An Applied Data Justice Framework: Analysing Datafication and Marginalised Communities in Cities of the Global South

Richard Heeks & Satyarupa Shekhar


Rapid recent growth in the role of data within international development has meant analysis of this phenomenon has been lagging; particularly, analysis of broader impacts of real-world initiatives.  Addressing this gap through a focus on data’s increasing presence in urban development, this paper makes two contributions.  First – drawing from the emerging literature on “data justice” – it presents an explicit, systematic and comprehensive new framework that can be used for analysis of datafication.  Second, it applies the framework to four initiatives in cities of the global South that capture and visualise new data about marginalised communities: residents living in slums and other informal settlements.  Analysing across procedural, rights, instrumental and structural dimensions, it finds these initiatives deliver real incremental gains for their target communities.  But it is external actors and wealthier communities that gain more; thus increasing relative inequality.

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Educators’ guide

Synopsis questions

  1. How has the relation between data and urban development been developing in recent years?  [Section B]
  2. What analytical model(s) does the paper propose?  [Section B]
  3. What research methods did the study use?  [Section C]
  4. What “data injustices” did the focal communities suffer?  [Section C1]
  5. What was the impact of data-related processes in these initiatives?  [Section D1]
  6. What was the impact on data representation and privacy of these initiatives?  [Section D2]
  7. What were the developmental results of these initiatives?  [Section D3]
  8. How were contextual factors impacting on, and impacted by, these initiatives?  [Section D4]
  9. What was the overall impact of these initiatives on inequality?  [Section E1]


Development questions

  1. What other conceptual frameworks could be used to analyse these initiatives?
  2. Which is more important for slum communities: to be visible to external stakeholders, or to be invisible?
  3. Would anything important be missed if the structural data justice perspective was excluded?
  4. Select a different case study of datafication and re-analyse it using the Figure 1 model.
  5. Can data-related projects for marginalised groups ever hope to achieve transformational outcomes?
  6. Imagine you are planning a community mapping initiative in a developing country city.  What lessons would you draw from this paper?