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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. [ABSTRACT FROM AUTHOR]

The proliferation of volunteered geographic information (VGI), such as OpenStreetMap (OSM) enabled by technological advancements, has led to large volumes of user-generated geographical content. While this data is becoming widely used, the understanding of the quality characteristics of such data is still largely unexplored. An open research question is the relationship between demographic indicators and VGI quality. While earlier studies have suggested a potential relationship between VGI quality and population density or socio-economic characteristics of an area, such relationships have not been rigorously explored, and mainly remained qualitative in nature. This paper addresses this gap by quantifying the relationship between demographic properties of a given area and the quality of VGI contributions. We study specifically the demographic characteristics of the mapped area and its relation to two dimensions of spatial data quality, namely positional accuracy and completeness of the corresponding VGI contributions with respect to OSM using the Denver (Colorado, US) area as a case study. We use non-spatial and spatial analysis techniques to identify potential associations among demographics data and the distribution of positional and completeness errors found within VGI data. Generally, the results of our study show a lack of statistically significant support for the assumption that demographic properties affect the positional accuracy or completeness of VGI. While this research is focused on a specific area, our results showcase the complex nature of the relationship between VGI quality and demographics, and highlights the need for a better understanding of it. By doing so, we add to the debate of how demographics impact on the quality of VGI data and lays the foundation to further work. [ABSTRACT FROM AUTHOR]