Just a tiny example with a ~1 GB .gpkg file (with an index)...the equivalent GeoParquet is ~600 MB (without an index), can be read into #rstats sf in a ~ 5 s, and can be written in ~3 s (mostly because of the 'arrow' package's use of ALTREP for strings) #rspatial
The {regional} package calculates intra-regional and inter-regional similarities based on user-provided spatial vector objects (regions) and spatial raster objects (cells with values).
Learn more at https://t.co/neAodyvhHT.
#rspatial#rstats#gischat
Sorry @milos_agathon , today is my turn! (Although probably you already made this map). Life expectancy at birth, 2019. Made with #rspatial following this blog post: https://t.co/qLB8fZWsU0
Right geo nerds! Does anyone have opnions on the best COG GeoTIFF spec for a single band raster when using georaster-layer-for-leaflet / leafem::leafem::addGeotiff?Happy to sacrifice accuracy/precision for performance! #rspatial#gischat#gdal🙏
#rspatial#gischat
using QGIS instead of other interactive map packages in R: an example. I use postgis in the background (st_write in the end of the pipe), didn't manage to notify the database so I use a 0.1 interval refresh rate. For me it is a game changer.
7/n I ran the same survey for R. Guess what!
90% love RStudio.
So it’s simpler to pick R. Everyone uses RStudio. We all learn the same way.
But that’s just the 2nd reason.
If you’re looking for a 6-figure data scientist job, what if I told you that #R was a smarter choice than #python?
You’d probably laugh at me right?
Well please read on…
#rstats
Recently I compared the performance of the most popular packages for vector data processing in #python and #rstats. It seems that {terra} is mainly used for raster data, but it does quite well with vectors too.
#rspatial#gischat
🤔How do others use ds in your industry?
🙋🏼♀️How do you start a meetup at your company?
This site has been an idea for a while & makes me so happy to share. Lmk what you think. I'd love to keep adding resources (blogs, meetups, tips) from all♥️ https://t.co/OhC41Tkc9C #rstats
I'm *soooo* close to wrapping up the development sprint I've been on for the last month.
I've made several new mapping functions that I split off into a separate {birdseyeview} package (mainly to keep #overedge from getting too complex) https://t.co/gp160IJELL #rspatial
Getting excited for preliminary read prototyping in #rstats geoarrow 🌏⏩! Can now read WKB-encoded parquet files (as written by sfarrow and geopandas), with some exciting preliminary benchmarks (11 million points + attributes to sf in 5 ish seconds). #rspatial
#rspatial Do we need a package for using our own pictures as base maps? I don’t know, but anyway we have it! {rasterpic} 📦 is now on CRAN, and we can convert our pngs to spatial rasters like this
https://t.co/fpoollARoN
BAM! Thanks so much @TimSalabim3 for helping with this. I can't quite get over how well this works! >1million lines rendered in the browser (v fast) with no lag at all. Issue was some broken geometries and needing to add the src=TRUE arg in leafgl::addGlPolylines. #rspatial
So the #rspatial workflow would be to tile first, load each tile separately and then process? I’m not sure about this because I am trying to classify the imagery with one set of samples for training and validation. Would I have to have individual samples for each tile? Thanks!