GEOSTAT 2018 - links and slides
Roger Bivand: “A practical history of R-sig-geo”
Edzer Pebesma: "New R packages for spatial and spatiotemporal vector and raster data"
Tim Appelhans: "Introduction to mapview and mapedit"
Robin Lovelace: “Geocomputation with R”
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data"
Tom Hengl: "Machine Learning as a generic framework for spatial prediction"
Edzer Pebesma: "Simple features for R / tidy spatial analysis"
Tim Appelhans: “Using mapview and mapedit to visualise and edit geo-spatial data interactively in R”
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data" (practical)
Jianghao Wang: Urban Sensing and Computing - Big Data Analytic with Open Source Software
Jakub Nowosad: “GeoPAT 2 - analysis of spatial and temporal patterns”
Robin Lovelace: “Integrating spatial data in dplyr workflows: pitfalls and potential”
Jannes Münchow: “R-GIS bridges: the examples of RQGIS and RSAGA”
Jannes Münchow: “The importance of spatial cross-validation in predictive modeling”
Robin Lovelace, Jakub Nowosad, Jannes Münchow: "Geocomputation with R - book unlaunch"
Markus Neteler: “Analysing environmental data with GRASS GIS” (practicals)
Veronica Andreo: "Spatiotemporal data processing and visualization in GRASS GIS"
Tomislav Hengl: “Computing with large rasters in R: tiling, parallelization, optimization”
Edzer Pebesma: “Scalable raster data analysis in the cloud with R”
Tomislav Hengl: "Mapping species distribution using Machine Learning"
Chris Reudenbach: "Link2GI - consistent linking of Open Source GIS with R"
Roger Bivand: "Process scale and autocorrelation"
- Veronica Andreo
- Roger Bivand
- Tim Appelhans
- Hanna Meyer
- Chris Reudenbach
- Robin Lovelace
- Jakub Nowosad
- Jannes Muenchow
- Marcus Neteler
- Matej Man
- Tom Hengl
- Edzer Pebesma
- The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments
- A nice toolbox based on R and GRASS GIS for mapping and modeling invasive species through remote sensing (LiDAR and Hyperspectral)
- To handle large polygons and compute with larger stacks of rasters use: (a) fasterize, and (b) tabularaster.