predictpycno: Estimate populations for a set of zones based on...

In pycno: Pycnophylactic Interpolation

Estimate populations for a set of zones based on pycnophylactic interpolation

Description

Given a SpatialGridDataFrame of population estimates and a set of polygons, compute a population estimate based on Tobler's pycnophylactic interpolation algorithm for each zone. The result is a vector.

Usage

estimate.pycno(sgdf, spdf)

Arguments

sgdf	A SpatialGridDataFrame containing the ouput of a pycnophylatic interpolation, such as those produced by pycno.
spdf	A SpatialPolygonsDataFrame, giving the polygons for which estimates are wanted.

Details

Takes the estimate of population density for each pixel, checks which polygon each pixel is in, and aggregates them. Accuracy depends on the scale of pixels in the initial interpolation.

Value

A vector in which each each pixel set at the estimated population aggregation to each zone in spdf.

Note

Pycnophylatic interpolation has the property that the sum of the estimated values associated with all of the pixels in any polygon equals the supplied population for that polygon. A further property is that all pixel values are greater than or equal to zero. The method is generally used to obtain pixel-based population estimates when total populations for a set of irregular polygons (eg. counties) are known.

Author(s)

Chris Brunsdon

References

Tobler, W.R. (1979) Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, v74(367) pp. 519-530.

See Also

pycno

Examples

library(sp)
# Read in data for North Carolina as a SpatialPolygonsDataFrame
#nc.sids <- readShapeSpatial(system.file("shapes/sids.shp", package="maptools")[1], 
#  IDvar="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))

nc.sids <- as(sf::st_read(system.file("shape/nc.shp", package="sf")), "Spatial")
row.names(nc.sids) <- as.character(nc.sids$FIPSNO)

# Compute the pycnophylactic surface for 1974 births as a SpatialGridDataFrame
# Note probably shouldn't really base grid cells on Lat/Long coordinates
# This example just serves to illustrate the use of the functions
births74 <- pycno(nc.sids,nc.sids$BIR74,0.05,converge=1)

# Create a new 'blocky' set of zones
#blocks <- gUnionCascaded(nc.sids,1*(coordinates(nc.sids)[,2] > 36) + 
#  2*(coordinates(nc.sids)[,1] > -80))

crds <- sf::st_coordinates(sf::st_centroid(sf::st_geometry(sf::st_as_sf(nc.sids)),
 of_largest_polygon = TRUE))
block_ID <- 1*(crds[,2] > 36) + 2*(crds[,1] > -80)
temp <- sf::st_as_sf(nc.sids)
temp$block_ID <- block_ID
blocks <- as(aggregate(temp, by=list(temp$block_ID), head, n=1), "Spatial")

# Plot the blocky zones
plot(blocks)
# Aggregate data to them
estimates <- estimate.pycno(births74,blocks)
# Write the estimates on to the map
text(coordinates(blocks),as.character(estimates))

pycno documentation built on Sept. 28, 2023, 5:08 p.m.