geospatial-data

The gdal/swig/python/samples/ogr_layer_algebra.py should be promoted to an official Python script

Steps:

• move it to gdal/swig/python/scripts
• create a new test script for it in autotest/pyscripts/test_ogr_layer_algebra.py
• write a documentation page in doc/source/programs/ogr_layer_algebra (and link it to the index)

Is there a way to know when the imagery was collected? I assume sometime in the daylight hours, and probably recently(?) but I have no way of knowing by looking at the output and the associated image.

Can that output be put in the CLI output or some other metadata file that gets associated with the LC####### directory?

In the event payload there is always the source and the possibility to add custom properties. Is it possible to extend types and make a base event type with source and custom properties?

TS:
https://github.com/geoman-io/leaflet-geoman/blob/1fdc918fa39ffa84327fdf639fa75865168f716d/leaflet-geoman.d.ts#L1437-L1440

JS where the payload and the custom payload are merged:
https://github.com/

Context: this from @agila5: https://twitter.com/a_gilardi5/status/1487813588454678528

I have not had a chance to read the explanation and am not sure what we should say on the topic but guess it's worth a mention here. Any thoughts?

It's possible for the user to create tables inside the working copy that Kart uses, but if they do so, Kart will never take any notice of them, and there is currently no correct way to add them to the Kart repository (you could try importing such a table, but unless you rename it during import, the imported data will collide with the original table, which will cause the command to fail and add to

In episode _episodes_rmd/12-time-series-raster.Rmd

There is a big chunk of code that can probably be made to look nicer via dplyr:

# Plot RGB data for Julian day 133
 RGB_133 <- stack("data/NEON-DS-Landsat-NDVI/HARV/2011/RGB/133_HARV_landRGB.tif")
 RGB_133_df <- raster::as.data.frame(RGB_133, xy = TRUE)
 quantiles = c(0.02, 0.98)
 r <- quantile(RGB_133_df$X133_HARV_landRGB.1, q

Expected behavior and actual behavior

Cropping any raster with any GeoVector should work. Instead, if the GeoVector is bigger than the world, an error is raised.

simple_world.geojson.zip

Steps to reproduce the probl

In Setup:
https://carpentries-incubator.github.io/geospatial-python/setup.html

Several times, in assisting with this and similar lessons using Anaconda, I have found that it's best to recommend that the learner check off the option "Add Anaconda to my PATH environment variable" in addition to leaving checked the "Register Anaconda as my default Python 3.x" option. It solves a lot of eventual G

Lines L315:L325 in Lesson 5 advocate for subsetting columns without comma notation:

head(gapminder[3])

I'd argue that for clarity sake, we should change this to always use comma notation:

head(gapminder[,3]

See #86