# Perform Tobler's pycnophylactic algorithm.

### 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`

.

### Usage

1 |

### Arguments

`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 |

### Details

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.

### Value

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.

### 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.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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"))
# 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)
``` |

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