| Title: | Pycnophylactic Interpolation |
|---|---|
| Description: | Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame. Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>. |
| Authors: | Chris Brunsdon |
| Maintainer: | Francesca Bitonti <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.4 |
| Built: | 2024-12-10 06:43:10 UTC |
| Source: | CRAN |
Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame.
| Package: | pycno |
| Type: | Package |
| Version: | 1.4 |
| Date: | 2023-09-28 |
| License: | GPL (>=2) |
| LazyLoad: | yes |
For use in conjunction with the sp package, computes pycnophylactic surfaces given a SpatialPolygonsDataFrame and a population for each poplygon. A pycnophylactic surface is smooth, but populations allocated to each pixel sum up to the initial polygon counts, when summed over the polygons contained in each pixel.
Chris Brunsdon
Maintainer: Francesca Bitonti [email protected]
Tobler, W.R. (1979) Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, v74(367) pp. 519-530.
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$FIPSNO) # Compute the pycnophylactic surface for 1974 births as a SpatialGridDataFrame # Note probably shouldn't really base grid cells on Lat/Long coordinates # This example just serves to illustrate the use of the function births74 <- pycno(nc.sids,nc.sids$BIR74,0.05, converge=1) # Draw it image(births74) # Overlay North Carolina county boundaries for reference plot(nc.sids,add=TRUE)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$FIPSNO) # Compute the pycnophylactic surface for 1974 births as a SpatialGridDataFrame # Note probably shouldn't really base grid cells on Lat/Long coordinates # This example just serves to illustrate the use of the function births74 <- pycno(nc.sids,nc.sids$BIR74,0.05, converge=1) # Draw it image(births74) # Overlay North Carolina county boundaries for reference plot(nc.sids,add=TRUE)
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.
estimate.pycno(sgdf, spdf)estimate.pycno(sgdf, spdf)
sgdf |
A |
spdf |
A |
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.
A vector in which each each pixel set at the estimated population aggregation to each zone in spdf.
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.
Chris Brunsdon
Tobler, W.R. (1979) Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, v74(367) pp. 519-530.
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))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))
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
compute a population density estimate based on Tobler's pycnophylactic interpolation
algorithm. The result is a SpatialGridDataFrame.
pycno(x, pops, celldim, r = 0.2, converge = 3, verbose=TRUE)pycno(x, pops, celldim, r = 0.2, converge = 3, verbose=TRUE)
x |
A |
pops |
A list of populations, one for each zone represented in |
celldim |
The size of the cell sides for the output SpatialGridDataFrame, or a SpatialGrid or SpatialGridDataFrame with the grid topology of the desired output. |
r |
A relaxation parameter for the iterative step in the pycnophylactic algorithm. Prevents over-compensation in the smoothing step. In practice the default value works well. |
converge |
A convergence parameter, informing the decision on when iterative improvements on the smooth surface have converged sufficiently - see details. |
verbose |
If set to |
This method uses an iterative approach, and for each iteration notes the maximum change in a pixel. When this value falls below a certain level (actually 10^(-converge) times the largest initial pixel value) the iterations stop and the current surface is reported.
A SpatialGridDataFrame with each pixel set at the estimated density computed with the Pycnophylactic algorithm, these values being stored in the data item dens. The extent of this object is set at by the bounding box of the input SpatialPolygonsDataFrame. Pixels outside of all of the polygons are set to NA.
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.
Chris Brunsdon
Tobler, W.R. (1979) Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, v74(367) pp. 519-530.
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$FIPSNO) # Compute the pycnophylactic surface for 1974 births as a SpatialGridDataFrame # Note probably shouldn't really base grid cells on Lat/Long coordinates # This example just serves to illustrate the use of the function # It is suggested to use a hihger value for 'converge' - this value justs speeds # things up for the example. births74 <- pycno(nc.sids,nc.sids$BIR74,0.05,converge=1) # Draw it image(births74) # Overlay North Carolina county boundaries for reference plot(nc.sids,add=TRUE)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$FIPSNO) # Compute the pycnophylactic surface for 1974 births as a SpatialGridDataFrame # Note probably shouldn't really base grid cells on Lat/Long coordinates # This example just serves to illustrate the use of the function # It is suggested to use a hihger value for 'converge' - this value justs speeds # things up for the example. births74 <- pycno(nc.sids,nc.sids$BIR74,0.05,converge=1) # Draw it image(births74) # Overlay North Carolina county boundaries for reference plot(nc.sids,add=TRUE)