Orbital Insights, a company headquartered in the World Trade Center Tower 1, accesses data generated by satellites, drones, balloons, smart phones, and sources which it process and analyze at petabyte scale to make it actionable for businesses, governments, and NGOs. Last evening at a reception for people attending GEOGRAPHY 2050 Powering Our Future Planet, I had an opportunity to chat with a couple of their technical experts about some the ways they are applying geospatial data that I doubt anyone would have imagined just a few years ago.  As an example of a commercial application Orbital Insights tracks global oil Inventories which lets you follow oil stock fluctuations on a map.  Their data correlates with the U.S. Energy Information Administration (EIA) data, but is available 2-3 days before EIA data is released which provide early valuable insight into price fluctuations.

Counting cars in airport parking lots as a measure of economic activity

Using satellite imagery from Digital Globe and other providers, Orbital Insights used machine learning to identify and count the number of cars in parking lots at Damascus and Aleppo airports.  One of the periods they analyzed was during and after the government’s successful attack on Aleppo.  While the number of cars parked at Damascus airport remained fairly constant, at the cars parked at Aleppo’s airport gradually dropped as the government attack intensified.  Ultimately it dropped to zero and then gradually began to increase again.  But inspecting the high resolution imagery, it was possible to make out that the aircraft parked at the airport after the fall of Aleppo were not commercial as before, but military aircraft.

Monitoring cell phone activity as a measure of car manufacturing activity

Anonymized cell phone activity data, which is available commercially from Apple, Google and others, carries a location and time stamp.  In a creative application of how this data cal be used, analysts at Orbital Insights defined polygons that enclosed the automobile assembly plants of two car manufacturers in the southern U.S. This provided a surrogate for activity on these assembly lines.  They looked at a period that included a couple of weeks prior to a major hurricane coming through the area and for week or two subsequent to the hurricane.  They were able to determine that one of the manufacturers ramped production up sharply before the hurricane hit while the other proceded in a business as usual mode.  They were also able to determine how long it took to restore production to pre-hurricane levels.

In both cases it is difficult to imagine how this kind of information could have been acquired and made public using a traditional approach.  I doubt that the Syrian government would have been forthcoming with this data, and in the second case this is competitive information that would have been difficult to acquire and release publicly.