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Comparison · Analytics
GSODR vs mlr3spatial
A side-by-side editorial comparison of GSODR and mlr3spatial — release velocity, themes, recent moves, and the top alternatives to consider.
GSODR vs mlr3spatial: at a glance
What is GSODR?
A weather-station data client that broke one return type to hand back distances instead of bare IDs.
GSODR fetches and tidies NOAA Global Surface Summary of the Day weather data for R. The 4.0.0 release made nearest_stations() return a data.table of full station metadata plus distance in kilometres rather than a character vector of station IDs, with a documented one-liner for anyone who only wanted the IDs. Nothing has shipped since March 2024.
What is mlr3spatial?
Raster prediction in mlr3 finally returns class probabilities, not just hard labels.
mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.
GSODR vs mlr3spatial: editorial side-by-side
GSODR
0.0ANALYTICS
A weather-station data client that broke one return type to hand back distances instead of bare IDs.
◆ Current state
GSODR fetches and tidies NOAA Global Surface Summary of the Day weather data for R. The 4.0.0 release made nearest_stations() return a data.table of full station metadata plus distance in kilometres rather than a character vector of station IDs, with a documented one-liner for anyone who only wanted the IDs. Nothing has shipped since March 2024.
◆ Where it's heading
The package is mature and its releases read as upkeep: refreshing the internal ISD history database, dropping dependencies in favour of base and curl, and hardening the download path against station-year combinations that do not exist. The 4.0.0 change fits the same pattern of returning more structure by default rather than making callers query twice.
◆ Prediction
The most likely next release is another internal station-history refresh; there is no signal of new data sources or analysis features in these entries.
mlr3spatial
0.0ANALYTICS
Raster prediction in mlr3 finally returns class probabilities, not just hard labels.
◆ Current state
mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.
◆ Where it's heading
The package tracks the mlr3 core rather than leading it — 0.5.0 and 0.6.1 exist to absorb upstream changes in paradox and mlr3. Against that background, 0.7.0 adding probability predictions to predict_spatial() is the first genuine capability increase in a while, arriving alongside two DataBackendRaster fixes for multi-band sources and similarly-named layers. Cadence is roughly one release per year.
◆ Prediction
Given the pattern, the next release is more likely to be compatibility work against a new mlr3 or terra version than another feature; further raster-backend edge cases around layer naming are the visible loose end.
Alternatives to GSODR and mlr3spatial
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either GSODR or mlr3spatial.
sftime
The spatiotemporal companion to sf, moving at the pace of the packages around it.
ReLTER
An interface to Europe's long-term ecosystem research network that went quiet after 2.0.
waywiser
Spatial model assessment that spent the last year on cross-platform arithmetic and CRAN rules.
taxizedb
Stopped trusting the cloud to prepare its taxonomic databases and does the conversion locally.
fellingdater
Went from estimating felling dates to doing the crossdating that produces them.
See all GSODR alternatives → · See all mlr3spatial alternatives →
Recent activity from GSODR and mlr3spatial
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3spatialpredict_spatial() gains probability predictions
- 10mo agomlr3spatialCompatibility with mlr3 1.2.0
- 1y agomlr3spatialError on conflicting X/Y columns in sf objects
- 2y agoGSODRnearest_stations() returns metadata and distances
- 2y agomlr3spatialCompatibility with paradox 1.0.0
- 2y agoGSODRStation history refresh and internal tidying
- 2y agoGSODRBad station-year requests warn instead of failing the batch
- 3y agomlr3spatialUse terra::inMemory() instead of the @ptr slot
- 3y agomlr3spatialspatial_predict() accepts stars, sf and Raster* inputs
Frequently asked questions
What is the difference between GSODR and mlr3spatial?
They serve adjacent needs but don't currently overlap on shipped themes. GSODR and mlr3spatial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is GSODR better than mlr3spatial?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GSODR and mlr3spatial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to GSODR?
Top GSODR alternatives in Analytics are ranked by recent ship velocity. Browse the "GSODR alternatives" section above for the current picks, or visit /alternatives/gsodr for the full list with editorial commentary on each.
What are the best alternatives to mlr3spatial?
Top mlr3spatial alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3spatial alternatives" section above for the current picks, or visit /alternatives/mlr3spatial for the full list with editorial commentary on each.