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Delivering Urban Data Justice for “Smart Cities 2.0”

11 February 2021 Leave a comment

What new institutions are needed to ensure smart cities are also data-just cities?

Smart City 1.0 “is primarily focused on diffusing smart technologies for corporate and economic interests”.  Smart City 2.0 is “a decentralised, people-centric approach where smart technologies are employed as tools to tackle social problems, address resident needs and foster collaborative participation”.[1]

Given their people-centrism, a foundation for Smart Cities 2.0 must therefore be delivery of urban data justice: fairness in the way people are made visible, represented and treated as a result of the production of urban digital data.[2]

We already know the constituent parts of urban data justice, as shown in the figure below.[3]

But a key argument of this model is that data justice is significantly shaped by urban social structures.  If those structures are unjust then data practices and outcomes will likely be unjust.  How, then, do we create urban social structures more likely to deliver the data justice that is part of Smart City 2.0?

Setting aside more radical restructuring of the urban polity, three more incremental forms can play a role:

1. Living Labs

“Living labs employ a user-focused design environment, a strategy of co-creation, and, increasingly, an institutionalized space wherein citizens, administrators, entrepreneurs and academics come together to develop smartness into concrete applications. They help identify and join localized expertise, real-life testing and prototyping with strategic networking of resources to address challenges that cannot be solved by single cities or departments.”[4]  Located at the upstream end of the innovation cycle, living labs are well-placed to come up with new, just ways of applying urban data.[5]

2. Urban Data Trusts

Data trusts are “a legal structure that provides independent stewardship of data … an approach to looking after and making decisions about data in a similar way that trusts have been used to look after and make decisions about other forms of asset in the past, such as land trusts that steward land on behalf of local communities.”[6]  These can form an institutional superstructure to ensure justice in the ownership, sharing and use of data; particularly data gathered about urban citizens.[7]

3. Community Data Intermediaries

Community data intermediaries are “organizations that gather data relevant for neighborhood-level analysis and make the information available to community groups and local institutions”.  Alongside their key role in gathering data – for example via community mapping – CDIs may also have features of both living labs (innovating application of that data) and data trusts (acting as stewards of the data for communities).[8]

The devil here will be in the detail: how exactly are these entities structured and run?  Simply attaching a label to an organisation does not make it just, with critiques in circulation of living labs[9], urban data trusts[10], and community data intermediaries[11].  Nonetheless, it is these types of urban institutional innovation that will underlie delivery of data justice in Smart Cities 2.0.  I look forward to further examples of these and similar innovations.

 

[1] Trencher, G. (2019) Towards the smart city 2.0: empirical evidence of using smartness as a tool for tackling social challenges, Technological Forecasting and Social Change, 142, 117-128

[2] Adapted slightly from Taylor, L. (2017) What is data justice? The case for connecting digital rights and freedoms globally, Big Data & Society, 4(2), 2053951717736335

[3] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

[4] Baykurt, B. (2020) Are “smart” cities living up to the hype?, University of Massachusetts Amherst News, 1 May

[5] For a data justice perspective on the activities of one Living Lab in Kathmandu plus related organisations, see: Mulder, F. (2020) Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal, Journal of Contingencies and Crisis Management, 28(4), 432-445

[6] Hardinges, J. (2020) Data trusts in 2020, Open Data Institute, 17 Mar

[7] For more on urban civic data trusts, see: Kariotis, T. (2020) Civic Data Trusts, Melbourne School of Government, University of Melbourne, Australia

[8] For a guide on creating community data intermedaries and examples, see: Hendey, L., Cowan, J., Kingsley, G.T. & Pettit, K.L. (2016) NNIP’s Guide to Starting a Local Data Intermediary, NNIP, Washington, DC

[9] Taylor, L. (2020) Exploitation as innovation: research ethics and the governance of experimentation in the urban living lab. Regional Studies, advance online publication.

[10] Artyushina, A. (2020) Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto, Telematics and Informatics, 55, 101456

[11] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

Latest Digital Development Outputs (Agriculture, Data, Social Media) from CDD, Manchester

18 November 2020 Leave a comment

Recent outputs – on Agricultural Platforms; Data-for-Development; Social Media and Education – from the Centre for Digital Development, University of Manchester:

AGRICULTURAL PLATFORMS:

Ag-Platforms in East Africa: National and Regional Policy Gaps” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag identifies national and regional governance deficits (gaps) in the diffusion of digital agricultural platforms, and consequently how Ag-platforms bridge national and regional policy gaps.

Platforms in Agricultural Value Chains: Emergence of New Business Models” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag explains the various models of digital agricultural platforms that exist, and provides policy-makers with a roadmap that supports the proliferation of sustainable Ag-platforms.

DATA-FOR-DEVELOPMENT:

Datafication, Value and Power in Developing Countries” by Richard Heeks, Vanya Rakesh, Ritam Sengupta, Sumandro Chattapadhyay & Christopher Foster analyses the implementation challenges and impact of big data on organisational value, sources of power, and wider politics.

Identifying Potential Positive Deviants Across Rice-Producing Areas in Indonesia: An Application of Big Data Analytics and Approaches” (open access) by Basma Albanna, Dharani Dhar Burra & Michael Dyer uses remote sensing and survey data to identify “positive deviant” rice-farming villages in Indonesia: those which outperform their peers in agricultural productivity.

The Urban Data Justice Case Study Collection” (open access) presents ten case studies analysing new urban data in Latin America, Africa and Asia from data justice/rights perspectives.  It also outlines a future research agenda on urban data justice in the global South.

SOCIAL MEDIA AND EDUCATIONAL DEVELOPMENT

WhatsApp-Supported Language Teacher Development: A Case Study in the Zataari Refugee Camp” (open access) by Gary Motteram, Susan Dawson & Nazmi Al-Masri through a thematic analysis of WhatsApp exchanges, explores how Syrian English Language teachers working in refugee camps in Jordan work collaboratively on teacher development.

Positive Deviance: A Data-Powered Approach to the Covid-19 Response

Nations around the world are struggling with their response to the Covid-19 pandemic.  In particular, they seek guidance on what works best in terms of preventive measures, treatments, and public health, economic and other policies.  Can we use the novel approach of data-powered positive deviance to improve the guidance being offered?

Positive Deviance and Covid-19

Positive deviants are those in a population that significantly outperform their peers.  While the terminology of positive deviance is absent from public discourse on Covid-19, the concept is implicitly present at least at the level of nations.  In an evolving list, countries like New Zealand, Australia, Taiwan, South Korea and Germany regularly appear among those seen as most “successful” in terms of their relative infection or death rates so far.

Here we argue first that the ideas and techniques of positive deviance could usefully be called on more directly; second that application of PD is probably more useful at levels other than the nation-state.  In the table below, we summarise four levels at which PD could be applied, giving potential examples and also potential explanators: the factors that underpin the outperformance of positive deviants.

Level Potential positive deviants Potential PD explanators
Nation[i] Countries with very low relative infection or death rates
  • Early lockdown
  • Extensive testing
  • Use of contact-tracing incl. apps
  • Cultural acceptance of mask-wearing
  • Prior mandatory TB vaccination
  • Quality of leadership
Locality (Regions, Cities)[ii] Cities and regions with significantly slower spread of Covid-19 infection than peers
  • Extensive or innovative community education campaigns
  • Testing well in excess of national levels
  • Earlier-than-national lockdown
  • Extensive sanitisation of public transport
  • Quality and breadth of local healthcare
  • Quality of leadership
Facility (Hospitals, Health Centres)[iii] Health facilities with significantly higher recovery rates than peers
  • Innovative use of existing (scarce) healthcare technologies / materials
  • Innovative use of new healthcare technologies: AI, new treatments
  • Level of medical staff expertise and Covid-19-specific training
Health facilities with significantly lower staff infection rates than peers
  • Provision of high-quality personal protective equipment in sufficient quantity
  • Strict adherence to infection monitoring and control measures
  • Strict adherence to high-quality disinfection procedures
  • Innovative use of contact-free healthcare technologies: chat bots, robots, interactive voice response, etc
Individual[iv] Individuals in vulnerable groups who contract full-blown Covid-19 and survive
  • Psychological resilience
  • Physical fitness
  • Absence of underlying health conditions
  • Effective therapies
  • Genetics

 

At present, items in the table are hypothetical and/or illustrative but they show the significant value that could be derived from identification of positive deviants and their explanators.  Those explanators that are under social control – such as use of technological solutions or policy/managerial measures – can be rapidly scaled across populations.  Those explanators such as genetics or pre-existing levels of healthcare capacity which are not under social control can be built into policy responses; for example in customising responses to particular groups or locations.

Evidence from positive deviance analysis can help currently in designing policies and specific interventions to help stem infection and death rates.  Soon it will be able to help design more-effective lockdown exit strategies as these start to show differential results, and as post-lockdown positive deviants start to appear.

However, positive deviance consists of two elements; not just outperformance but outperformance of peers.  It is the “peers” element that confounds the value of positive deviance at the nation-state level.

Public discourse has focused mainly on supposedly outperforming nations [v]; yet countries are complex systems that make meaningful comparisons very difficult[vi]: dataset definitions are different (e.g. how countries count deaths); dataset accuracy is different (with some countries suspected of artificially suppressing death rates from Covid-19); population profiles and densities are different (countries with young, rural populations differing from those with old, urban populations); climates are different (which may or may not have an impact); health service capacities are different; pre-existing health condition profiles are different; testing methods are different; and so on.  Within all this, there is a great danger of apophenia: the mistaken identification of “patterns” in the data that are either not actually present or which are just random.

More valid and hence more useful will be application of positive deviance at lower levels.  Indeed, the lower the level, the more feasible it becomes to identify and control for dimensions of difference and to then cluster data into true peer groups within which positive deviants – and perhaps also some of their explanators – can then be identified.

Data-Powered Positive Deviance and Covid-19

The traditional approach to identifying positive deviants has been the field survey: going out into human populations (positive deviants have historically been understood only as individuals or families) and asking questions of hundreds or thousands of respondents.  Not only was this time-consuming and costly but it also becomes more risky or more difficult or even impractical during a pandemic.

Much better, then, is to look at analysis of large-scale datasets which may be big data[vii] and/or open data, since this offers many potential benefits compared to the traditional approach[viii].  Many such datasets already exist online[ix], while others may be accessed as they are created by national statistical or public health authorities.

Analytical techniques, such as those being developed by the Data-Powered Positive Deviance project, can then be applied: clustering the data into peer groups, defining the level of outperformance needed to be classified as a positive deviant, identifying the positive deviants, then interrogating the dataset further to see if any PD explanators can be extracted from it.

An example already underway is clustering the 368 districts in Germany based on data from the country’s Landatlas dataset and identifying those which are outperforming in terms of spread of the virus.  Retrospective regression analysis is already suggesting structural factors that may be of importance in positive deviant districts: extent and nature of health infrastructure including family doctors and pharmacies, population density, and levels of higher education and of unemployment.

This can then be complemented in two directions – diving deeper into the data via machine learning to try to predict future spread of the disease; and complementing this large-scale open data with “thick data” using online survey and other methods to identify the non-structural factors that may underlie outperformance.  The latter particularly will look for factors under socio-political control such as policies on lockdown, testing, etc.

Of course, great care must be taken here.  Even setting aside deliberate under-reporting, accuracy of the most basic measures – cases of, and deaths from Covid-19 – has some inherent uncertainties[x].  Beyond accuracy are the broader issues of “data justice”[xi] as it applies to Covid-19-related analysis[xii], including:

  • Representation: the issue of who is and is not represented on datasets. Poorer countries, poorer populations, ethnic minority populations are often under-represented.  If not accounted for, data analysis may not only be inaccurate but also unjust.
  • Privacy: arguments about the benefits of analysing data are being used to push out the boundaries of what is seen as acceptable data privacy; opening the possibility of greater state surveillance of populations. As Privacy International notes, any boundary-pushing “must be temporary, necessary, and proportionate”[xiii].
  • Access and Ownership: best practice would seem to be datasets that are publicly-owned and open-access with analysis that is transparently explained. The danger is that private interests seek to sequester the value of Covid-19-related data or its analysis.
  • Inequality: the key systems of relevance to any Covid-19 response are the economic and public health systems. These contain structural inequalities that benefit some more than others.  Unless data-driven responses take this into account, those responses may further exacerbate existing social fracture lines.

However, if these challenges can be navigated, then the potential of data-powered positive deviance can be effectively harnessed in the fight against Covid-19.  By identifying Covid-19 positive deviants, we can spotlight the places, institutions and people who are dealing best with the pandemic.  By identifying PD explanators, we can understand what constitutes best practice in terms of prevention and treatment; from public health to direct healthcare.  By scaling out those PD explanators within peer groups, we can ensure a much-broader application of best practice which should reduce infections and save lives.  And using the power of digital datasets and data analytics, we can do this in a cost- and time-effective manner.

The “Data-Powered Positive Deviance” project will be working on this over coming months.  We welcome collaborations with colleagues around the world on this exciting initiative and encourage you to contact the GIZ Data Lab or the Centre for Digital Development (University of Manchester).

This blogpost was co-authored by Richard Heeks and Basma Albanna and was originally published on the Data-Powered Positive Deviance blog.

 

 

[i] https://interestingengineering.com/7-countries-keeping-covid-19-cases-in-check-so-far; https://www.forbes.com/sites/avivahwittenbergcox/2020/04/13/what-do-countries-with-the-best-coronavirus-reponses-have-in-common-women-leaders; https://www.maskssavelives.org/; https://www.bloomberg.com/news/articles/2020-04-02/fewer-coronavirus-deaths-seen-in-countries-that-mandate-tb-vaccine

[ii] https://www.weforum.org/agenda/2020/03/how-should-cities-prepare-for-coronavirus-pandemics/; https://www.wri.org/blog/2020/03/covid-19-could-affect-cities-years-here-are-4-ways-theyre-coping-now; https://www.fox9.com/news/experts-explain-why-minnesota-has-the-nations-lowest-per-capita-covid-19-infection-rate; https://www.bbc.co.uk/news/world-asia-52269607

[iii] https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19; https://www.cuimc.columbia.edu/news/columbia-develops-ventilator-sharing-protocol-covid-19-patients; https://www.esht.nhs.uk/2020/04/02/innovation-and-change-to-manage-covid-19-at-esht/; https://www.med-technews.com/topics/covid-19/; https://www.innovationsinhealthcare.org/covid-19-innovations-in-healthcare-responds/; https://www.cnbc.com/2020/03/23/video-hospital-in-china-where-covid-19-patients-treated-by-robots.html; https://www.researchprofessionalnews.com/rr-news-new-zealand-2020-4-high-quality-ppe-crucial-for-at-risk-healthcare-workers/; https://www.ecdc.europa.eu/sites/default/files/documents/Environmental-persistence-of-SARS_CoV_2-virus-Options-for-cleaning2020-03-26_0.pdf

[iv] https://www.sacbee.com/news/coronavirus/article241687336.html; https://www.thelocal.it/20200327/italian-101-year-old-leaves-hospital-after-recovering-from-coronavirus; https://www.vox.com/science-and-health/2020/4/8/21207269/covid-19-coronavirus-risk-factors; https://www.medrxiv.org/content/10.1101/2020.04.22.20072124v2; https://www.bloomberg.com/news/articles/2020-04-16/your-risk-of-getting-sick-from-covid-19-may-lie-in-your-genes

[v] Specifically, this refers to the positive discourse.  There is a significant “negative deviant” discourse (albeit, again, not using this specific terminology) that looks especially at countries and individuals which are under-performing the norm.

[vi] https://www.bbc.co.uk/news/52311014; https://www.theguardian.com/world/2020/apr/24/is-comparing-covid-19-death-rates-across-europe-helpful-

[vii] https://www.forbes.com/sites/ciocentral/2020/03/30/big-data-in-the-time-of-coronavirus-covid-19; https://healthitanalytics.com/news/understanding-the-covid-19-pandemic-as-a-big-data-analytics-issue

[viii] https://doi.org/10.1002/isd2.12063

[ix] E.g. via https://datasetsearch.research.google.com/search?query=coronavirus%20covid-19

[x] https://www.medicalnewstoday.com/articles/why-are-covid-19-death-rates-so-hard-to-calculate-experts-weigh-in; https://www.newsletter.co.uk/health/coronavirus/coronavirus-world-health-organisation-accepts-difficulties-teasing-out-true-death-rates-covid-19-2527689

[xi] https://doi.org/10.1080/1369118X.2019.1599039

[xii] https://www.opendemocracy.net/en/openmovements/widening-data-divide-covid-19-and-global-south/; https://www.wired.com/story/big-data-could-undermine-the-covid-19-response/; https://www.thenewhumanitarian.org/opinion/2020/03/30/coronavirus-apps-technology; https://botpopuli.net/covid19-coronavirus-technology-rights

[xiii] https://privacyinternational.org/examples/tracking-global-response-covid-19; see also https://globalprivacyassembly.org/covid19/

An Applied Data Justice Framework for Datafication and Development

Data is playing an ever-growing role in international development.  But what lens can we use to analyse the impact of data on development?

The emerging field of “data justice” offers some valuable ideas but they have not yet been put together into a systematic and comprehensive framework.  My open-access paper – Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework, written with Satyarupa Shekhar – provides such a framework, as shown below.

The framework exposes five dimensions of data justice:

  • Procedural: fairness in the way in which data is handled.
  • Instrumental: fairness in the results of data being used.
  • Rights-based: adherence to basic data rights such as representation, privacy, access and ownership.
  • Structural: the degree to which the interests and power in wider society support fair outcomes in other forms of data justice.
  • Distributive: an overarching dimension relating to the (in)equality of data-related outcomes that can be applied to each of the other dimensions of data justice.

The dimensions can be used individually; for example, just to analyse data practices, or just to analyse the impact of context on new data systems in developing countries.  Or the model can be used holistically; for example, to understand the full development impact of a particular data initiative.

The Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework paper takes the latter route.  It analyses “pro-equity data initiatives” that were implemented by data activists in four cities: Chennai, Nairobi, Pune and Surakarta.  These initiatives specifically sought to address the data injustices suffered by slum dwellers and other marginalised groups; particularly their invisibility to urban planners and other external agencies.

Using the data justice lens, this research finds that new data flows do have a positive impact in counteracting the injustice of invisibility, but they disproportionately serve those with the motivation and power to use that data.  Results in terms of service improvements and epistemic change are beneficial for slum communities and other marginalised citizens, and these initiatives can be justified on that basis.

However, though there can be no exact calibration from qualitative research, it is likely that these pro-equity initiatives actually increase relative inequalities.  Ordinary community members have seen some benefits but external actors who find the data to match their agenda and capabilities, benefit more.  It is the latter who are more empowered to access, use and control the new data.

If you would like to know more about this research’s findings, framework and recommendations for practice, then take a look at the paper: https://www.tandfonline.com/doi/full/10.1080/1369118X.2019.1599039

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