoda, for example, networks are realities that exist at the edge

of sensibility. They are at once something that we see and a way of seeing, inextricably

both material and metaphorical (Jagoda,2016). Infrastructures, therefore, cannot be

thought of separately from the ways in which they are visualized. And as we have seen

in Figure1, the last mile is a key component of network visualization, pointing to its

peripheral terminals. Visualization, in this context, is not just a matter of ‘pictures’, but

a broader process of rendering visible (Halpern,2015).

Building on these different and at times disconnected contributions, for us

‘seeing like a last- mile business model’ is both a descriptive device that captures the

ways in which motorcycle taxis become inscribed in the algorithmic suturing of the

loose ends of urban networks, and a methodological orientation. It is a methodological

orientation because seeing like a last mile means charting the business models of these

platforms: the algorithmic scripts through which the ends of networks are made visible

as sites of coordination and optimization. As Liliana Doganova has shown in her work

(e.g. Doganova and Eyquem- Renault,2009), business models are indeed particular

kinds of market abstractions: ways of seeing that participate in the making of economic

12 See, for example, Wendy Chun’s Discriminating Data (2021).

POLLIO, CIROLIA AND ODEO 8

realities. In other words, last- mile business models are one of the ‘legibility’ scripts

through which fractured urban networks are coordinated into digital platforms. When

algorithmically visualized, fragments and sutures become more than just metaphors

of urban life; they become discrete processes of spatial and economic optimization

(Pasquinelli,2019).

Cognizant that the perspective of platform startups may be contested or

reinforced by how the riders themselves ‘see’ the algorithmic economies in which they

are imbricated, as Qadri and D’Ignazio write in Seeing like a Driver(2022), our practical

strategy to see like a last mile involved a scoping of all Nairobi- based digital platforms

that incorporate boda boda in their operations and a mapping of both their value chains

and supply chains to understand the dierent roles boda boda play in each business

model. This work included trial- and- error practices of ‘playing’ with the apps, trying

out websites, making orders ourselves, speaking to customer services, and analysing

online information available for each platform, as well as accessing private archives

that document investments in digital companies. In the cases we now move on to

discuss, we were also able to conduct traditional ‘corporate interviews’ with founders

and other platform managers (Schoenberger,1991). To do full justice to this empirical

richness, the three cases below are presented as detailed vignettes that showcase how

each business model sees fragmented urban economies as sites of algorithmic suturing,

viewed through the eyes of those whom Jane Guyer(2016) calls ‘platform attendants’

—

that is, the creators and managers of last- mile platforms.

Seeing like a last mile in Nairobi

Global e- hailing companies such as Uber and Bolt, Africa’s largest e- commerce

platform Jumia, and Delivery Hero- owned e- grocer Glovo are undoubtedly the most

visible operators in Kenya’s last- mile urban economies. They are not, however, the

sole players. As our research shows (Sitas et al.,2022), not only do dozens of smaller

platforms exist on the market, seeking to create alternatives to these oligopolies, but

several digital startups are also active in the value chain of these bigger platform ﬁrms.

Through their last- mile business models, many of these ﬂedgling companies onboard

motorcycle riders into the functioning of their algorithms, with the aim of using

informal boda boda networks as a spatial suture that reduces logistical costs and creates

additional avenues of optimization.

In this section of the article, we chart the business models of three such

startups

—

three smaller platforms that are using boda boda to address a number of

last- mile problems

—

ranging from the exploitative nature of precarious work, to the

addressability of lower middle- income residents, to the reconﬁguration of hub- and-

spoke logistics in Kenya. The ﬁrst company, Cleanify, is a gig- work platform for domestic

workers. The other two companies, Shoppist and Dasher, are an e- commerce platform

and a last- mile logistics provider, respectively. For each of these cases, we map how

their business models frame boda boda as a prosthetic link to issues of algorithmic

optimization, and foreground what these ways of seeing make legible about urban

economies in Nairobi. We therefore ask the reader to take the perspective of these

businesses and to ‘see’ the splintered urban realities as coordination problems that can

be bridged by motorcycle taxis.

— Networks of waiting among last- mile workers

Soft- spoken and eloquent, Mike13 is the co- founder of Cleanify, a four- year- old

startup oering on- demand laundry and household cleaning services. With a degree in

statistics and a passion for crunching data, Mike does not have the boisterous panache of

13 All names of people and companies in this article are pseudonyms that we used to protect the confidentiality of

our interviewees.

9ALGORITHMIC SUTURING

the prototypical startup founder; by his own admission, he had to learn how to successfully

promote his company by imitating the seemingly limitless conﬁdence of others. To do so,

when he and his co- founder started the company, they joined one of the co- working

spaces that Nairobi is famous for. Rubbing shoulders with other ‘techies’ taught them how

to perform at pitching events and competitions. Four years later, and despite the COVID- 19

slow- down, Cleanify is running at a profit, provides employment for more than 200

women, and recently raised capital to switch from a web- to a mobile- based application.

Cleanify’s business model looks very simple on paper: it is a gig- work platform

through which households can hire the services of a cleaner. By charging a fee on

each transaction, Cleanify replicates the business model of large platform companies,

matching freelance labour with local demand. The work of the algorithm also seems

rather simple: by geolocating both homes and cleaners, the platform connects the supply

and demand of household cleaning services. However, as Mike explains, things are more

complicated than this, both in the demand and in the supply of these services. As most

middle- class Nairobians already have regular (often live- in) domestic help, the demand

for gig work comes from the lower- middle class, especially from households that cannot

aord the services of a regular or live- in domestic help and which (more importantly)

lack cleaning appliances such as washing machines. In response to this demand, families

rely on the informal work of women sometimes referred to as mamafua or mamasaﬁ

(Kiswahili for cleaning mother/lady). Mamafua are known to be found in specific

waiting areas, usually at the interstice between wealthier and poorer neighbourhoods.

They rely on the odd jobs that neighbourhood families have for them, usually hand-

washing clothes and, occasionally, heavy- duty housework.

Cleanify recruited and trained a number of these women, taught them how to

use their smartphones to receive and locate a ‘call’, assigned each of them a unique

identiﬁcation (ID) code, and linked them to an algorithm through which the logistics of

the last- mile problem could be optimized. It is at this point that motorcycle taxis come

into play. Much like these women, boda riders wait in speciﬁc areas of the city. Connecting

these two networks of waiting spaces, Cleanify’s algorithm determines whether a job

will require the support of a nearby motorcycle taxi and, because mamafua cannot

normally aord this commuting service, adds a half- dollar fee (50 Kenyan Shillings) to

the end customer’s bill. In this way, the platform relies on the boda riders’ unparalleled

knowledge of the city, addressing a logistical problem inherent to freelance housework.

Yet Mike is adamant that this last- mile problem was not just a proﬁt opportunity.

He explains:

You know, platform gig companies are very exploitative because their business

model functions on very small margins. Our idea was to start from the opposite

side of the business model and ask how these small margins could actually help

the livelihoods of these … precarious workers while addressing a need of an

under- served market.

As he further notes, it is in the marginal price difference between informal

work and gig work that Cleanify’s last- mile innovation lies. End customers are willing

to pay slightly more than they would normally because the quality of the service is

better, faster, more consistent and more reliable

—

not least, thanks to the boda riders.

It is in this willingness to pay (marginally) more that Mike and his business partner

saw the possibility of injecting a different kind of rationality. Contrary to platform

business models that seek to drive the cost of labour down, Cleanify operates to render

fluctuating informal gains and working conditions more stable. The unique ID, the

iterative training (which allows for returning customers), the plug- in of motorcycle

taxis, and the algorithmic distribution of calls now provide every mamafua with a

monthly income that is above the national minimum wage.

POLLIO, CIROLIA AND ODEO 10

The last- mile algorithm, however, is not just a piece of software. It entails a

careful process through which Cleanify enrols various infrastructures, including human

networks that make the algorithmic suturing possible. In Mike’s words:

Each area now has a group leader, and the group leader is the one who

coordinates all this. So when an SMS goes to a lady and she needs a rider, she

goes to the group leader, who is usually stationed in the same waiting area that

we have selected as a base, and she arranges a ride. Usually our bases are close

to boda boda waiting areas. And the company reimburses the riders at the end

of the day. It’s a dynamic web network that we are trying to make work through

the platform. The ﬁrst step was to envision the interfaces between these smaller

networks and from there imagine how some key players could function as

mediators between the platform and the complex dynamics of each area.

As Cleanify grows, so does the number of networks with which it seeks to

interface. Informal ﬁnancial infrastructures are the latest addition. When Mike and his

partner saw that mamafua were starting table- banking groups with their savings, they

prototyped a ﬁnancial scheme that allows cleaners to buy shares in a savings account

owned by the company. In this way, each saving club member can borrow or withdraw

what they contributed (as with a normal table- banking protocol), while also beneﬁting

from a basic interest rate that cash- based saving does not yield. These are all very small

improvements, Mike admits, but they became visible by looking at the margins of the

many fractured networks and treating them as data problems of algorithmic distribution:

by seeing Nairobi’s urban fragments as a challenge for last- mile coordination.

What we thus begin to see, through Cleanify’s last- mile business model, are

the complex operations through which disjunctions in urban economies

—

such as

domestic housework

—

become legible as problems that require algorithmic suturing.

In these operations, boda boda are one of the infrastructural optimizations through

which platforms seek to interface with various precarious networks in the city: from

cleaning services to pillion commuting and even informal banking. Rather than simply

disciplining labour, or replacing more secure forms of employment with gig work,

Cleanify’s algorithm is designed to optimize marginal gains at the intersection of two

demographics

—

the lower- middle class and informal workers

—

that not only constitute

the bulk of Nairobi’s dwellers, but are both extremely sensitive to changes in the price

of piece work, whether to earn a living wage or to pay for essential services. The next

two case studies will speak more directly to the question of aordability for the lower-

middle class, and also to the unique last- mile problems that arise through creating

e- commerce solutions for a shifting and elusive demographic group (Darbon,2019)

that deﬁes sociological deﬁnitions but very clearly shapes how digital platform startups

visualize their markets.

Before moving on to the next company, it is worth noting one last important

thing about Cleanify. Like any startup, Mike and his co- founder have ambitious plans for

the future of the business. These plans include a distributed network of Cleanify- owned

ghost laundromats which will increase the number of heavy- duty cleaning options

available to its customers. It goes without saying that boda boda will provide the logistics

between these laundromats and households. In other words, seeing through a last- mile

business model also sheds light on the speculative urban networks that are not there yet,

but are waiting in the wings of future algorithmic iterations.

— Last- mile addressability in e- commerce

With a decade of experience in marketing for e- commerce companies, Li landed

in Nairobi from southern China early in 2019 to join the marketing department of a

Chinese retailer selling fast- moving consumer goods (FMCGs). He now works as the

11ALGORITHMIC SUTURING

head of marketing for Shoppist, one of the up- and- coming e- commerce platforms in

East Africa, operating in Kenya and Uganda. For many young managers of international

Chinese companies, Li explains, moving to Africa is an ambitious career move, usually a

trampoline to somewhere else in the world. In his case, however, Shoppist represents a

rather unique journey, because the company was actually founded in Kenya in the mid-

2010s by a Chinese expatriate, a former Huawei executive.

The inspiration for Shoppist was obviously the Chinese company Alibaba and the

rags- to- riches story of its founder Jack Ma, who made a fortune by creating the largest

e- commerce company on the globe (by gross merchandise value). Alibaba’s success, Li

argues, lay in its capacity to experiment with and diversify its business models, from

drop- shipping to consumer- to- consumer options. For Shoppist’s founder, Alibaba and

other Chinese giants like Jingdong (JD.com) had something valuable to teach Africa’s

ﬂedgling e- commerce sector: reaching the customers who were usually left out from

online shopping platforms was not just about marketing prowess, but also about last-

mile logistics, both ﬁnancially and geographically.

However, as Li explains, ‘e- commerce in China is very unique.… In Kenya, we

[could] not just copy Alibaba or Amazon. And so that’s why it was important to have

local intelligence’. Local intelligence, as he later elaborates, meant more than hiring

a team of locals for the operations team. It entailed relying on their understanding of

the East African market, on their knowledge of speciﬁc urban economies of logistical

distribution (in this case, the boda boda network), as well as on an iterative, cumulative

process of data- powered learning.

From the very beginning, these experimental processes translated into a series

of small adaptations and tweaks to more traditional e- commerce models. The ﬁrst of

these changes was the integration of M- Pesa,14 Kenya’s mobile currency system. This was

no small feat, even though M- Pesa has an application programming interface (API) for

embedding the mobile currency as an online payment option. The reason other online

retailers of foreign goods only allow purchases by credit card, Li speculates, is because

of the currency risk. But with M- Pesa, Shoppist became one of the ﬁrst online retailers

where Kenyans could buy aordable phones made in China in the same way they used

to do in a physical shop: with a one- o, fully paid, cash- like transaction.

The other change in the business model stemmed from the intuition that

Shoppist did not need to function completely as either a drop- shipping company (i.e.

with no warehouse) or a stockist (i.e. with large- scale warehousing facilities). Shoppist’s

hybrid model is based on a central warehouse located close to Nairobi’s international

airport (in an area that is becoming more and more densely packed with this kind of

logistical facility) and pop- up warehouses in Mombasa and Kisumu that it can use

during peak- order periods (such as Christmas time). A three- tiered product taxonomy

thus ensues: things that are already in the warehouse (where they never stay for more

than a month); things that are sold by other stockists in Kenya (and are in third- party

warehouses); and things ordered directly from China (which therefore only stay brieﬂy

in the Nairobi warehouse before being dispatched).

For this carefully orchestrated supply model, Li argues, coordinating the

last mile is as important as understanding the suppliers. In fact, understanding the

supplier side depends on last- mile data and coordination. For goods to move quickly

to their end customers, two motorcycle- enabled solutions are crafted to resolve two

addressability issues. The ﬁrst is the fact that Nairobi addresses may or may not be

accurately geolocated, or even have a speciﬁc house number

—

boda riders know the

city well enough to ﬁll this gap. The second is the total lack of an address, because

some Shoppist customers live in parts of the city that for one reason or another are

14 This local mobile money platform was originally developed by telecom operator Safaricom. See Guma and

Mwaura(2021) for an infrastructural perspective on last- mile mobile telephony configurations in Kenya.

POLLIO, CIROLIA AND ODEO 12

unaddressable. In this case, the last- mile solution consists of little delivery hubs called

Shopposts. Deliveries to these hubs are fulﬁlled daily by boda riders and they have the

double function of providing a delivery address for people without one, as well as for

those customers who cannot receive deliveries at home during working hours. As Li

explains, Shoppist customers are mostly young people who do not belong to the upper-

income brackets of Kenyan society; consequently, costlier delivery options on Amazon or

other e- commerce platforms are simply prohibitive. The Shoppost option is essential for

this type of customer because they are less addressable than middle- class households.

While Shoppist employs a small number of boda riders directly, the majority

of them are subcontracted through business- to- business (B2B) last- mile logistical

platforms. The reason for this choice is that specialized two- wheel delivery companies

have developed more ecient distribution algorithms; as we will see in the next case

study, they are better at optimizing delivery routes across urban centres, and their

riders have much better performance indicators than could be achieved with in- house

operations. At the same time, keeping a few riders on the payroll means that Shoppist

also has a degree of flexibility and can adapt to unexpected market fluctuations,

particularly in Nairobi. Such a system, Li concludes, did not happen just by chance, but

evolved organically using the last mile itself as a data source, constantly monitoring and

experimenting with feedback loops to improve the ﬁnal leg of logistical coordination,

while ensuring that small tweaks to the dispatch system would not aect the aordability

of Shoppist’s e- commerce oering.

These are small but crucial aspects of Shoppist’s business model which speak

to the ways in which platform algorithms seek to mend disjointed urban fabrics. First,

Shoppist visualizes the goal of selling aordable products to its target customers (the

urban lower- middle class and youths) as a problem of addressability. Second, creating

an e- commerce market from scratch requires incremental and iterative data- driven

experiments. This is very much in line with what has been observed about large platform

companies, which experiment with the city as a testbed for new products and services

(Mattern,2016). What the example of Shoppist also shows us, however, is how these

experiments are framed as potential solutions to logistical problems that are unique to

a city like Nairobi and to which boda boda networks present a gap- ﬁlling opportunity

—

whether by knowing intimately the complicated urban fabric of a booming postcolonial

city or by buering against the ﬂuctuations in demand for last- mile services. Once again,

as with Cleanify, the lower- middle class represents a key driver of these new economies

of algorithmic suturing. Yet as with Cleanify’s business model too, we catch sight of how

these new companies speculate on new urban spaces (such as ghost warehouses and

distributed delivery hubs) as the future interfaces through which algorithmic economies

will latch onto the city.

— A last- mile crouching tiger

To ﬁnd one of the specialized last- mile companies that support e- commerce

platforms like Shoppist, one doesn’t need to travel very far from the headquarters of

the latter. At the southern end of Mombasa road, Nairobi’s main trac artery, stands

a growing cluster of depots and other logistics operations, attracted by the proximity

of Kenya’s main international airport, the terminus of the newly built standard-

gauge railway, and access to the country’s busiest highway. At the crossroads of these

networked systems, Dasher occupies two large warehouses painted in bright colours,

one being the distribution centre and the other a sorting facility.

Dasher is one of an increasing number of small last- mile companies that oer

logistics services through a network of boda riders across Kenya. Unlike bigger platforms

like Uber and Bolt, Dasher only operates in the B2B market, oering a plug- and- play

delivery service to e- commerce businesses. This is explained to us by Jenny, a minute,

animated young woman who is head of sales for the company, which was started by

13ALGORITHMIC SUTURING

a small team of Chinese expatriates in late 2020. Kenya was not their first market,

however. Dasher is the subsidiary of a conglomerate already active in half a dozen

African countries, as well as, more recently, in the Middle East. These subsidiaries all

report to headquarters in Shanghai but, as Jenny elaborates, they only do so once a

year, and Dasher is an independent business unit operating as a startup. The investment

came from one of China’s leading express delivery companies, a logistics firm that

made its fortune on the heels of e- commerce giants like Alibaba and JD.com and which

now handles a staggering volume of more than ten million parcels a day. Its expansion

into the African market replicates the model that the company used in China, with a

growing cluster of subsidiary startups that gradually formed a comprehensive network

of logistical services, from line- haul ﬁrst- mile to express last- mile solutions.

In Kenya, Dasher is spearheaded by Kevin, a tall young man who cut his teeth as

a logistics manager in southern China during the heyday of the e- commerce boom, ten

years prior to relocating to Nairobi. Sitting with Jenny in a small oce separated from

the sorting facility by a glazed wall he maps out Dasher’s network for us. At the centre

of the network stands the warehouse complex in which they welcomed us. Parcels

coming in from overseas, for example, are scanned in the sorting facility. This is a

manual operation but it is augmented by technological equipment imported from China

and by in- house software that Dasher built itself to automate its logistics management.

In the future, Kevin plans to import a fully automated sorting belt, but so far the volume

of parcels does not warrant a large- scale machine. From the sorting facility, items move

across to the distribution centre. Here, parcels are assigned to variously sized trucks and

vans which head out to smaller distribution hubs across the city and in most of Kenya’s

other counties.15 These are either small shop fronts with a loading bay or, in some cases,

other businesses acting as franchisees. From these hubs, boda riders bridge the last- mile

gap, delivering parcels to end customers on behalf of Dasher’s B2B clients. These hubs

are also drop- o points for domestic deliveries: once an item is scanned in at one of

these hubs, it travels to the main distribution centre in Nairobi from where it is line-

hauled to the destination hub and eventually delivered by local boda riders. This

centralized system responds to the need to optimize the interface between the middle

and the last mile, Jenny explains, pointing to the second warehouse building:

Everything goes through this DC [distribution centre]. So, for example, between

Meru and Mombasa trucks would be half full. But because they all go through

this DC, we make sure that all the routes are optimized … In logistics, the most

important thing is the network; the more a network is capillary, the bigger your

abilities.

The ultimate capillaries of this network are indeed the boda riders, she goes on

to explain, showing us the algorithmic system that renders each rider and each parcel

a discrete data point thanks to the barcodes that are scanned at key points of a delivery.

Unlike other large on- demand labour platforms oering parcel services, Uber

included, Dasher does not rely on a casual workforce. The riders are all formally

employed, although their wages are piece- based. Only during peak periods such as

Black Friday are temporary riders added to the base team, which, after just one year of

operation, already comprises some 200 members (ranging from 75 in Nairobi itself to

just a couple in smaller cities). While casual labour would be a cheaper option, Dasher

decided to invest in training couriers so as to baseline their customer service quality and

make sure their riders are indeed data collectors as well as couriers. Although neither

Jenny nor Kevin mention it explicitly, and only refer to a ‘politeness’ problem, it is

15 Since the constitutional reform of 2010, Kenya has been divided into 47 counties which vary greatly in both size

and population.

POLLIO, CIROLIA AND ODEO 14

clear that the decision to onboard riders as employees is a way to address the negative

perceptions which surround the boda boda sector in Kenya.

In many respects, Dasher is not unique in its business model or in the way that

it formalizes boda riders as direct employees. Research into platforms has shown time

and again that on- demand work can make labour less rather than more precarious,

but not necessarily dierent or better (Schor et al.,2020). Nonetheless, by examining

the ways in which Dasher visualizes its last- mile network we catch a glimpse of how

business models envision the future of urban mobility infrastructures. Both Kevin and

Jenny explain that Dasher’s aim is to become Africa’s ‘number one last- mile service

provider’ dedicated to e- commerce. This is something of a pipedream, as they admit,

since e- commerce in Africa is still only a very small market. But they are playing the

‘long game’, says Kevin, arguing that it only takes two ingredients for e- commerce to

boom: a functional, diused online payment system and eective last- mile logistics:

If anyone can solve these two things, online payment systems and last- mile

logistics, e- commerce will be pumping … No country can escape e- commerce.

I don’t know who will be the last winner. In America it’s Amazon, in China it’s

Alibaba, and in different countries they have their own. I don’t know who will be

the winner in Africa. But whoever it is, they’ll need us.

So why did we come here? At this phase, we are building the night work. We’ve

connected every county, trained our team, taught them how to be ready. We are

waiting like a crouching tiger.

In playing this long game16 while African e- commerce develops, Kevin is already

working on the distributed infrastructure that a boom in the demand for last- mile

delivery will mean for a city like Nairobi. He has ordered twenty smart cabinets from

China. These cabinets, which will be distributed across the city to shopping malls and

large corporate oces, will function as the last- mile endpoints to Dasher’s delivery

services. Enabled with Internet- of- Things (IOT) technology and fully automated, the

cabinets sport the same bright colours as the company’s brand and are equipped with

small lock- boxes. Every day, boda riders will ﬁll and take from these cabinets across the

city

—

addressing the same issue of addressability that Shoppist is trying to solve with its

Shoppost model.

The prototype of one of these smart cabinets, soon to become a ﬁxture of urban

life in Nairobi, sits just outside the oce where Jenny and Kevin are talking to us. In

many respects, Dasher makes some of the same issues that we have already seen in

the two previous case studies legible; once again, as problems of last- mile algorithm

coordination. Motorcycles, in Dasher’s business model, contribute to a unique hub-

and- spoke logistics model in which the gap- ﬁlling capacities of the boda boda sector are

visualized as both capillaries of a larger distribution network and the data- gathering

terminals of this network. In other words, algorithmic suturing is always more than

just software- based ‘tricks’ (Pasquinelli,2019: 6): it entails riders, motorcycles, cabinets,

warehouses, and so much besides. What is also worth noting

—

which Dasher illustrates

even more than the other two cases

—

is the speculative nature of these platforms; not

only does Dasher currently operate at a loss, waiting for an e- commerce boom that may

or may not materialize, but it does so by speculating on a spatial network of distribution

that is currently in the making but not there yet. As we move now to the conclusion

16 In a way, this confirms and extends C.K. Lee’s(2017) argument that corporate China in Africa is often involved in

practices of profit optimization rather than profit maximization. While Lee’s point is specifically about state capital

as opposed to other varieties of capital, our research suggests that Chinese tech startups in Kenya also operate

across diverse temporalities of profitability.

15ALGORITHMIC SUTURING

of this article, we will return to the importance of these future- making algorithmic

practices for urban research.

Conclusion

What can be seen about a city when one starts with the business models of last-

mile startups? How do they see, make sense of and make visible the ends of splintered

urban infrastructures and economies? Of course, these are complex questions that can

be cut and sliced in many ways (Qadri and D’Ignazio,2022). Owing to the ubiquity of the

motorcycle in the provision of last- mile logistics in African cities, we have intentionally

selected startups that tap into Nairobi’s (in)famous boda boda sector as our entry point

into this way of seeing and making legible. Boda boda, as we have shown, operate as

a prosthetic tie that helps suture the fragmented urban infrastructures of Nairobi.

Increasingly, they do so through digital platforms, and through processes that we have

labelled using the concept of ‘algorithmic suturing’: the more- than- spatial patching of

urban fragments through practices of data- driven optimization.

Our contribution with this article is therefore both empirical and conceptual. On

an immediate level, our case studies show how last- mile algorithmic business models

seek to stitch together splintered infrastructures by making them legible as sites of

coordination. More speciﬁcally, what last- mile business models visualize

—

and therefore

seek to address

—

are the gaps in the urban economies that they can tap into. In this

sense, our research speaks to a body of Southern infrastructure and Southern urbanism

scholarship which has shown how in Southern cities networked systems (where they

exist) are gap- ﬁlled and sutured by practices of material improvisation and the marginal

technological survival of the poor. Yet the last- mile examples we have narrated in this

article point to something different: stitching together the loose ends of splintered

urban infrastructure is about more than material improvisation, or individual responses,

or marginal practices of survivability. Algorithmic suturing is, in fact, a deliberate,

elaborate set of experimental business models through which urban fractures become

legible as problems of optimization and, therefore, as sources of value. In other words,

acts of suturing are not just performed by informal economies through make- do thrift,

but by deliberate algorithms that seek to integrate the latter into (perhaps) proﬁtable

data- driven platforms.

Moreover, while there is a tendency (particularly among scholars who have

sought to reconcile top- down readings of infrastructure and platforms with everyday

lived experiences) to assume that suturing happens at the individual level, or at small

collective scales, the last mile is about big numbers: it is about producing algorithmic

stitches that do not address single splinters of urbanity but enrol whole interfaces

between inconsistent economies, using the ends of these networks

—

the last mile

—

as a

constant data source to recursively coordinate better and optimize more. Put dierently,

the ‘infrastructural heterogeneity’ often attributed to Southern cities (Lawhon et

al.,2018) is also the calculated outcome of highly scalable business models. Uber and

the like know this very well, and to leave this insight with them represents a missed

opportunity for urban research on digital infrastructure, both in the African city and

beyond.

Seeing like a last mile is also a methodological intervention. Like any method, as

John Law reminds us(2004), our approach makes some things legible while others fall

into the background. We have mentioned, for example, how the exploitation of platform

labour (Van Doorn,2017) is not the focus of this article. At the same time, our perspective

inductively calls attention to important undercurrents sweeping across African cities.

The cases we have outlined hint at some of these issues, which we cannot address in

full here. However, it is worth highlighting a few of the issues which merit a much

richer urban inquiry. The last mile sees, for instance, the possibilities that lie within the

needs and aspirations of the lower rungs of Africa’s urban middle class, as well as the

POLLIO, CIROLIA AND ODEO 16

inescapably tight margins of this demographic as drivers of urban change. The last mile

also reveals the impressive strides made by Chinese tech and e- commerce companies,

so often overlooked by scholars concerned with China’s large- scale and state- driven

investments (Pollio,2022b). Finally, these business models also foreground the quiet

emergence of new spaces of urban logistics such as dark kitchens, dark laundromats

and ghost warehouses (Shapiro,2022), or even more mundane urban ‘objects’ like

smart cabinets. Last- mile startups envision and speculate on these spaces and objects

before they are materialized, when they are still being considered, planned and tested.

Algorithmic suturing, we have shown, is speculatively experimental.

On a more pragmatic level, the last mile sheds light on the diverse forms of value

promised by these platformed urban economies and the various shades of invisible

work and knowledge that sustain them (Irani,2015). There is a need, we believe, to

bring the perspective represented in this article into scholarly research that centres

questions of labour, recognizing that labour dynamics are also and fundamentally

shaped by contextual practices of algorithmic suturing. Without a fuller appreciation of

this nexus, we are already witnessing the emergence of regulations (and the purposeful

lack thereof ) which negatively aect those whom they are meant to protect. As engaged

researchers, we will seek to make these insights speak to regulators and industrial

players through the work that we do beyond academic publishing. In the meantime, our

hope is that scholars of cities and infrastructure will take up the research challenges that

the last mile makes visible, some of which we have outlined in this article.

Andrea Pollio, Department of Urban and Regional Research and Planning (DIST),

Polytechnic of Turin and African Centre for Cities, University of Cape Town,

Rondebosch, 7701, South Africa, andrea.pollio@polito.it

Liza Rose Cirolia, African Centre for Cities, University of Cape Town, Rondebosch,

7701, South Africa, liza.cirolia@gmail.com

Jack Ong’iro Odeo, Department of Human Geography, Stockholm University, SE-

106 91 Stockholm, Sweden, jack.odeo@gmail.com

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August 2023

Andrea Pollio · Liza Cirolia · Jack Ong'iro Odeo

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Full-text available

Feb 2023

In the last decade, research on southern cities has fruitfully contributed to infrastructure debates and theorisations. A critical thread challenges the 'networked infrastructure ideal' and celebrates the diverse ways people secure access to services in the absence of uniform and centrally provided delivery systems. This chapter reflects on the value and limitations of this emerging scholarship, focusing on what is referred to as 'hybrid' or 'heterogenous' infrastructure configurations. One of the limitations, we argue, is that infrastructural hybridity is often conflated with survivability, marginality, or informality. Particularly in the study of African cities, it focusses on small scale, make-do tactics, devoid of state involvement. Attending to this narrow focus, we identify 'spectrums' of hybridity. We use illustrations from across the continent to substantiate this framework and broaden its scope. Finally, deploying this spectral notion of hybridity, we ask: what might this conceptual toolkit mean for the engaged scholar?

Matatu: A History of Popular Transportation in Nairobi

Book

Jan 2017

Kenda Mutongi

The Relevance of Algorithms

Chapter

Jan 2014

Tarleton Gillespie

Scholars from communication and media studies join those from science and technology studies to examine media technologies as complex, sociomaterial phenomena. In recent years, scholarship around media technologies has finally shed the assumption that these technologies are separate from and powerfully determining of social life, looking at them instead as produced by and embedded in distinct social, cultural, and political practices. Communication and media scholars have increasingly taken theoretical perspectives originating in science and technology studies (STS), while some STS scholars interested in information technologies have linked their research to media studies inquiries into the symbolic dimensions of these tools. In this volume, scholars from both fields come together to advance this view of media technologies as complex sociomaterial phenomena. The contributors first address the relationship between materiality and mediation, considering such topics as the lived realities of network infrastructure. The contributors then highlight media technologies as always in motion, held together through the minute, unobserved work of many, including efforts to keep these technologies alive. Contributors Pablo J. Boczkowski, Geoffrey C. Bowker, Finn Brunton, Gabriella Coleman, Gregory J. Downey, Kirsten A. Foot, Tarleton Gillespie, Steven J. Jackson, Christopher M. Kelty, Leah A. Lievrouw, Sonia Livingstone, Ignacio Siles, Jonathan Sterne, Lucy Suchman, Fred Turner

Ring roads, revived plans, & plotted practice: The multiple makings of Nairobi’s urban periphery

Article

Dec 2023
HABITAT INT

Hope and Hype in Africa’s Digital Economy: The Rise of Innovation Hubs

Chapter

Feb 2019

Nicolas Friederici

Investigations of what increasing digital connectivity and the digitalization of the economy mean for people and places at the world's economic margins. Within the last decade, more than one billion people became new Internet users. Once, digital connectivity was confined to economically prosperous parts of the world; now Internet users make up a majority of the world's population. In this book, contributors from a range of disciplines and locations investigate the impact of increased digital connectivity on people and places at the world's economic margins. Does the advent of a digitalized economy mean that those in economic peripheries can transcend spatial, organizational, social, and political constraints—or do digital tools and techniques tend to reinforce existing inequalities? The contributors present a diverse set of case studies, reporting on digitalization in countries ranging from Chile to Kenya to the Philippines, and develop a broad range of theoretical positions. They consider, among other things, data-driven disintermediation, women's economic empowerment and gendered power relations, digital humanitarianism and philanthropic capitalism, the spread of innovation hubs, and two cases of the reversal of core and periphery in digital innovation. ContributorsNiels Beerepoot, Ryan Burns, Jenna Burrell, Julie Yujie Chen, Peter Dannenberg, Uwe Deichmann, Jonathan Donner, Christopher Foster, Mark Graham, Nicolas Friederici, Hernan Galperin, Catrihel Greppi, Anita Gurumurthy, Isis Hjorth, Lilly Irani, Molly Jackman, Calestous Juma, Dorothea Kleine, Madlen Krone, Vili Lehdonvirta, Chris Locke, Silvia Masiero, Hannah McCarrick,Deepak K. Mishra, Bitange Ndemo, Jorien Oprins, Elisa Oreglia, Stefan Ouma, Robert Pepper, Jack Linchuan Qiu, Julian Stenmanns, Tim Unwin, Julia Verne, Timothy Waema

Owners of the Map: Motorcycle Taxi Drivers, Mobility, and Politics in Bangkok

Book

Mar 2019

Claudio Sopranzetti

Disrupted Urbanism: Situated Smart Initiatives in African Cities

Book

Jan 2023

Nancy Odendaal

Seeing like a driver: How workers repair, resist, and reinforce the platform's algorithmic visions

Article

Jul 2022

jats:p> This article theorizes the relationship between two ways of “seeing” and organizing urban mobility markets: the abstract, algorithmic vision of the mobility platform and the experiential, relational vision of the platform driver. Using the case of mobility platforms in Jakarta, we empirically demonstrate how drivers experience the limitations of the platform's visions and how they deploy their own alternative visions of work and the city. We offer this drivers’ “View from Within” as a counterpoint to the visions of the platform, decentering the platform's visions as the sole arbiter of change and optimization in the city. At the same time, we disrupt the assumed binary between these views, showing how they exist in a complex dance of complementarity and contestation. We conclude with a discussion on the opportunities this entanglement presents for worker agency in the algorithmic market, the hurdles toward more “worker centered design” in platform economies and the tensions between globalizing technological solutions and their localized instantiations. Through this article, we argue for seeing deep, embedded relationships as culturally and historically important modes of urban life which technology has to interact with but cannot fully capture nor do away with. </jats:p

Algorithmic suturing: platforms, motorcycles and the "last mile" in urban Africa

Abstract

Supplementary resource (1)

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