Borrowing from the tidy data principles developed for tabular datasets [Wickham, 2014](https://vita.had.co.nz/papers/tidy-data.pdf), this presentation imagines 'tidy' principles for n-dimensional array data represented by Xarray objects with a specific focus on geospatial datasets
This is a collection of 8 large-scale, real-world datasets for Geospatial Interlinking
The explosive growth in both the volume and density of geospatial data, which has resulted in the di...
Spatial data are today needed in a wide range of application domains. Indeed, spatial properties are...
This paper provides a structure to the recently intensified discussion around ‘data cubes’ as a mean...
© 2018 by the authors. Geographic data is growing in size and variety, which calls for big data mana...
Geographic data is growing in size and variety, which calls for big data management tools and analys...
Real scientific workflows often require working with many heterogeneous but related datasets. Exampl...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
Earth observation sensors deliver ever-expanding collections of geospatial data at multiple resoluti...
<div>Video recording of Robert Benneto's presentation, entitled "Tidy geometries in R" presented at ...
A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been litt...
Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. Inclu...
This paper defines the Field data type for big spatial data. Most big spatial data sets provide info...
Clustering Geo-Data Cubes (CGC) is a Python package to perform clustering analysis for multidimensio...
This is a collection of 8 large-scale, real-world datasets for Geospatial Interlinking
The explosive growth in both the volume and density of geospatial data, which has resulted in the di...
Spatial data are today needed in a wide range of application domains. Indeed, spatial properties are...
This paper provides a structure to the recently intensified discussion around ‘data cubes’ as a mean...
© 2018 by the authors. Geographic data is growing in size and variety, which calls for big data mana...
Geographic data is growing in size and variety, which calls for big data management tools and analys...
Real scientific workflows often require working with many heterogeneous but related datasets. Exampl...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
Earth observation sensors deliver ever-expanding collections of geospatial data at multiple resoluti...
<div>Video recording of Robert Benneto's presentation, entitled "Tidy geometries in R" presented at ...
A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been litt...
Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. Inclu...
This paper defines the Field data type for big spatial data. Most big spatial data sets provide info...
Clustering Geo-Data Cubes (CGC) is a Python package to perform clustering analysis for multidimensio...
This is a collection of 8 large-scale, real-world datasets for Geospatial Interlinking
The explosive growth in both the volume and density of geospatial data, which has resulted in the di...
Spatial data are today needed in a wide range of application domains. Indeed, spatial properties are...
DOI
Add to ORKG