Description

Geo-spatial and biometric data technologies are the backbone of a datafication of migration – where data about migration and migrants and the ability to analyse that data is driven both by policy narratives and market logics. In urban areas we are seeing an increasing use of geo-data technologies to streamline and optimise service provision and to keep a tap on urban populations – migrants and non-migrants alike. While datafication in relation to border control is predominantly framing migration as risk and as a problem to be solved in the process stripping migrants of access to their rights, cities have traditionally been places where migrants play a crucial part in the local economy and where non-citizen rights are protected.

At this point it is unclear if cities will be able to uphold this role as new data analytics are being incorporated into how cities are run, how urban economies function and how non-citizen residents are counted, categorised and how their movement is controlled within cities by those new systems. There is a growing literature that suggests that data technologies tend to exasperate existing social inequalities. This raises questions about what kind of city the datafied migrant city will be – what will the topographies of digital urban borderlands look like and what do we want them to look like? These are very large questions and much beyond the scope of a Masters thesis but they do point us to various sub-concerns that merit tackling in small case study based projects.

Primarily what is important to understand at this moment is how technologies get embedded in controlling the movement of migrants not only to cities but also in cities and how civil society and people on the move are reacting to those changes?
Some more specific questions to be considered can be focused on two themes:

**Actors behind urban migrant tech:**
• Who are the actors that influence which technologies get used? What are their roles and interests?
• How can migrants’ be part of the technological aspirations of newly planned tech cities?
• How is civil society involved in the datafication of migration in cities?

**Political and economic concerns around geo-data technologies, cities and migration**:
• Where and why can we observe policy changes that encourage the use of geo-data technologies to control the movement of people to and in cities? How are relevant policies changing over time?
• What are the economic and political interests that drive tech implementation in this field?
• What role do logics of optimisation play in the implementation of migrant tech in cities?
• How does solutionism matter in understanding tech and migrant cities? E.g. Which ‘covid-tech’ may be most likely to become migrant tech?

For all those sub-theme questions projects should aim to explore *how and why it matters for understanding the social implications of geospatial data technologies*?

The above are just some possible sub-theme questions. Students are encouraged to propose their own research questions related to the overarching question for this theme: *How geospatial data technologies shape migrant cities*?

For feasibility projects will need to be focused on specific case studies that students should suggest themselves.

Possible case studies might be drawn from Latin American contexts (e.g., Mexico, Chile, Venezuela, Colombia) in the Middle East (e.g., Syria, Iraq, Afghanistan), North and Central Africa regions (e.g., Eritrea, Sudan - possible focus on ‘New Towns’ very welcome), South Asia (e.g., Indonesia), among others.

Objectives and Methodology

The primary objectives of projects in this theme should be to:
- Acquire the ability to critically evaluate geo-data technologies and their social implications
- Contribute to an emerging field of research on critical data studies and migration through specific case studies
- Develop and apply social science research skills and (if appropriate) link those with specific GIS skills (e.g., mapping dataflows or actors to explain current migration information infrastructures).

As this theme allows for different research questions, the methodological approach of the specific project of a student will depend on the precise project question that the student develops during proposal writing. Mixed method approaches are encouraged, especially the creative use of spatial technologies and spatial data in the process of research as well as innovative social science methodologies such as network analytic approaches. It is likely that the analysis of textual data sources (e.g., policy documents, newspaper articles, expert interviews) will be appropriate.

Further reading

Iliadis, Andrew; Russo, Federica (2016): Critical data studies. An introduction. In Big Data & Society 3 (2), 205395171667423. DOI: 10.1177/2053951716674238.

Ajana, Btihaj (2020): Digital Biopolitics. In Emma Cox, Sam Durrant, David Farrier, Lyndsey Stonebridge, Agnes Woolley (Eds.): Refugee imaginaries. Research across the humanities. Edinburgh: Edinburgh University Press, pp. 463–479.

Akbari, Azadeh (2020): Follow the Thing. Data. In Antipode 52 (2), pp. 408–429. DOI: 10.1111/anti.12596.

Amoore, Louise (2020): Cloud ethics. Algorithms and the attributes of ourselves and others. Durham: Duke University Press.

Dencik, Lina; Redden, Joanna; Hintz, Arne; Warne, Harry (2019): The ‘golden view’. Data-driven governance in the scoring society. In Internet Policy Review 8 (2). DOI: 10.14763/2019.2.1413.

D'Ignazio, Catherine; Klein, Lauren F. (2020): Data feminism. Cambridge Massachusetts: The MIT Press (Strong ideas series).

Heeks, Richard; Evans, James; Graham, Mark; Taylor, Linnet (2020): The Urban Data Justice Case Study Collection. Global Development Institute, SEED. Manchester (Digital Development Working Paper Series, 88). Available online at http://www.gdi.manchester.ac.uk/research/publications/di/.

Schäfer, Mirko Tobias; van Es, Karin (2017): The Datafied Society. Studying Culture through Data: Amsterdam University Press.

Scheel, Stephan; Ruppert, Evelyn; Ustek-Spilda, Funda (2019): Enacting migration through data practices. In Environ Plan D 37 (4), pp. 579–588. DOI: 10.1177/0263775819865791.

Taylor, Linnet; Meissner, Fran (2020): A Crisis of Opportunity. Market-Making, Big Data, and the Consolidation of Migration as Risk. In Antipode 52 (1), pp. 270-290. DOI: 10.1111/anti.12583.