OpasnetUtils (version 1.1.0)

GIS: Opasnet spatially distributed data interfaces

Description

Currently there are only two GIS functions: one for producing spatial concentration maps (GIS.Concentration.matrix) and one for using spatial population data and concentration maps to calculate exposure (GIS.Exposure).

Usage

GIS.Concentration.matrix(Emission, LO, LA, distx = 10.5, disty = 10.5, 
    resolution = 1, N = 1000, dbug = FALSE, ...)
GIS.Exposure(Concentration.matrix, dbug = FALSE, ...)

Arguments

Emission

numeric, emission in any units; can be an ovariable as well

Concentration.matrix

ovariable concentration matrix, usually produced by GIS.Concentration.matrix

LO

numeric, longitude corresponding to the center of the considered area (emission source)

LA

numeric, latitude corresponding to the center of the considered area (emission source)

distx

numeric, maximum distance from center on the x axis of the area, 10.5 corresponds to the source-receiver-matrices used

disty

numeric, maximum distance from center on the y axis of the area, 10.5 corresponds to the source-receiver-matrices used

resolution

numeric, size of the grid, default 1 is 1km x 1km grid

N

integer, number of iterations to run

dbug

use TRUE to turn debug prints on

...

excess arguments are ignored or passed to tidy on data download

Value

• GIS.Concentration.matrix returns an ovariable whose output is a grid defined as bins for coordinates. GIS.Exposure returns an ovariable whose output is concentration * population. All population data that matches cells defined by the concentration matrix is included in the output.

Details

The concentration matrix is computed using PILTTI source-receiver-matrices (http://en.opasnet.org/w/Piltti_source-receptor_matrix). They are originally for modeling PM2.5 distributions in a few Finnish cities between the years 2000 and 2003. To produce a rudimentary probability distribution these matrices are randomized between iterations. Exposure is calculated by matching a concentration matrix to Finnish population data (http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en2949.2012). LA and LO are not required arguments for exposure, but speed the computation significantly. See also: http://en.opasnet.org/

Examples

# Excerpt from http://en.opasnet.org/w/Health_impacts_of_fine_particles_in_Rauma 
# (not evaluated)

# Paasto Emissions

Paasto <- new(
	"ovariable", 
	name = "Paasto",
	dependencies = data.frame(Name = "tieliikennepaastot", Key = "0194s0uuucjxq8Wi"),
	formula = function(dependencies, ...) {
		ComputeDependencies(dependencies, ...)
		
		# Muutetaan paivapaasto vuosipaastoksi ja grammat tonneiksi
		out <- tieliikennepaastot * 365 * 1E-6 

		return(out)
	}
)

# Muita tarpeellisia arvoja Other important values

bg.mort <- 45182 / 5203826 # same values as used in PILTTI

## J. T. Tuomisto, A. Wilson, et al. Uncertainty in mortality response to 
## airborne fine particulate matter... 2008
erf <- 0.0097 
# unit: m^3 /ug

# Ovariablet

## Pitoisuudet Concentrations

Pitoisuus <- new(
	"ovariable", 
	name = "Pitoisuus",
	dependencies = data.frame(
		Name = c("Paasto", "LO", "LA")
	),
	formula =  function(dependencies, ...) {
		ComputeDependencies(dependencies, ...)

		temp <- GIS.Concentration.matrix(Paasto, LO, LA, ...)

		return(temp)
	} 
)

## Altistuminen Exposure

Altistuminen <- new(
	"ovariable", 
	name = "Altistuminen", 
	dependencies = data.frame(
		Name = c("Pitoisuus", "LO", "LA")
	),
	formula = function(dependencies, ...) {
		ComputeDependencies(dependencies, ...)

		out <- GIS.Exposure(Pitoisuus, LO, LA, ...)

		return(out)
	}
)