Interesting Remote Sensing Applications #1: Estimating shop profits from space
I’m starting a new series here on my blog about interesting and somewhat out-of-the-ordinary applications of remote sensing. So…here is number 1.
An analysis firm called Remote Sensing Metrics LLC has been using satellite images to predict changes in the profits of firms such as Walmart, and thus help predict share prices. How does this work? Well it’s fairly simply – for a firm like Walmart, the more people shop in its stores, the more profitable it will be. Of course, this is a generalisation, but there is likely to be a fairly strong correlation between these two variables. So, all you need to do is find a way to estimate how many people shop at Walmart.
How does remote sensing come into this? Well, you just use high resolution imagery and count the cars in the car parks at their stores. Obtaining base-line data for a number of stores, and then looking at the trends should give a guide to trends in profits. If you want to go further – and these analysts did – you can perform a regression to obtain a quantitative relationship between the number of cars seen in the car parks and the profits of the company.
The technicalities of the approach aren’t detailed in the article – for obvious commercial reasons – but it seems that the data is provided by sensors such as QuickBird and Geo-Eye, which have resolutions down to 40cm. This means they can easily resolve cars – which tend to be at least 1m wide by 2m long. Whether they count the cars using human analysts, or whether some sort of object-based image analysis is used to classify cars in defined areas (the selected car parks), I don’t know – but I suspect the latter.
What do you think of this? Clever use of technology, or "Cold War-style satellite surveillance" (as CNBC put it)? Why not leave a comment.
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This post originally appeared on Robin's Blog.
Categorised as: Academic, Remote Sensing
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