Blended Data Infrastructures and Localisation: Smart Urbanism in the Age of Pandemic

By |2021-08-16T18:01:58+02:00August 11th 2021|Categories: Smart & Digital Development, Urban Health|Tags: , , |

Smart city initiatives have played an increasingly prominent role in urban planning. However, there remains a fundamental gap between data driven urban governance and promoting inclusivity. Jaideep Gupte argues that cities that prioritise citizens over technologies are likely to be the ‘smartest’ and most liveable.

“You may call it “smart”, but it does not work!” exclaimed a local councillor in the Indian city of Kochi when I interviewed her about city plans to provide information and communications technology (ICT)-led and smart government service delivery. What she required was granular data on the challenges residents in her neighbourhood face day in and day out. City-scale and centralised data systems were not responsive enough to help her.

Data production is both a source of power and an instrument for disenfranchisement. This tension is extenuated because urban risks and vulnerabilities collide, are layered, and therefore compound into everyday lived experiences. Over the past two decades, smart city initiatives in low- and middle-income country contexts have played an increasingly prominent role in urban legislation and planning, decision making over land-use, and in making urban service provision more efficient.

While these initiatives invoke a variety of interpretations of ‘smartness’, the dominant rationale for ‘smartening up’ cities has been triggering economic growth. However, there remains a fundamental gap between the types of technological solutions being proposed to enable data driven urban governance, and whether these solutions are promoting inclusivity.

Can Data-Driven Solutions Foster Inclusivity?

Smart city responses to COVID-19, for instance app-based track and trace “solutions”, are a vivid illustration of these tensions and gaps. For example, the Aarogya Setu mHealth app promoted by the government of India, or the NHS Track and Trace app required in the UK, have both encountered increasing unease. The apps are a part of a required public health regime, but they mistakenly assume equality of access to digital infrastructures, which in fact are gendered and unequal.

They assume that the disease is spread through contact resulting from voluntary movement alone and disregard the compulsory nature of the economic and sociocultural relationships that force people to continue living and working in precarious locations despite the risk of contracting or spreading the disease. Track and trace approaches also severely misunderstand the magnitude of the involuntary movement of people often forced by authorities or landlords who, at least in the initial stages of lockdown, continued evictions unabated.

Key responsibilities of social and economic welfare have been transferred onto private sector technology providers, implementers, and corporations accountable to shareholders, and to algorithms accountable to no-one. This has potentially worsened existing inequalities. considering that data ecosystems continue to be susceptible to gendered and socio-political discrimination or that the pandemic has most seriously impacted areas whithout appropriate health systems and basic services so that quality health data are not readily available at a granular enough level to meaningfully zero in on these locations.

What Digital Infrastructures Make Cities Liveable?

With more people living in cities, and as urban residents face increasing health impacts and disruptions to urban systems from pandemics (indeed, some say we are in the “age of the pandemic”), we need a radical rethink of not just how we build, maintain, and govern the physical infrastructure our cities, but also ask the same questions of the digital city. What digital infrastructures will make cities liveable in the age of pandemic and how must they be governed? I offer four ways forward:

(1) Citizen-led data. Urban risk is fluid and complex, and it can change rapidly. Urban residents know these realities best, and ‘citizen-led’ data – data that is generated, owned, and used by citizens to advocate for their own needs – is a particularly efficient way to know urban realities. From a cost-benefit, or ‘value for money’ approach, relying on urban residents, who often already have long standing data gathering practices, to report on their day-to-day urban experiences, rather than on new and expensive sensor-based methods, seems to be an easy win-win for cities.

This can only be actualised if “official” processes recognise citizen-led data as authentic and credible. For example, official data sources often underestimate the burdens of chronic disease among the urban poor. It can be uncomfortable for cities to recognise these structural challenges, but doing so is a critical step towards inclusive cities.

(2) Blended data environments. ‘Data blending’ is the process of combining multiple data sources for a unified objective. For cities, the key questions are when, how and by whom is citizen-led data is layered in with other urban data sources, in particular data that originates from and is demanded by low-resourced or marginalised urban residents. Indeed, best practice shows that urban decision making that is based on formal and informal data architectures is able to respond more effectively to crises. Equally, it is important to maintain analogue data systems to include marginalised communities without access to digital devices, connectivity and digital literacy, or agency in design and management of the urban infrastructure.

Local data ecosystem involves multiple actors with a range of responsibilities and motivations, and institutions, technologies, equipment, and processes with varying degrees of direct representation of at-risk groups. Data architectures that are able to recognise the links between these and the ways these are mediated in (by) the built environment, are most likely to produce liveable cities.

(3) Trust and frugal innovation. City data architectures are smart if they make it easier for us to trust one another. Unfortunately, current urban data infrastructures are deeply siloed, and reward distrust. Technologies like blockchain can help by creating permanent ledgers of data flows and other transactions that can be traced. However, if they are to produce truly inclusive data-reliant decision support systems, these technologies require institutional changes: national data governance standards with local data action plans, and multi-stakeholder data alliances with explicit representation from community groups and civil society. It is necessary to promote innovation practices which are based on principles of openness, diffusion, and shared vision. Importantly, this need not rely solely on ‘frontier technologies’ but also involve ‘frugal’ and more mundane innovations.

(4) Capacity strengthening. If cities are to be liveable, they will require local urban authorities trained to anticipate that all data systems grow incrementally, and therefore require continual capacity strengthening. This must however be based on the core principle that citizens, not cities, should be the creators, architects, and arbiters of technologies.
In sum, cities that prioritise their citizens over the technologies they call upon in planning and delivering services, are likely to be the ‘smartest’ and most liveable.

Jaideep Gupte