Aggregate populations to the specified areal level

Takes simulated populations and aggregates them to the specified areal level. Also calculates the aggregated risk and prevalence.

Usage

pixelPopToArea(
  pixelLevelPop,
  eaSamples,
  areas,
  stratifyByUrban = TRUE,
  targetPopMat = NULL,
  doFineScaleRisk = !is.null(pixelLevelPop$fineScaleRisk$p),
  doSmoothRisk = !is.null(pixelLevelPop$smoothRisk$p)
)

areaPopToArea(
  areaLevelPop,
  areasFrom,
  areasTo,
  stratifyByUrban = TRUE,
  doFineScaleRisk = !is.null(areaLevelPop$aggregationResults$pFineScaleRisk),
  doSmoothRisk = !is.null(areaLevelPop$aggregationResults$pSmoothRisk)
)

Arguments

pixelLevelPop: pixel level population information that we want aggregate. In the same format as output from simPopCustom
eaSamples: nIntegrationPoint x nsim matrix of the number of enumeration areas per pixel sampled in the input pixel level population
areas: character vector of length nIntegrationPoints of area names over which we want to aggregate. Can also be subareas
stratifyByUrban: whether or not to stratify simulations by urban/rural classification
targetPopMat: pixellated grid data frame with variables `lon`, `lat`, `pop` (target population), `area`, `subareas` (if subareaLevel is TRUE), `urban` (if stratifyByUrban is TRUE), `east`, and `north`
doFineScaleRisk: whether or not to calculate the fine scale risk in addition to the prevalence. See details
doSmoothRisk: Whether or not to calculate the smooth risk in addition to the prevalence. See details
areaLevelPop: output of simPopCustom containing pixel level information about the population of interest
areasFrom: character vector of length equal to the number of areas from which we would like to aggregate containing the unique names of the areas. Can also be subareas, but these are smaller than the "to areas", and each "from area" must be entirely contained in a single "to area"
areasTo: character vector of length equal to the number of areas from which we would like to aggregate containing the names of the areas containing with each respective `from' area. Can also be a set of subareas, but these are larger than the "from areas".

Value

A list containing elements `fineScalePrevalence` and `fineScaleRisk`. Each of these are in turn lists with aggregated prevalence and risk for the area of interest, containg the following elements, were paranethesis indicate the elements for the fineScaleRisk model rather than fineScalePrevalence:

p: Aggregated prevalence (risk), calculated as aggregate of Z divided by aggregate of N
Z: Aggregated (expected) population numerator
N: Aggregated (expected) population denominator
pUrban: Aggregated prevalence (risk) in urban part of the area, calculated as aggregate of Z divided by aggregate of N
ZUrban: Aggregated (expected) population numerator in urban part of the area
NUrban: Aggregated (expected) population denominator in urban part of the area
pRural: Aggregated prevalence (risk) in rural part of the area, calculated as aggregate of Z divided by aggregate of N
ZRural: Aggregated (expected) population numerator in rural part of the area
NRural: Aggregated (expected) population denominator in rural part of the area
A: Aggregation matrix used to aggregate from pixel level to areal level
AUrban: Aggregation matrix used to aggregate from pixel level to urban part of the areal level
ARural: Aggregation matrix used to aggregate from pixel level to rural part of the areal level

Functions

pixelPopToArea(): Aggregate from pixel to areal level
areaPopToArea(): Aggregate areal populations to another areal level

References

In Preparation

Author

John Paige

Examples

if (FALSE) { # \dontrun{
##### Now we make a model for the risk. We will use an SPDE model with these 
##### parameters for the linear predictor on the logist scale, which are chosen 
##### to be of practical interest:
beta0=-2.9 # intercept
gamma=-1 # urban effect
rho=(1/3)^2 # spatial variance
effRange = 400 # effective spatial range in km
sigmaEpsilon=sqrt(1/2.5) # cluster (nugget) effect standard deviation

# simulate the population! Note that this produces multiple dense 
# nEA x nsim and nIntegrationPoint x nsim matrices. In the future 
# sparse matrices will and chunk by chunk computations may be incorporated.
simPop = simPopSPDE(nsim=1, easpa=easpaKenyaNeonatal, 
                    popMat=popMatKenya, targetPopMat=popMatKenyaNeonatal, 
                    poppsub=poppsubKenya, spdeMesh=kenyaMesh, 
                    margVar=rho, sigmaEpsilonSq=sigmaEpsilon^2, 
                    gamma=gamma, effRange=effRange, beta0=beta0, 
                    seed=123, inla.seed=12, nHHSampled=25, 
                    stratifyByUrban=TRUE, subareaLevel=TRUE, 
                    doFineScaleRisk=TRUE, 
                    min1PerSubarea=TRUE)

pixelPop = simPop$pixelPop
subareaPop = pixelPopToArea(pixelLevelPop=pixelPop, eaSamples=pixelPop$eaSamples, 
  areas=popMatKenya$subarea, stratifyByUrban=TRUE, 
  targetPopMat=popMatKenyaNeonatal, doFineScaleRisk=TRUE)

# get areas associated with each subarea for aggregation
tempAreasFrom = popMatKenya$subarea
tempAreasTo = popMatKenya$area
areasFrom = sort(unique(tempAreasFrom))
areasToI = match(areasFrom, tempAreasFrom)
areasTo = tempAreasTo[areasToI]

# do the aggregation from subareas to areas
outAreaLevel = areaPopToArea(areaLevelPop=subareaPop, 
  areasFrom=areasFrom, areasTo=areasTo, 
  stratifyByUrban=TRUE, doFineScaleRisk=TRUE)
} # }