Friday, September 21, 2018

Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration

Readers of the blog might know we have an interest in volunteered geographic information, social media and Web 2.0 technologies and how they can be used to explore urban systems. Recently however, we turned our focus on how such information and technologies can be used to explore and understand mass migrations.

To this end we recently had a paper published in the Journal of Geographical Systems entitled "Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration". We adopt the term Exodus 2.0 to refer to this new migration paradigm in the digital age, whereby information is a commodity in the migration process.

Given the nature of migration processes, it is possible to explore them across two key dimensions: geographical and situational. The geographical dimension is associated with the physical migration pathways migrants take from a country of origin to a destination site (often through a number of intermediate “stop” sites). The situational dimension is associated with the social connectivity of moving migrant populations, the conditions on the ground, and the activities that take place as part of migration efforts (including the root conditions, proximate conditions and triggering events).
Factors that potentially cause refugee production and
 mass movement based on identified factors detailed by
Clark (1989) and Zottarelli (1998).
In the paper, we use the ongoing Syrian humanitarian crisis as a case study to to explore how the factors that potentially causes refugee production and mass movement  can be gleamed from new sources of data. Specifically, the potential of crowd-generated data—especially open data, volunteered geographic information and social media content (e.g. OpenStreetMap, Flickr, Twitter and Instagram)  to provide information about migration processes.  Through a series of case studies  we show how such data (when combined with more traditional data sources) offers a new lens to study such the geographical and situational dimensions of mass migration. Finally we discuss  how such data could be used to inform migration modeling. If we have not bored you yet and you are interested in finding out more about this line of inquiry, below we provide the abstract to the paper, some of the figures which go along with our analysis for studying the refugee production and movement. Finally, we also provide the full reference and a link to the paper. 

The exodus of displaced populations is a recurring historical phenomenon, and the ongoing Syrian humanitarian crisis is its latest incarnation. During such mass migration events, information is an essential commodity. Of particular importance is geographical (e.g., pathways and refugee camps) and social (e.g., refugee activities and networking) information. Traditionally, such information had been produced and disseminated by authorities, but a new paradigm is emerging: Web 2.0 and mobile computing technologies enable the involved stakeholder communities to produce, access, and consume migration-related information. The purpose of this article is to put forward a new typology for understanding the factors around migration and to examine the potential of crowd-generated data—especially open data and volunteered geographic information—to study such events. Using the recent wave of migration to Europe from the Middle East and northern Africa as a case study, we examine how migration-related information can be dynamically mined and analyzed to study the migrants’ pathways from their home countries to their destination sites, as well as the conditions and activities that evolve during the migration process. These new data sources can provide a deeper and more fine-grained understanding of the migration process, often in real-time, and often through the eyes of the communities affected by it. Nevertheless, this also raises significant methodological and technical challenges for their future use associated with potential biases, data quality issues, and data processing.

Keywords: Refugees, Forced migration, Humanitarian crisis, Volunteered geographic information, Crowdsourcing, Social media, GIS, Web 2.0.
Cumulative flow (2011–2015) illustrating Syrian forced migration to neighboring countries and other destination countries. Line thickness indicates increasing number of persons migrating.

Retweet network of geolocated Twitter microblogs that are discussing opinions, news and retweeting information related to “refugee” in multiple languages from May to August 2017.

A concept graph illustrating the associations between a keyword related to root factors of mass migration such as poverty (“welfare”) to other keywords, as they appear in our Twitter data corpus. The color of the node refers to specific themes: locations (green), actors (dark red), topics (red), entities and individuals (blue), concepts (white), and events (yellow). Red edges represent active associations between terms; gray edges represent inactive associations between terms.

An agent-based model of migration: top: the spatial environment, where the lines represent migration pathways, and the nodes represent number of migrants. Purple nodes represent final destination sites, red nodes show migrant deaths, and green nodes show migrants en route (source: Hu 2016).

Full Reference: 
Curry, T., Croitoru, A., Crooks, A.T. and Stefanidis, A. (in press), Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration, Journal of Geographical Systems. DOI: (pdf)

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