Using R for Spatial Analysis. Tina A.

Transcription

1 Using R for Spatial Analysis Tina A. Cormier

2 Outline What is R & why should you consider using it for geo? What can you do with R? Common challenges Code examples with outputs Case Study: Forest Biomass Mapping

3 What is R? a powerful tool for statistical programming. an open source software project and programming language with over 10,000 contributed packages. most widely known for its statistical capabilities, beautiful graphics, and epic flexibility. lesser known as a fully functional command line GIS.

4 Is R the new GIS?

5 Why should you consider R for Geo? free and open source RAD - Repeatability, Automation, Documentation platform independent (Mac, Windows, Linux) very large and active user community - easy to get help! cutting edge and actively being developed more than 10,000 packages that build on base R

6 What can you do with R? perform the same operations as a dedicated GIS system like QGIS or ArcGIS* on vector or raster data. data pre-processing and clean up. amazing, customizable visualizations. integrated environment for automated preprocessing, spatial analysis, and modeling. read/write any data formats that GDAL handles.

7 What can you do with R?

8 What can you do with R?

9 What can you do with R?

10 What can you do with R?

11 What can you do with R?

12 Common Challenges Steep learning curve, though programming or command line experience helps. Limited interaction with maps/graphs natively - need helper packages like leaflet, plotly, shiny, etc. Change in mindset - No GUI and can t really see analysis unfold as in a traditional desktop GIS. Note that R IS interactive, however. Objects are stored in memory.

13 Use Case: Quick Thematic Maps qtm(shp = birds.sp, symbols.col="common.name", symbols.size=0.15, title="species Observations\n ", symbols.title.col="common Name") + tm_compass() + tm_scale_bar()

14 Use Case: Quick Thematic Maps

15 Use Case: Data Clean Up # "Easy" date formatting = lubridate package birds$observation.date <- as_date(birds$observation.date) > max(birds$observation.date) [1] " " > median(birds$observation.date) [1] " " > min(birds$observation.date) [1] " "

16 Use Case: Data Clean Up # Remove dups birds <- birds[!duplicated(birds),] # Remove incomplete cases birds <- birds[complete.cases(birds),]

17 Use Case: Vector Processing # Records from a text file -> spatial object birds.sp <- SpatialPointsDataFrame(coords=birds.xy, data=birds, proj4string = CRS("+proj=longlat +datum=wgs84 +ellps=wgs84 +towgs84=0,0,0")) # Reprojecting birds.proj <- sptransform(birds.sp, CRS("+init=epsg:4267"))

18 Use Case: Vector Processing # Intersection birds.int <- intersect(birds.proj, county) # Clip - using an index! birds.clip <- birds.proj[county,] # Dissolve states <- aggregate(county, by="state_name")

19 Use Case: Data Summary # Summarize one attribute (i.e., column) by another tapply(birds$observation.count, birds$state_name, sum) > CT ME MA NH RI VT 82538

20 Use Case: Data Summary

21 Use Case: Raster processing and remote sensing Clip Mask Reproj Band Math

22 Use Case: Raster processing library(raster) library(ggplot2) Birds.cc <extract(canopy_cover, birds) aes(canopy_cover, fill=common.name, color=common.name)) + geom_density(alpha=0.1)

23 Use Case: Raster processing and remote sensing library(ggalley) ggpairs(birds[, c("canopy_cover", "imp_surface", "ndvi")])

24 Use Case: Choropleth Maps library(choroplethr) library(choroplethrmaps) col.pal <- brewer.pal(7,"rdylgn") m <- CountyChoropleth$new(df_for_map) m$ggplot_scale <- scale_fill_manual(name="anomalies", values=col.pal, drop=false)

25 Case Study: Forest Biomass Mapping Objective: To undertake the research required to demonstrate the potential for annual changes in the aboveground carbon density (ACD) of forests and other woody vegetation to be estimated directly, consistently, and with measurable accuracy across large areas...

26 Case Study: Forest Biomass Mapping General workflow: field plots -> lidar transects -> satellite images

27 Case Study: Forest Biomass Mapping 1. Acquire lidar over field data. 2. Tile lidar to ultimate res of biomass map. 3. Extract lidar for each field plot. 4. Metrics for each tile and plot. 5. Use plot-level lidar metrics to train a biomass model. 6. Apply model to the rest of the tiles = biomass map! 7. Scale #5 to country level *annually* using [satellite imagery].

28 Case Study: Forest Biomass Mapping Lidar Processing Flow Before R: 1. Convert 1 km las tiles from vendor -> 30 m las tiles (LAStools). 2. Las2txt (LAStools) so we could read the lidar files into R as text files = duplicated our entire data set! 3. Calculate custom metrics on the text files (R). 4. Calculate traditional metrics on the las files (system call from R to FUSION software). 5. Modeling (R). 6. Apply model to map/grid (R) = YAY!

29 Case Study: Forest Biomass Mapping rlidar - 04/20/2015 a lidar processing and visualization package. emphasis on identification and metrics surrounding individual trees. has las metrics function that computes a suite of traditional lidar metrics (e.g., return counts by class, height percentiles, intensity metrics). limited number of functions. For use on small las files.

30 Case Study: Forest Biomass Mapping lidr - 12/31/2016 a lidar processing and (2D & 3D) visualization package. build and apply functions to lidar catalogs. build terrain models. normalization. clip lidar with various geometries. compute predefined and custom metrics. point filtering individual tree segmentation

31 Case Study: Forest Biomass Mapping library(lidr) # 1. Read the las file directly into R las <- readlas(lasfile) # 2. Plot the las file plot(las)

32 Case Study: Forest Biomass Mapping Quickly inspect my tile, including zoom and rotate capabilities

33 Case Study: Forest Biomass Mapping LAS Header Info: lidar collection info (e.g., point density, number of points, area) min/max X, Y, Z

34 Case Study: Forest Biomass Mapping

35 Case Study: Forest Biomass Mapping Create Digital Terrain Model and Normalize raw lidar elevation ground elevation height above ground

36 Case Study: Forest Biomass Mapping Create Digital Terrain Model and Normalize Seriously? Look how easy this is!! ^^^ I used to normalize like this - first had to build terrain model using LAStools >>>

37 Case Study: Forest Biomass Mapping Now for some fun! lasclip our field sites from the larger lidar acquisition. Build height profiles and look at the vertical structure of our field plots.

38 Case Study: Forest Biomass Mapping Field plots now linked with lidar Use a model to predict biomass across the rest of the lidar acquisition.

39 Case Study: Forest Biomass Mapping Preliminary

40 Case Study: Forest Biomass Mapping Preliminary

41 Case Study: Forest Biomass Mapping Preliminary

42 Case Study: Forest Biomass Mapping Preliminary

43 Packages

44 Where to get help Stack overflow R Reference Card R FAQ R mailing lists R-sig-geo Pro Tip: Half the battle is knowing how to google #devgoogle

45 Resources - not exhaustive! R s spatial ecosystem ggmap cheat sheet R Spatial Processing large rasters Geospatial Viz in R Efficient programming in R Creating maps with R geojson in R Colors in R & R colors cheat sheet R plot.ly library R graph gallery

46 Thank you!

47 Appendix - Extra Slides

48 Data Structures in R - for the programmers Vectors = 1-D, single type (numeric, character). Matrix = 2-D, single type, rows x columns. Data Frame = 2-D, multiple types, rows x columns. Array = N-D, single type, rows x columns x n (time, image bands, etc.) List = Collection of objects, multiple types

49 Data Structures in R - for the GIS people Image from: