Data Acquisition for Environmental and Humanitarian Crisis Management

Crises are complex phenomena, whereby a long-term situation produces short-term but extremely alerting incidents. Such a crisis is caused by the wave of Middle Eastern refugees and immigrants, attempting to find refuge in European countries. This crisis exhibits an obvious humanitarian component, but also severely adverse environmental effects. A systematic crisis and disaster management process that involves big data analytics with principal goal to minimize the negative impact or consequences of crises and disasters, thus protecting societal and natural environment. Green IT engineering principles are here translated as a need to analyze data in order to detect early warnings of evolving environmental effects. Big Data analytics in the context of crisis management involves efficient solutions in four fundamental aspects of the related technology: Data Volume, measuring the amount of data available, with typical data sets occupying many terabytes. Data velocity is a measure of the rate of data creation, streaming and aggregation. Data variety is a measure of the heterogeneity of data sources, together with the richness of data representation—text, images, videos etc. Data value, measures the usefulness of data in making decisions. This chapter aims to present appropriate solutions in all aspects of distributed data analysis of social media data, so as to define the enabling technologies for high performance decision support for the purpose of crisis management. The presentation will include both existing and innovative appropriate technologies and existing state of the art systems and will aim to propose the advantages and disadvantages of different possibilities for alternative integrated solutions. Additionally, sources of data related to the Syrian refugee crisis are identified in the context of the social media platforms Facebook and Twitter with effects in both the humanitarian and the environmental fronts.