OpasnetUtils/GIS

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Latitude of the emission site: Longitude of the emission site: Emission of PM2.5 (Mg/a or tons per year): + Show code- Hide code library(OpasnetUtils) concentration <- GIS.Concentration.matrix(Emission = emissions, LA = LA, LO = LO) oprint(head(concentration@output)) exposure <- GIS.Exposure(concentration, LA = LA, LO = LO) oprint(head(exposure@output))

Description

This section contains a couple of general GIS related functions

Code

https://www.opasnet.org/svn/opasnet_utils/trunk/R/GIS%20methods.r

Kehitysehdotus

Tarkastellaan GIS.Exposure-funktion tätä osaa:

temp <- Population * Concentration.matrix
	
	if(dbug) cat(colnames(temp@output), "\n")
	
	#temp <- oapply(temp, cols = c("LObin", "LAbin"), sum)
	# Sum over spatial data.
	out <- tapply(
		temp@output$Result, 
		temp@output[,colnames(temp@output)[temp@marginal & !colnames(temp@output) %in% c("LObin", "LAbin")]], 
		sum,
		na.rm = TRUE
	)

Väestö * pitoisuus -sarakkeen lisäksi lasketaan

• Ryhmittely quantiles kvantiiliin pitoisuuden perusteella. Oletusarvo on 1 eli nykyinen käytäntö. Jos quantiles = 0, ei tapplyta lainkaan vaan lasketaan alkuperäisillä riveillä. Joka tapauksessa kuitenkin LO ja LA poistetaan, koska paikkatietoa ei haluta antaa funktion läpi. --# : Koska kvantiilit voivat muiden indeksien takia olla hyvinkin erisuuruisia, voi kuitenkin olla järkevämpää laskea yksinkertaisesti cut:lla pilkottuja tasalevyisiä pitoisuusluokkia. --Jouni 10:46, 17 March 2013 (EET)
• tapplytaan yllä olevalla ehdolla mutta lisättynä juuri luodulla kvantiilisarakkeella:
  • väestö (funktio: sum)
  • pitoisuus (funktio: mean)

Tällä tavalla saadaan output, jossa on tarvittavat tiedot HIA-laskentaan, esim. Exposures in Finland-tyyppiseen tauluun Exposure, ja väestön koko.

Functions

GIS.Exposure

Exposure computes exposure using a given concentration matrix and protected population data from Heande.

Inputs:

• Concentration.matrix - A matrix containing spatially dependent concentratio data;
• LO & LA - coordinates of the center of the concentration matrix;
• distx & disty - maximum displacement from center of concentration matrix, assumed symmetrical, defaults to 10.5 km (Piltti source-receptor matrix);
• resolution - resolution of concentration matrix, length of side of grid element which are assumed squares, defaults to 1 km (Piltti source-receptor matrix)

Output:

• An ovariable containing result of Population * Concentration. Output and marginal slots are defined. Spatial information lost in summation (though there is an easy way around it).

GIS.Concentration.matrix

Computes a concentration matrix from given emission and coordinates, based on random sampling Piltti source-receptor matrix.

Inputs:

• Emission - emission of substance in Mga^-1;
• LO & LA - coordinates where emission occurs;
• distx & disty - maximum displacement in kilometers from center of desired matrix, assumed symmetrical, defaults to 10.5 km (Piltti source-receptor matrix);
• resolution - resolution of desired matrix (length in kilometers of side of grid element which are assumed squares), defaults to 1 km (Piltti source-receptor matrix);
• N - number of iterations to be run

Output:

• An ovariable containing spatially dependent concentration data. Output and marginal slots are defined.

See also

• OpasnetBaseUtils
• Object-oriented programming in Opasnet
• Opasnet (R library)
• Piltti source-receptor matrix

Calculations

This code creates a function that is used to plot a concentration field on a Google map.

+ Show code- Hide code library(OpasnetUtils) # Function concmap draws a concentration field on a dynamic Google map. concmap <- function(Pitoisuus, ncol_rast = 41, nrow_rast = 41, ...) { # Required packages: #library(OpasnetUtils) #library(ggplot2) #library(xtable) #library(OpasnetUtilsExt) #library(rgdal) #library(maptools) #library(RColorBrewer) #library(classInt) #library(raster) #par(mfrow=c(6,1), mar=c(3,1,0,1), cex=1.5) # Graphics settings cat("Concentration field.\n") colorstrip <- function(colors, labels) { count <- length(colors) m <- matrix(1:count, count, 1) image(m, col=colors, ylab="", axes=FALSE) axis(1,approx(c(0, 1), n=length(labels))$y, labels) } # x is a factor with ranges. Function range2sum averages the ranges and gives averages as the output. range2num <- function(x) { temp <- gsub("[\\(\\)\\[ ]|\\]", "", levels(x)) # Remove characters "()[] " temp <- strsplit(temp, ",") # Split into two from the comma (assuming exactly one comma) temp2 <- c() for(i in 1:length(temp)) { a <- as.numeric(temp[[i]][1]) b <- as.numeric(temp[[i]][2]) temp2[i] <- ((a + b) / 2) # Average of the lower and upper part of range. } levels(x) <- temp2 x <- as.numeric(as.character(x)) return(x) } data <- data.frame( # Create the data.frame that is used as the input for the concentration field. LO = range2num(Pitoisuus@output$LObin), LA = range2num(Pitoisuus@output$LAbin), log_concentration = (log10(Pitoisuus@output$PitoisuusResult) - log10(0.001)) / 4 # Miksi jaetaan neljällä? ) # Plot the data coordinates(data) <- c("LO","LA") proj4string(data) <- ("+init=epsg:4326") epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") shp <- spTransform(data,epsg4326String) #Create blank raster rast<-raster() #Set raster extent to that of point data extent(rast) <- extent(shp) #Choose number of columns and rows ncol(rast) <- ncol_rast nrow(rast) <- nrow_rast #Rasterize point data rast2 <- rasterize(shp, rast, shp$log_concentration, fun=mean) steps <- c(0.001,0.003,0.01,0.03,0.1,0.3,1,3,10,30) steps <- c(steps[steps < max(data$log_concentration)], max(data$log_concentration)) # Limit upper part to max value. colors <- rev(rainbow(length(steps), start=0, end=0.50)) cat(colorstrip(colors, steps)) # Plot the colorstrip (legend). #Plot data return(google.show_raster_on_maps(rast2, col=colors, style="height:500px;")) } objects.store(concmap) cat("Object concmap stored. It plots a concentration field on a Google map.\n")