A group of UCLA geographers published a paper yesterday in the MIT International Review entitled “Finding Osama bin Laden: An Application of Biogeographic Theories and Satellite Imagery”. The UCLA team used purely open source data, including “Landsat ETM+, Shuttle Radar Topography Mission, Defense Meteorological Satellite, QuickBird”. Then used a variety of commons geographic analysis techniques, “distance-decay theory, island biogeography theory, and life history characteristics” to predict high probability locations for Osama Bin Laden. The story has already been picked up by 90 media outlets and has been popping up on the front page of several major news outlets.
It would never make it out of the labyrinth of classification schemas in the US government, but it would be fascinating to see what a crowdsourced search for Bin Laden would turn up if better data was made available from the intel/defense community. Since the government data will never be released we thought we could at least help make the open source data easily accesible. So, we took the available data in the MIT article plus relevant data on Afghanistan and pushed it into GeoCommons. We’ve embedded a map with our own take below.
To view this map in GeoCommons Maker! click here.
In addition to the UCLA data we’ve added gridded population data for the area. A big part of the UCLA thesis was Osama would be, “in a larger town rather than a smaller and more isolated town where extinction rate would be higher”. So, the gridded data gives a rough view of population densities in the remote Tora Bora region.
Source data for the maps is here:
Would be great to see what other folks can do with the data to promote other perspectives. Also a nice opportunity to show the power of opening data up for better analysis, QA and alternative perspectives. Kudos to the UCLA team – great to see geographers in the news for doing what they do best.
Welcome to the Esri DC Development Center blog. We write about features of our work on big data analytics, open platforms, and open data, what is new and exciting in the Esri and community, and general industry thought leadership and discussions of geospatial data visualization and analysis.
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