pycno-package(pycno)
pycno-package()所属R语言包:pycno
Pycnophylactic Interpolation
Pycnophylactic插值
译者:生物统计家园网 机器人LoveR
描述----------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.
给定一个SpatialPolygonsDataFrame和种群的一组为每个多边形,计算人口密度估计的基础上托布勒的pycnophylactic插值算法。结果是一个SpatialGridDataFrame。
Details
详细信息----------Details----------
</table> 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.
</表>的sp包的结合使用,计算给予的SpatialPolygonsDataFrame和为每个poplygon人口pycnophylactic表面的。甲pycnophylactic表面是光滑的,但种群分配给每个像素总和的初始多边形计数超过包含在每个像素中的多边形,当求和。
(作者)----------Author(s)----------
Chris Brunsdon
Maintainer: Chris Brunsdon <a href="mailto:cb179@le.ac.uk">cb179@le.ac.uk</a>
参考文献----------References----------
Tobler, W.R. (1979) Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, v74(367) pp. 519-530.
实例----------Examples----------
# Read in data for North Carolina as a SpatialPolygonsDataFrame[在北卡罗莱纳州的数据读取一个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[1974年出生,作为SpatialGridDataFrame计算的pycnophylactic表面]
# Note probably shouldn't really base grid cells on Lat/Long coordinates[注意应该没有真正的基础网格单元上的纬度/经度坐标]
# This example just serves to illustrate the use of the function[这个例子正好说明了使用的功能]
births74 <- pycno(nc.sids,nc.sids$BIR74,0.05)
# Draw it[它画]
image(births74)
# Overlay North Carolina county boundaries for reference[覆盖北卡罗莱纳州的县界,以供参考]
plot(nc.sids,add=TRUE)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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