Description Usage Format Details Source References Examples
New York leukemia data taken from the data sets supporting Waller and Gotway 2004 (the data should be loaded by running example(NY_data)
to demonstrate spatial data import techniques).
1 |
A data frame with 281 observations on the following 12 variables, and the binary coded spatial weights used in the source.
AREANAME
name of census tract
AREAKEY
unique FIPS code for each tract
X
x-coordinate of tract centroid (in km)
Y
y-coordinate of tract centroid (in km)
POP8
population size (1980 U.S. Census)
TRACTCAS
number of cases 1978-1982
PROPCAS
proportion of cases per tract
PCTOWNHOME
percentage of people in each tract owning their own home
PCTAGE65P
percentage of people in each tract aged 65 or more
Z
ransformed propoprtions
AVGIDIST
average distance between centroid and TCE sites
PEXPOSURE
"exposure potential": inverse distance between each census tract centroid and the nearest TCE site, IDIST, transformed via log(100*IDIST)
The examples section shows how the DBF files from the book website for Chapter 9 were converted into the nydata
data frame and the listw_NY
spatial weights list.
http://web1.sph.emory.edu/users/lwaller/ch9index.htm
Waller, L. and C. Gotway (2004) Applied Spatial Statistics for Public Health Data. New York: John Wiley and Sons.
1 2 3 4 5 6 7 8 9 10 11 | ## NY leukemia
library(foreign)
nydata <- read.dbf(system.file("etc/misc/nydata.dbf", package="spdep")[1])
coordinates(nydata) <- c("X", "Y")
nyadjmat <- as.matrix(read.dbf(system.file("etc/misc/nyadjwts.dbf",
package="spdep")[1])[-1])
ID <- as.character(names(read.dbf(system.file("etc/misc/nyadjwts.dbf",
package="spdep")[1]))[-1])
identical(substring(ID, 2, 10), substring(as.character(nydata$AREAKEY), 2, 10))
nyadjlw <- mat2listw(nyadjmat, as.character(nydata$AREAKEY))
listw_NY <- nb2listw(nyadjlw$neighbours, style="B")
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Loading required package: sp
Loading required package: Matrix
Field name: 'Z600701190' changed to: 'Z600701190.1'
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Field name: 'Z606701680' changed to: 'Z606701680.1'
Field name: 'Z606701690' changed to: 'Z606701690.1'
Field name: 'Z600701190' changed to: 'Z600701190.1'
Field name: 'Z600701190' changed to: 'Z600701190.2'
Field name: 'Z600701200' changed to: 'Z600701200.1'
Field name: 'Z600701210' changed to: 'Z600701210.1'
Field name: 'Z600701210' changed to: 'Z600701210.2'
Field name: 'Z600701220' changed to: 'Z600701220.1'
Field name: 'Z600701270' changed to: 'Z600701270.1'
Field name: 'Z600701320' changed to: 'Z600701320.1'
Field name: 'Z600701330' changed to: 'Z600701330.1'
Field name: 'Z605303010' changed to: 'Z605303010.1'
Field name: 'Z605303010' changed to: 'Z605303010.2'
Field name: 'Z605303040' changed to: 'Z605303040.1'
Field name: 'Z605303040' changed to: 'Z605303040.2'
Field name: 'Z605303050' changed to: 'Z605303050.1'
Field name: 'Z606700170' changed to: 'Z606700170.1'
Field name: 'Z606700360' changed to: 'Z606700360.1'
Field name: 'Z606700560' changed to: 'Z606700560.1'
Field name: 'Z606700610' changed to: 'Z606700610.1'
Field name: 'Z606700610' changed to: 'Z606700610.2'
Field name: 'Z606701100' changed to: 'Z606701100.1'
Field name: 'Z606701110' changed to: 'Z606701110.1'
Field name: 'Z606701120' changed to: 'Z606701120.1'
Field name: 'Z606701120' changed to: 'Z606701120.2'
Field name: 'Z606701120' changed to: 'Z606701120.3'
Field name: 'Z606701140' changed to: 'Z606701140.1'
Field name: 'Z606701520' changed to: 'Z606701520.1'
Field name: 'Z606701650' changed to: 'Z606701650.1'
Field name: 'Z606701680' changed to: 'Z606701680.1'
Field name: 'Z606701690' changed to: 'Z606701690.1'
[1] TRUE
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