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We appreciate the pedantry.

I think I came up with that name from a game of telephone of sources claiming to have the longest line of sight. There's a lot of really random sources on the Internet that mention the line of sight but do not cite the data or analysis. I have seen Hindu Tagh thrown around as well so if it's more accurate we want it there.

Part of our motivations were to stop the game of telephone and become an authoritative source on this stuff.

I'm not much of a geographer, more of a HPC guy.

I have this on the TODO list to update tomorrow.


Thanks umpriel for the PR, hope you're well!

Hey Marc, Ryan here. Tom may respond as well

There's two forms of interpolation going on here that I'm not sure you or Dr Dueschle are using. We interpolate a "band of sight" of single a degree for our azithmual projection, but uniquely we also rotate the DEM elevations around all the observers rather than the observer around to see all the elevations.

The effects of the first can be lessened by lowering the band of sight such that we only process half a degree at a time so that we make sure we get more coverage further away. We plan on running some more experiments by rotating to cover more points.

The algorithm is already fairly expensive to run against the whole world so we weren't particularly interested in that level of coverage for the full earth.

For total viewshed area, our algorithm comes in at roughly a percent or so difference which was what we used as our benchmark for correctness.

All this to say, no, we don't think you both are wrong, we've been looking at making ours more accurate. At a world scale that's quite computationally expensive, so we didn't use that methodology for our initial launch. We see our results as validation of yours, not as something we've disproved.

Edit: grammar


Ok that makes sense, thanks for the reply! Maybe document this "percent or so" error in the FAQ since it is about 16 times bigger than the (other?) ~0.0685% error you mention that can be caused by the AEQD reprojections.

Good idea, I'll add it to the FAQ later today. Under a section of "why don't these results match the other tools". The projection error is separate as you mentioned.

The error I've experienced hunting bugs tends to be within about .5-2%. That's a vibe, not an empirical "I've calculated the error to be 1.5%". We definitely expect that bound to tighten as we get access to more computational resources.

I do not think this is numerical however. I think it's more directly related to rasterization, interpolation, and not enough angle coverage. We have fairly good numerical and viewshed tests to double check we don't have weirdness going on there.


The short answer: yes, this is partially the reason.

Options trading is making a bet that a stock will go up or down. To accomplish this, you buy the rights to sell/buy stock at a given stock price (we call this a strike price). You do not OWN the stock, you borrow it, with the guarantee you can exercise your right to the buy/sell the stock once it hits the strike price.

When you trade options, you typically don't buy the right to buy/sell individual stocks, you buy the right to sell 100 at a time. This means if Softbank buys 4bil in options in companies, they now have the risk/reward of 400bil in the market. In order to give Softbank the rights to the stock if they were to exercise their options, banks must back those options with shares. You may see where this is going. By making 4bil in options orders, Softbank has caused banks to be forced to buy 400bil in stocks, pumping the price.

TLDR; Softbank forced banks to buy 400bil in stocks, effectively pumping the market


This isn't correct. Option prices are quoted as the price for one share, but you have to pay 100x.

The leverage you get from options is the difference between the price of the option and the price of the stock. The total value of the stocks you have the right to buy or sell is called the notational value.

Your multiply by a 100 thing isn't at all how this works.


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