Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study. (May 2018) - Levin, Noam; Ali, Saleem; Crandall, David
Abstract: 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. We used the recent Arab Spring as a case study, and examined temporal trends in monthly time series of variables which we 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 from the Global Data on Events, Location and Tone (GDELT) project on fight events were positively correlated with actual deaths from conflicts. We propose that big data and remote sensing datasets have potential to provide disaggregated and timely data on conflicts where official statistics are lacking, offering an effective approach for monitoring geopolitical and environmental changes on Earth. Highlights: We collated spatially explicit time series of night lights, social media & big data. We quantified conflict intensity in all Arab countries following the Arab Spring. Changes in Flickr photos, night lights and news items indicated intense conflicts. This approach can aid in monitoring geopolitical and environmental changes on Earth.