Findings from a Search for R Spatial Analysis Support. Donald L. Schrupp Wildlife Ecologist Colorado Division of Wildlife (Retired)

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1 Findings from a Search for R Spatial Analysis Support Donald L. Schrupp Wildlife Ecologist Colorado Division of Wildlife (Retired)

2 Findings from a Search for R Spatial Analysis Support === Approach Steps === Install 'R' and RStudio Search(s) for R Spatial Analysis Support Findings: Packages Documentation Tutorials

3 Install 'R' and RStudio 'R' Source: RStudio Source:

4 Search for R Spatial Analysis Support R-Search:

5 Search for R Spatial Analysis Support R Site Search:

6 Search for R Spatial Analysis Support R Site Search Results FUNCTIONS VIGNETTES TASK VIEWS

7 Search for R Spatial Analysis Support R Site Search Results - FUNCTIONS

8 Search for R Spatial Analysis Support R Site Search Results - VIGNETTES

9 Search for R Spatial Analysis Support R Site Search Results TASK VIEW(S)

10 Search for R Spatial Analysis Support CRAN TASK VIEW Analysis of Spatial Data

11 Search for R Spatial Analysis Support CRAN TASK VIEW Analysis of Spatial Data TOPIC AREAS Classes for Spatial Data Handling Spatial Data Reading and Writing Spatial Data Reading and Writing Spatial Data Other Packages Visualization Point Pattern Analysis Geostatistics Disease Mapping and Areal Data Analysis Spatial Regression Ecological Analysis

12 Findings from a Search for R Spatial Analysis Support FINDINGS PACKAGES (168 Related Packages) Ones I Installed in Working Through Tutorials (to date) GISTools gstat maptools raster rgdal rgeos sp spgrass6 Some further GIS capabilities for R Spatial and Spatio-Temporal Geostatistical Modeling, Prediction and Simulation Tools for Reading and Handling Spatial Objects Geographic Data Analysis and Modeling Bindings for Geospatial Data Abstraction Library Interface to Geometry Engine Open Sources (GEOS) Classes and Methods for Spatial Data Interface between GRASS 6+ GIS and R

13 Findings from a Search for R Spatial Analysis Support FINDINGS DOCUMENTATION sp: raster: spgrass6: rgdal: rgeos: gstat: maptools: lattice: rastervis: GISTools:

14 Findings from a Search for R Spatial Analysis Support FINDINGS DOCUMENTATION sp Example

15 Findings from a Search for R Spatial Analysis Support FINDINGS DOCUMENTATION sp Example

16 Findings from a Search for R Spatial Analysis Support FINDINGS DOCUMENTATION sp PDF -1

17 FINDINGS DOCUMENTATION sp PDF - 2

18 FINDINGS DOCUMENTATION sp PDF - 3

19 FINDINGS DOCUMENTATION sp PDF - 4

20 Findings from a Search for R Spatial Analysis Support FINDINGS TUTORIALS Spatial Analysis in R NEON: NPS: CRAN ( spgrass6 PDF )

21 Findings from a Search for R Spatial Analysis Support FINDINGS TUTORIALS NEON:

22 NEON: FINDINGS TUTORIALS TUTORIALS Cover: Metadata Raster Data Vector Data Data Visualization GIS & Spatial Data in R and Python Remote Sensing Hyperspectral Remote Sensing Hierarchical Data Format Version 5 R Programming 1 tutorial 8 tutorials 6 tutorials 4 tutorials 18 tutorials 12 tutorials 7 tutorials 9 tutorials 45 tutorials Data Sets and Coding is supplied; Necessary R Packages are specified

23 NEON: FINDINGS TUTORIALS TUTORIALS by 'R' Package Available for: dplyr (7) ggplot2 (12) h5py (1) lubridate (6) maps (1) maptools (2) plyr (2) raster (25) rastervis (3) rgdal (GIS) (21) rgeos (3) rhdf5 (11) sp (5) scales (4) gridextra (4) grid (2) reshape2 (3) Note: 'R_package_name' ( # ) [ of tutorials available ]

NEON: FINDINGS TUTORIALS http://neondataskills.

24 NEON: FINDINGS TUTORIALS TUTORIAL Example Code in RStudio:

FINDINGS TUTORIALS NPS: http://science.nature.nps.

25 FINDINGS TUTORIALS NPS:

26 NPS: FINDINGS TUTORIALS Introduction Why use R for spatial data? Before the Webinar Spatial Objects Reading Spatial Objects Writing Spatial Objects Creating kml files for Google Earth / Google Maps Spatial Overlays and Extractions Raster Computations Package raster documentation Why use R raster? Key raster concepts Basic raster function example: Raster for climate data analyses

27 NPS: FINDINGS TUTORIALS If you wish to download the data files and R code to play along at home, I have posted them on the NPS R website: CABR data (DEM, Boundary, Slope, Aspect, Vegetation Map Polygons) PRISM data from Colorado (for John Gross' raster examples. Extract the.gz files into a "prism/t_min" for John's code.) tmin_1900.zip tmin_2010.zip tmin_sum_ zip tmin_sum_ zip CMIP3_Tavg_Monthly_A2.zip Tom's R code using gdal, overlay, etc. John's R code using raster Tom's kmlpolygons.r function to generate labeled kml polygons for Google Earth & Google Maps

28 ` FINDINGS TUTORIALS CRAN ( spgrass6 PDF ) Documentation / Code Examples

29 FINDINGS TUTORIALS CRAN ( spgrass6 PDF ) Document Content Headings Introduction Installing the interface package The sp package Using graphics with sp objects Using the spgrass6 package with raster data Using the spgrass6 package with vector data Conclusion References

30 FINDINGS TUTORIALS CRAN ( spgrass6 PDF ) Sample Code library(spgrass6) system("g.region -g3") grand_nlcd <- readrast6("grand_county_nlcd_attributed_n83_z13", ignore.stderr = TRUE) summary(grand_nlcd) table(grand_nlcd$cat) image(grand_nlcd, col = rev(colors(16))) legend("top", legend = 11:12, fill = rev(colors(16)), cex = 0.8, bty = "n", horiz = TRUE) legend("left", legend = 21:24, fill = rev(colors(16)), cex = 0.8, bty = "n", horiz = FALSE) legend("right", legend = 41:43, fill = rev(colors(16)), cex = 0.8, bty = "n", horiz = FALSE) legend("bottom", legend = 90:95, fill = rev(colors(16)), cex = 0.8, bty = "n", horiz = TRUE) grand_dem <- readrast6("grand_county_co_ned_30m", ignore.stderr = TRUE) summary(grand) table(grand_dem$cat) image(grand_dem, col = rev(colors(10)))

31 FINDINGS TUTORIALS CRAN ( spgrass6 PDF ) Example Code Outputs

32 FINDINGS GME: The Geospatial Modeling Environment (GME) is a platform designed to help to facilitate rigorous spatial analysis and modeling. GME provides you with a suite of analysis and modeling tools, ranging from small 'building blocks' that you can use to construct a sophisticated work-flow, to completely self-contained analysis programs. It also uses the extraordinarily powerful open source software R as the statistical engine to drive some of the analysis tools. One of the many strengths of R is that it is open source, completely transparent and well documented: important characteristics for any scientific analytical software.

33 FINDINGS Evolutionary Developments GME:

34 Findings from a Search for R Spatial Analysis Support ACKNOWLEDGEMENTS and THANKS: The R Development Community Open Source Geospatial Community NEON and NPS QUESTIONS?

Roger S. Bivand Edzer J. Pebesma Virgilio Gömez-Rubio. Applied Spatial Data Analysis with R. 4:1 Springer

Roger S. Bivand Edzer J. Pebesma Virgilio Gömez-Rubio Applied Spatial Data Analysis with R 4:1 Springer Contents Preface VII 1 Hello World: Introducing Spatial Data 1 1.1 Applied Spatial Data Analysis