Viewsheds (2d) .6

viewshed on 3d grid_sml

comparing the amount of things one sees on any point on a site, to form a 3 dimensional grid of best vantage points.

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works on existing buildings too!

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Some amazingly useless functions

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um. rain. yes, a rain generator. nope i haven’t managed to parse weathermap data yet.

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and i guess this is a rain occlusion calculator. also, no wind rose. yet. XD

Well.. i mean, i do get the point of calculating rain occlusion on a site. it could be used to drive landscaping design decisions, building placement, perhaps material decisions based on the amount of weathering each part of the building would get.

Or even drive the massing itself so as to utilize the natural topographical features of the site and surrounding landscape to create passive water catchment areas for purposes of rainwater collection and water management.

I could probably go on for a while citing non-existent features and their amazing potential, but, but… no.

I would love to write about potential, but only when their corresponding components are ready to be battle-tested. So, not yet.

p.s. mmm Ironpython can be such a can of worms when it wants to be.

EDIT: (2017/03/27) – finally able to split the gif from weather underground into separate pngs for passing into grasshopper!

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some dirty data

Viewsheds (2d)

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saw a youtube video of KPFUI’s talk at the AEC Symposium 2016 yesterday and i thought it might be fun to work on an implementation of their viewshed generator to plug into the city generator.

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definitely not yet complete (only spent 2 hours on it and it was really cold at the cafe XD), as it only calculates viewsheds in 2d, meaning the data is only valid if you cover your eyes so that you can only see through a horizontal slit at the horizon.

 

longitude comparison of NDVI

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NDVI data from Nasa Earth Observations on october 2016. link

Normalized Difference Vegetation Indexes (NDVI) provide a great way to calculate greenery from photographs alone. What’s most amazing is the fact that Nasa provides data at an accuracy of 0.1 degrees for the entire world, so once can track the changing of forest cover to an accuracy of 11.132 x 11.132km. per month!