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Bulk Interpolation using R Environment Jiří Kadlec – Aalto University, Finland Pavel Treml – Masaryk Water Research Institute and Charles University, Czech.

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Presentation on theme: "Bulk Interpolation using R Environment Jiří Kadlec – Aalto University, Finland Pavel Treml – Masaryk Water Research Institute and Charles University, Czech."— Presentation transcript:

1 Bulk Interpolation using R Environment Jiří Kadlec – Aalto University, Finland Pavel Treml – Masaryk Water Research Institute and Charles University, Czech Republic FOSS4G Conference - Nottingham

2 Fields with Observations in time and space What, Where, When (irregular sampling) Space, S Time, T Variables, V s t ViVi D c “Where” “What” “When” A data value 4.2 Praha Air Temperature (C) Image created by CUAHSI, 2010

3 Type of data in this tutorial Tens of variables Hundreds of stations Hundreds of observations per station Incomplete data Observations stationlongitudelatitudevariabletimevalue Praha temperature Praha temperature Praha snow Brno temperature Brno snow TASK For each time-step, Examine how one or More variables change In space

4 Interpolation Deterministic methods Geostatistical methods Repeated over many time steps How to automate? ? time value lat lon ? Interpolation - Time Interpolation - Space

5 the R Environment Free statistical software Windows, Mac, Linux Create High-quality graphics Many input data formats – Text file – Vector data (shapefile) – Raster data (grid) Many output data formats – Picture – Text file – Raster, vector Scripting language for automating repeated tasks

6 Case study: Maps of air temperature and snow, Czech Republic Year 2013 (365 maps) Require identical color-scale Need to show rivers, boundaries Need to show point values (to assess interpolation) Load Data sets Select next time Select matching observations Run Interpolation Create map cartography More times ? END START NO YES

7 Load Data Sets – Raster (SRTM elevation) Library(“sp”) library(“raster") library(“rcolorBrewer") srtm <- raster("srtm_utm.asc") colors=brewer.pal(6, "YlOrRd") intervals = c(0, 250, 500, 750, 1000, 1600) spplot(srtm_proj, col.regions=colors, at=intervals) Select packages With needed functions Color ramp setting Raster: Load from local file Visualize raster using spplot Note: You can get DEM for your area in R using:

8 Load Data Sets – Vector (boundaries, rivers) library(“maptools") library(“sp") border = readShapeLines(“border.shp") border_layer = list("sp.lines", border, lwd=2.0,col=“black") rivers = readShapeLines(“rivers.shp") proj4string(rivers) <- CRS("+proj=krovak +lat_0=49.5 +lon_0= ") rivers_proj <- spTransform(rivers, CRS("+proj=utm +zone=33")) river_layer = list("sp.lines", rivers_proj, lwd=1,col=“blue") layout = list(river_layer, border_layer) spplot(srtm, sp.layout=layout) Select packages With needed functions Rivers: Load shapefile, Reproject vector from Krovak to UTM system Borders: Load shapefile Visualize vector datasets on top of raster

9 Load Text File – Point Data (Stations) At first, we can use a subset (for first date/time in the dataset) data = read.table(“data.txt”, header = TRUE, sep = "\t", dec = ".") st = subset(data, DateTimeUTC == ‘ ’ & VariableCode==“SNOW") Coordinates(st) = ~Long + Lat proj4string(st) = CRS("+init=epsg:4326") stations <- spTransform(st, CRS("+proj=utm +zone=33")) stations_layer = list("sp.points", stations, pch=19, cex=1.0, col="black") labels = list("panel.text", coordinates(stations)[,1], coordinates(stations)[,2], labels=stations$value, col="black", font=1, pos=2) layout = list(stations_layer, labels) Reproject from Lat/Lon to UTM system

10 All Layers together (raster, vector, points) (We use different color ramp in this example)

11 Interpolation: IDW and Kriging Methods

12 Interpolation: Covariate (Elevation) model = lm(value ~ elev, data) intercept = model$coefficients[1] slope = model$coefficients[2] plot(value~elev, data) abline(model) TEMP = a * ELEVATION + b In our case temperature is often negatively correlated with elevation

13 Interpolation: Elevation as covariate TEMP = a * ELEVATION + b residuals Instead of interpolating temperature directly, we create grid using regression equation and we only interpolate the residuals Using TEMP = a * ELEVATION + b Interpolated residuals Combination (regression + interpol. residuals

14 Color Breaks, Color Ramps #user-defined color breaks colors = rev(brewer.pal(8, "YlGnBu")) brk <- c(-40,-35,-30,-25,-20,-15,-10,-5,0) plot(grid,breaks=brk,col=palette(colors) One color ramp and set of user-defined color breaks easily re-used for different grids RColorBrewer packages provides Pre-defined color ramps

15 Example Final Map: One time step Combines the previous steps … Snow depth (cm):

16 Saving map to file figure = “ jpg” png(filename = figure, width = 1500, height = 1000, pointsize = 25, quality = 100, bg = "white", res = 150) DO THE PLOT COMMANDS HERE dev.off() Set image size Set image resolution Other options (margins, multiple maps in one image) PNG picture JPEG picture PDF file WMF file.asc ascii grid file (for GIS softwares) ……..

17 Bulk Interpolation: “FOR” loop (multiple time steps) for (j in 1:length(timesteps)) { # select subset of observations # run interpolation # create map # export map to file (picture, pdf, …) } Schema of the Loop Load Data sets Select next time Select matching observations Run Interpolation Create map cartography More times ? END START NO YES

18 Final Map: Multiple time steps

19 R as Compared with Desktop GIS R Statistical Software Environment Desktop GIS Maps are highly interactive Create small number of maps with graphical user interface Automated cartography (label placement…) Map-Layout, model builder tools ArcGIS, QGIS, SAGA, MapWindow,… IDV, Panoply, … (-) Maps are static (-) Some commands counter-intuitive for novice user (+) Create very large number of maps with same cartographic symbology (+) Task is easy to automate and reproducible (+) Good documentation and large user base Desktop GIS: project file (.mxd,.qpj, …)R: script (.R)

20 References Bivand, Roger S., Edzer J. Pebesma, and Virgilio Gómez Rubio. Applied spatial data: analysis with R. Springer, Hengl, Tomislav. A practical guide to geostatistical mapping of environmental variables. Vol No BOOKS (source of data in our demos) WEBSITES R-PACKAGES USED RColorBrewer, maptools, rgdal, sp, raster

21 Try the scripts yourself! hydrodata.info/R/ Sample scripts for bulk interpolation This presentation Source data { central Europe meteorological observations }


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