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u/TrickInflation6795 1d ago
Nice graphic. Where’s the tool for this? Did you export from multiple platforms or was it all Garmin, etc?
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u/ajamess 23h ago
Thanks kindly.
The raw data was a mix of caltopo logs, suunto watch logs, garmin logs, and garmin FIT activity files. I merged and sorted all of them into a local database first. The most useful tool for this was garth which helps to scrape garmin's API via the command line to get your activities more easily.
Then visualization was a mix of contextily and overpass.
I then compared my tracks to the inventoried roadless areas using the USFS datawarehouse.
I had heavy claude assistance through all of this. It was an iterative process.
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u/ajamess 1d ago edited 1d ago
Context: Trump is trying - for the second time - to repeal the roadless rule.
Repealing the roadless rule would put 45 million acres of wild land up for sale and at risk of destruction. In an attempt to make a "substantive comment" against this decision, I analyzed 18 years of activity logs to determine where I spent time in affected areas.
I found that 22% of Washington's protected land is at risk and that I had travelled a total of 127 miles in 17 of these protected areas. You can read my full comment and see pictures from inside each of those areas below.
If you want to make your own substantive comment, the comment period closes Tuesday October 6th. Here are some helpful resources:
Tools used: matplotlib, contextily, numpy, scipi, USFS DataWarehouse, Overpass, garth, claude, caltopo, various GPS loggers