Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study


Tracking global and regional conflict zones requires spatially explicit information in near real-time. Here, we examined the potential of remote sensing time-series data (night lights) and big data (data mining of news events and Flickr photos) for monitoring and understanding crisis development and refugee flows. I will present the recent Arab Spring as a case study, and examine temporal trends in monthly time series of variables which I hypothesized to indicate conflict intensity, covering all Arab countries. Both Flickr photos and night-time lights proved as sensitive indicators for loss of economic and human capital, and news items were positively correlated with actual deaths from conflicts. Big data and remote sensing datasets therefore can provide disaggregated and timely data on conflicts where official statistics are lacking, offering an effective approach for monitoring geopolitical and environmental changes on Earth.


Noam Levin is an Associate Professor and Head of the Remote Sensing Lab at the Department of Geography in the Hebrew University of Jerusalem since 2008. He studies geographical and environmental patterns and processes of land cover changes in the face of human and climate induced changes using remote sensing and Geographic Information Systems (GIS) tools. In his work he combines field work, remote sensing of satellite images, spatial analysis of GIS layers, statistical analyses and modelling. Noam’s current research focuses on remote sensing of night lights as indicators of human activity, wildfires, conservation planning over spatial scales from local to global, landscape ecology, historical geography and aeolian processes. Noam has a great interest in maps, and in exploring new methods to analyze spatial information, from historical maps, GIS layers, aerial photographs and satellite images. He is an international PI at the ARC Centre of Excellence for Environmental Decisions.


Room 314, level 3, Steele building (3#)

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