QGIS & R intro. Anto Aasa 07/03/

Transcription

1 QGIS & R intro Anto Aasa 07/03/

2 QGIS Desktop Project plugins Layers Vector Layer Print Composer Add map export geolayers data QGIS Project (*.qgs) queries & design output (map, table,...) English GUI, please...

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4 Layer design

5 Add map elements Print Composer Print composer Map export

6 QGIS Exercises 1. Thematic map Distribution of Estonian population in relation to nature preserves 2. How many people live in the 2 km buffer zone of Tallinn Tartu Luhamaa road? 3. What is the population density (persons per km2) in municipalities with borders intersecting the borders of natural preserves? Compare it with the density of other municipalities! Create thematic map!

7 R

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9 R Base Libraries Scripts Community Steep learning curve

10 R Powerful Extensible Relatively fast Open Command line interface Every problem can be solved in several different ways

11 R fully featured Geographical Information System geographically weighted regression spatial interaction models general purpose GIS spatial selections, buffers and clipping. all these capabilities are found in a single programme

12 fundamentally different from traditional GIS software ATTRIBUTE ADVANTAGES OF R DRAWBACKS OF R User interface Visualising data Selecting data Manipulating data Analysing/modelling data Command line interface allows rapid description of workflow and reproducibility Sophisticated and customisable graphics Concise and consistent method using square brackets (e.g. map1[x > y,] ) Very wide range of functions through additional packages Integrated processing, analysis, and modelling framework Steep learning curve (eased by RStudio) No dynamic zoomable canvas Difficult to dynamically select objects from map Only single core processing Sometimes more than one solution available

13 Introduction to spatial analysis in R

14 Spatial analysis in R

15 Reproducibility Science: knowledge covering general truths of the operation of general laws, esp. as obtained and tested through scientific method [and] concerned with the physical world (Webster's New Collegiate Dictionary) The gold standard for science is reproducibility. Ideally, research results are only worthy of attention, publication, and citation based if independent researchers can replicate them using a particular study s methods and materials. The Scientist Share the code!

16 R scripts library(rvest) library(ggplot2) tunni_temp <- read_html(" tbl <- tunni_temp %>% html_nodes("table") %>%.[[1]] %>% html_table(fill=t) jaamad_geocode2 <- read.csv(" sep=",", dec=".") tbl2 <- tbl[2:86,] ohutemp <- tbl2[,1:2] ohutemp <- as.list(tbl2[,2]) # temperatuuri jaoks 2!!! ohutemp <- t(as.data.frame(ohutemp)) ohutemp <- as.data.frame(ohutemp) ohutemp$v1 <- as.numeric(gsub(",", ".", ohutemp$v1)) ohutemp_fin <- as.data.frame(cbind(jaamad_geocode2, ohutemp)) ohutemp_fin2 <- ohutemp_fin ohutemp_fin3 <- na.omit(ohutemp_fin2) library(gstat) library(sp) library(maptools) ohutemp_fin <- na.omit(ohutemp_fin) coordinates(ohutemp_fin) = ~lon + lat x.range <- as.numeric(c(21.76, 28.21)) # min/max longitude of the interpolation area y.range <- as.numeric(c(57.45, 59.8)) # min/max latitude of the interpolation area grd <- expand.grid(x = seq(from = x.range[1], to = x.range[2], by = 0.02), y = seq(from = y.range[1], to = y.range[2], by = 0.02)) #expand points to grid coordinates(grd) <- ~x + y gridded(grd) <- TRUE idw <- idw(formula = V1 ~ 1, locations = ohutemp_fin, newdata = grd) # apply idw model for the data idw.output = as.data.frame(idw) # output is defined as a data table names(idw.output)[1:3] <- c("long", "lat", "var1.pred") est_mask <- read.csv(" sep=";") library("rcolorbrewer") mypalette <- colorramppalette(rev(brewer.pal(11, "Spectral"))) t_min <- min(ohutemp_fin3$v1) -1 t_max <- max(ohutemp_fin3$v1) + 1

17 50 lines of code 1440 still images One command to create the animation