Introduction to the North Carolina SIDS data set (re-revised)

Introduction

This data set was presented first in Symons, Grimson, and Yuan (1983), analysed with reference to the spatial nature of the data in Cressie and Read (1985), expanded in Cressie and Chan (1989), and used in detail in Cressie (1991). It is for the 100 counties of North Carolina, and includes counts of numbers of live births (also non-white live births) and numbers of sudden infant deaths, for the July 1, 1974 to June 30, 1978 and July 1, 1979 to June 30, 1984 periods. In Cressie and Read (1985), a listing of county neighbours based on shared boundaries (contiguity) is given, and in Cressie and Chan (1989), and in Cressie (1991, 386–89), a different listing based on the criterion of distance between county seats, with a cutoff at 30 miles. The county seat location coordinates are given in miles in a local (unknown) coordinate reference system. The data are also used to exemplify a range of functions in the spatial statistics module user’s manual (Kaluzny et al. 1996).

Getting the data into R

We will be using the spdep and spreg packages, here version: spdep, version 1.3-6, 2024-08-31, the sf package and the tmap package. The data from the sources referred to above is documented in the help page for the nc.sids data set in spData. The actual data, included in a shapefile of the county boundaries for North Carolina were made available in the maptools package ¹. These data are known to be geographical coordinates (longitude-latitude in decimal degrees) and are assumed to use the NAD27 datum. The ESRI Shapefile is deprecated, and was replaced here by a GeoPackage, written from reading the original files in spData 2.3.1:

library(spdep)
nc <- st_read(system.file("shapes/sids.gpkg", package="spData")[1], quiet=TRUE)
#st_crs(nc) <- "EPSG:4267"
row.names(nc) <- as.character(nc$FIPSNO)

The shapefile format presupposed that you had three files with extensions .shp, .shx, and .dbf, where the first contains the geometry data, the second the spatial index, and the third the attribute data. They were required to have the same name apart from the extension, and were read here using sf::st_read() into the sf object nc; the class is defined in sf. The centroids of the largest polygon in each county are available using the st_centroid method from sf as an sfc POINT object, and can be used to place labels after the extraction of the coordinate matrix:

sf_use_s2(TRUE)
plot(st_geometry(nc), axes=TRUE)
text(st_coordinates(st_centroid(st_geometry(nc), of_largest_polygon=TRUE)), label=nc$FIPSNO, cex=0.5)

We can examine the names of the columns of the data frame to see what it contains — in fact some of the same columns that we will be examining below, and some others which will be useful in cleaning the data set.

names(nc)

##  [1] "CNTY_ID"   "AREA"      "PERIMETER" "CNTY_"     "NAME"      "FIPS"     
##  [7] "FIPSNO"    "CRESS_ID"  "BIR74"     "SID74"     "NWBIR74"   "BIR79"    
## [13] "SID79"     "NWBIR79"   "east"      "north"     "x"         "y"        
## [19] "lon"       "lat"       "L_id"      "M_id"      "geom"

summary(nc)

##     CNTY_ID          AREA          PERIMETER         CNTY_     
##  Min.   :1825   Min.   :0.0420   Min.   :0.999   Min.   :1825  
##  1st Qu.:1902   1st Qu.:0.0910   1st Qu.:1.324   1st Qu.:1902  
##  Median :1982   Median :0.1205   Median :1.609   Median :1982  
##  Mean   :1986   Mean   :0.1263   Mean   :1.673   Mean   :1986  
##  3rd Qu.:2067   3rd Qu.:0.1542   3rd Qu.:1.859   3rd Qu.:2067  
##  Max.   :2241   Max.   :0.2410   Max.   :3.640   Max.   :2241  
##      NAME               FIPS               FIPSNO         CRESS_ID     
##  Length:100         Length:100         Min.   :37001   Min.   :  1.00  
##  Class :character   Class :character   1st Qu.:37050   1st Qu.: 25.75  
##  Mode  :character   Mode  :character   Median :37100   Median : 50.50  
##                                        Mean   :37100   Mean   : 50.50  
##                                        3rd Qu.:37150   3rd Qu.: 75.25  
##                                        Max.   :37199   Max.   :100.00  
##      BIR74           SID74          NWBIR74           BIR79      
##  Min.   :  248   Min.   : 0.00   Min.   :   1.0   Min.   :  319  
##  1st Qu.: 1077   1st Qu.: 2.00   1st Qu.: 190.0   1st Qu.: 1336  
##  Median : 2180   Median : 4.00   Median : 697.5   Median : 2636  
##  Mean   : 3300   Mean   : 6.67   Mean   :1051.0   Mean   : 4224  
##  3rd Qu.: 3936   3rd Qu.: 8.25   3rd Qu.:1168.5   3rd Qu.: 4889  
##  Max.   :21588   Max.   :44.00   Max.   :8027.0   Max.   :30757  
##      SID79          NWBIR79             east           north      
##  Min.   : 0.00   Min.   :    3.0   Min.   : 19.0   Min.   :  6.0  
##  1st Qu.: 2.00   1st Qu.:  250.5   1st Qu.:178.8   1st Qu.: 97.0  
##  Median : 5.00   Median :  874.5   Median :285.0   Median :125.5  
##  Mean   : 8.36   Mean   : 1352.8   Mean   :271.3   Mean   :122.1  
##  3rd Qu.:10.25   3rd Qu.: 1406.8   3rd Qu.:361.2   3rd Qu.:151.5  
##  Max.   :57.00   Max.   :11631.0   Max.   :482.0   Max.   :182.0  
##        x                 y             lon              lat       
##  Min.   :-328.04   Min.   :3757   Min.   :-84.08   Min.   :33.92  
##  1st Qu.: -60.55   1st Qu.:3920   1st Qu.:-81.20   1st Qu.:35.26  
##  Median : 114.38   Median :3963   Median :-79.26   Median :35.68  
##  Mean   :  91.46   Mean   :3953   Mean   :-79.51   Mean   :35.62  
##  3rd Qu.: 240.03   3rd Qu.:4000   3rd Qu.:-77.87   3rd Qu.:36.05  
##  Max.   : 439.65   Max.   :4060   Max.   :-75.67   Max.   :36.52  
##       L_id           M_id                 geom    
##  Min.   :1.00   Min.   :1.00   MULTIPOLYGON :100  
##  1st Qu.:1.00   1st Qu.:2.00   epsg:4267    :  0  
##  Median :2.00   Median :3.00   +proj=long...:  0  
##  Mean   :2.12   Mean   :2.67                      
##  3rd Qu.:3.00   3rd Qu.:3.25                      
##  Max.   :4.00   Max.   :4.00

Let’s check the different versions of the data against each other - sf and spData have NC SIDS files, as does GeoDa Center in two forms:

library(sf)
nc_sf <- st_read(system.file("shape/nc.shp", package="sf"),
                 quiet=TRUE)
st_crs(nc_sf)

## Coordinate Reference System:
##   User input: NAD27 
##   wkt:
## GEOGCRS["NAD27",
##     DATUM["North American Datum 1927",
##         ELLIPSOID["Clarke 1866",6378206.4,294.978698213898,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4267]]

nc <- st_read(system.file("shapes/sids.shp",
                 package="spData"), quiet=TRUE)
st_crs(nc)

## Coordinate Reference System: NA

As the actual CRS is unknown, spData reports missing, although it may very well be +proj=longlat +datum=NAD27

st_crs(nc) <- "+proj=longlat +datum=NAD27"

Next, are the geometries the same? sf::st_equals returns a logical matrix, so we’ll check that the diagonal values are all TRUE, and that only those values are TRUE by summing and recalling that n is 100:

suppressWarnings(st_crs(nc_sf) <- st_crs(nc))
xx <- st_equals(nc, nc_sf, sparse=FALSE)
all(diag(xx)) && sum(xx) == 100L

## [1] TRUE

Next, let’s download the GeoDa files and repeat the comparisons:

td <- tempdir()
#download.file("https://geodacenter.github.io/data-and-lab//data/sids.zip", file.path(td, "sids.zip"), quiet=TRUE) 
# local copy (2020-10-22) as repository sometimes offline
file.copy(system.file("etc/misc/sids.zip", package="spdep"), td)

## [1] TRUE

unzip(file.path(td, "sids.zip"), c("sids/sids.dbf", "sids/sids.prj", "sids/sids.shp", "sids/sids.shx"), exdir=td)
sids_sf <- st_read(file.path(td, "sids/sids.shp"), quiet=TRUE)
#download.file("https://geodacenter.github.io/data-and-lab//data/sids2.zip", file.path(td, "sids2.zip"), quiet=TRUE)
file.copy(system.file("etc/misc/sids2.zip", package="spdep"), td)

## [1] TRUE

unzip(file.path(td, "sids2.zip"), c("sids2/sids2.dbf", "sids2/sids2.prj", "sids2/sids2.shp", "sids2/sids2.shx"), exdir=td)
sids2_sf <- st_read(file.path(td, "sids2/sids2.shp"), quiet=TRUE)

st_crs(sids_sf)

## Coordinate Reference System:
##   User input: WGS 84 
##   wkt:
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4326]]

st_crs(sids2_sf)

## Coordinate Reference System:
##   User input: WGS 84 
##   wkt:
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4326]]

It looks as though the external files are assuming WGS84/NAD83 for the datum, but also contain the same geometries.

suppressWarnings(st_crs(sids_sf) <- st_crs(nc_sf))
xx <- st_equals(sids_sf, nc_sf, sparse=FALSE)
all(diag(xx)) && sum(xx) == 100L

## [1] FALSE

suppressWarnings(st_crs(sids2_sf) <- st_crs(nc_sf))
xx <- st_equals(sids2_sf, nc_sf, sparse=FALSE)
all(diag(xx)) && sum(xx) == 100L

## [1] FALSE

Now for the contents of the files - sids2 also contains rates, while the file in spData contains the coordinates as given in Cressie (1991), and the parcels of contiguous counties on p. 554, and the aggregations used for median polishing.

all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids_sf)[,1:14])

##  [1] "Names: 12 string mismatches"                                  
##  [2] "Component 4: Modes: numeric, character"                       
##  [3] "Component 4: target is numeric, current is character"         
##  [4] "Component 5: 100 string mismatches"                           
##  [5] "Component 6: Modes: character, numeric"                       
##  [6] "Component 6: target is character, current is numeric"         
##  [7] "Component 7: Mean relative difference: 0.9986388"             
##  [8] "Component 8: Mean relative difference: 64.33901"              
##  [9] "Component 9: Mean relative difference: 0.9979786"             
## [10] "Component 10: Mean relative difference: 156.5427"             
## [11] "Component 11: Mean relative difference: 3.01968"              
## [12] "Component 12: Mean relative difference: 0.9980208"            
## [13] "Component 13: Mean relative difference: 160.8194"             
## [14] "Component 14: Modes: numeric, list"                           
## [15] "Component 14: Attributes: < target is NULL, current is list >"
## [16] "Component 14: target is numeric, current is sfc_MULTIPOLYGON"

all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids2_sf)[,1:14])

##  [1] "Names: 12 string mismatches"                         
##  [2] "Component 4: Modes: numeric, character"              
##  [3] "Component 4: target is numeric, current is character"
##  [4] "Component 5: 100 string mismatches"                  
##  [5] "Component 6: Modes: character, numeric"              
##  [6] "Component 6: target is character, current is numeric"
##  [7] "Component 7: Mean relative difference: 0.9986388"    
##  [8] "Component 8: Mean relative difference: 64.33901"     
##  [9] "Component 9: Mean relative difference: 0.9979786"    
## [10] "Component 10: Mean relative difference: 156.5427"    
## [11] "Component 11: Mean relative difference: 3.01968"     
## [12] "Component 12: Mean relative difference: 0.9980208"   
## [13] "Component 13: Mean relative difference: 160.8194"    
## [14] "Component 14: Mean relative difference: 0.9984879"

The spData data set has some columns reordered and a surprise:

all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(nc)[,c(2,3,4,1,5:14)])

## [1] "Component \"NWBIR74\": Mean relative difference: 0.04891304"

so a difference in NWBIR74:

which(!(nc_sf$NWBIR74 == nc$NWBIR74))

## [1] 21

c(nc$NWBIR74[21], nc_sf$NWBIR74[21])

## [1] 386 368

where spData follows Cressie (1991) and sf and Geoda follow Cressie and Chan (1989) for NWBIR74 in Chowan county.

We will now examine the data set reproduced from Cressie and collaborators, included in spData (formerly in spdep), and add the neighbour relationships used in Cressie and Chan (1989) to the background map as a graph shown in Figure :

gal_file <- system.file("weights/ncCR85.gal", package="spData")[1]
ncCR85 <- read.gal(gal_file, region.id=nc$FIPSNO)
ncCR85

## Neighbour list object:
## Number of regions: 100 
## Number of nonzero links: 492 
## Percentage nonzero weights: 4.92 
## Average number of links: 4.92

gal_file <- system.file("weights/ncCC89.gal", package="spData")[1]
ncCC89 <- read.gal(gal_file, region.id=nc$FIPSNO)
ncCC89

## Neighbour list object:
## Number of regions: 100 
## Number of nonzero links: 394 
## Percentage nonzero weights: 3.94 
## Average number of links: 3.94 
## 2 regions with no links:
## 37055, 37095
## 3 disjoint connected subgraphs

plot(st_geometry(nc), border="grey")
plot(ncCC89, st_centroid(st_geometry(nc), of_largest_polygon), add=TRUE, col="blue")

Printing the neighbour object shows that it is a neighbour list object, with a very sparse structure — if displayed as a matrix, only 3.94% of cells would be filled. Objects of class nb contain a list as long as the number of counties; each component of the list is a vector with the index numbers of the neighbours of the county in question, so that the neighbours of the county with region.id of 37001 can be retreived by matching against the indices. More information can be obtained by using summary() on an nb object. Finally, we associate a vector of names with the neighbour list, through the row.names argument. The names should be unique, as with data frame row names.

r.id <- attr(ncCC89, "region.id")
ncCC89[[match("37001", r.id)]]

## [1] 11 26 29 30 48

r.id[ncCC89[[match("37001", r.id)]]]

## [1] 37033 37081 37135 37063 37037

The neighbour list object records neighbours by their order in relation to the list itself, so the neighbours list for the county with region.id “37001” are the seventeenth, nineteenth, thirty-second, forty-first and sixty-eighth in the list. We can retreive their codes by looking them up in the region.id attribute.

as.character(nc$NAME)[card(ncCC89) == 0]

## [1] "Dare" "Hyde"

We should also note that this neighbour criterion generates two counties with no neighbours, Dare and Hyde, whose county seats were more than 30 miles from their nearest neighbours. The card() function returns the cardinality of the neighbour set. We need to return to methods for handling no-neighbour objects later on. We will also show how new neighbours lists may be constructed in , and compare these with those from the literature.

Probability mapping

Rather than review functions for measuring and modelling spatial dependence in the spdep package, we will focus on probability mapping for disease rates data. Typically, we have counts of the incidence of some disease by spatial unit, associated with counts of populations at risk. The task is then to try to establish whether any spatial units seem to be characterised by higher or lower counts of cases than might have been expected in general terms (Bailey and Gatrell 1995).

An early approach by Choynowski (1959), described by Cressie and Read (1985) and Bailey and Gatrell (1995), assumes, given that the true rate for the spatial units is small, that as the population at risk increases to infinity, the spatial unit case counts are Poisson with mean value equal to the population at risk times the rate for the study area as a whole. Choynowski’s approach folds the two tails of the measured probabilities together, so that small values, for a chosen $\alpha$ , occur for spatial units with either unusually high or low rates. For this reason, the high and low counties are plotted separately below. Note that cut returns a factor labeled with cut intervals.

ch <- choynowski(nc$SID74, nc$BIR74)
nc$ch_pmap_low <- ifelse(ch$type, ch$pmap, NA)
nc$ch_pmap_high <- ifelse(!ch$type, ch$pmap, NA)
prbs <- c(0,.001,.01,.05,.1,1)
nc$high = cut(nc$ch_pmap_high, prbs)
nc$low = cut(nc$ch_pmap_low,prbs )

is_tmap <- FALSE
if (require(tmap, quietly=TRUE)) is_tmap <- TRUE
is_tmap

## [1] TRUE

library(tmap)
tmap4 <- packageVersion("tmap") >= "3.99"
if (tmap4) {
  tm_shape(nc) + tm_polygons(fill=c("low", "high"), fill.scale = tm_scale(values="brewer.set1"), fill.legend = tm_legend("p-values", frame=FALSE, item.r = 0), fill.free=FALSE, lwd=0.01) + tm_layout(panel.labels=c("low", "high"))
} else {
tm_shape(nc) + tm_fill(c("low", "high"), palette="Set1", title="p-values") +
  tm_facets(free.scales=FALSE) + tm_layout(panel.labels=c("low", "high"))
}

For more complicated thematic maps, it may be helpful to use ColorBrewer (https://colorbrewer2.org) colour palettes. Here we use palettes accessed through tmap, available in R in the RColorBrewer package.

While the choynowski() function only provides the probability map values required, the probmap() function returns raw (crude) rates, expected counts (assuming a constant rate across the study area), relative risks, and Poisson probability map values calculated using the standard cumulative distribution function ppois(). This does not fold the tails together, so that counties with lower observed counts than expected, based on population size, have values in the lower tail, and those with higher observed counts than expected have values in the upper tail, as we can see.

pmap <- probmap(nc$SID74, nc$BIR74)
nc$pmap <- pmap$pmap

brks <- c(0,0.001,0.01,0.025,0.05,0.95,0.975,0.99,0.999,1)
if (tmap4) {
  tm_shape(nc) + tm_polygons(fill="pmap", fill.scale = tm_scale(values="brewer.rd_bu", midpoint=0.5, breaks=brks), fill.legend = tm_legend(frame=FALSE, item.r = 0, position = tm_pos_out("right", "center")), lwd=0.01) + tm_layout(component.autoscale=FALSE)
} else {
tm_shape(nc) + tm_fill("pmap", breaks=brks, midpoint=0.5, palette="RdBu") + tm_layout(legend.outside=TRUE)
}

Marilia Carvalho (personal communication) and Virgilio Gómez Rubio (Gómez-Rubio, Ferrándiz-Ferragud, and López-Quílez 2005) have pointed to the unusual shape of the distribution of the Poisson probability values (histogram below), repeating the doubts about probability mapping voiced by Cressie (1991, 392): “an extreme value $\ldots$ may be more due to its lack of fit to the Poisson model than to its deviation from the constant rate assumption”. There are many more high values than one would have expected, suggesting perhaps overdispersion, that is that the ratio of the variance and mean is larger than unity.

hist(nc$pmap, main="")

One ad-hoc way to assess the impact of the possible failure of our assumption that the counts follow the Poisson distribution is to estimate the dispersion by fitting a generalized linear model of the observed counts including only the intercept (null model) and offset by the observed population at risk (suggested by Marilia Carvalho and associates):

res <- glm(SID74 ~ offset(log(BIR74)), data=nc, family="quasipoisson")
nc$stdres <- rstandard(res)

brks <- c(-4, -3, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 3, 4)
if (tmap4) {
  tm_shape(nc) + tm_polygons(fill="stdres", fill.scale = tm_scale(values="brewer.rd_bu", midpoint=0.5, breaks=brks), fill.legend = tm_legend(frame=FALSE, item.r = 0, position = tm_pos_out("right", "center")), lwd=0.01) + tm_layout(component.autoscale=FALSE)
} else {
  tm_shape(nc) + tm_fill("stdres", breaks=brks, midpoint=0, palette="RdBu") + tm_layout(legend.outside=TRUE)
}

The dispersion is equal to 2.2786188, much greater than unity; we calculate the corrected probability map values by taking the standardised residuals of the model, taking the size of the dispersion into account; the results are shown above. Many fewer counties appear now to have unexpectedly large or small numbers of cases. This is an ad-hoc adjustment made because R provides access to a wide range of model-fitting functions that can be used to help check our assumptions. Gómez-Rubio, Ferrándiz-Ferragud, and López-Quílez (2005) chose rather to construct a probability map under the hypothesis that data are drawn from a Negative Binomial distribution.

So far, none of the maps presented have made use of the spatial dependence possibly present in the data. A further elementary step that can be taken is to map Empirical Bayes estimates of the rates, which are smoothed in relation to the raw rates. The underlying question here is linked to the larger variance associated with rate estimates for counties with small populations at risk compared with counties with large populations at risk. Empirical Bayes estimates place more credence on the raw rates of counties with large populations at risk, and modify them much less than they modify rates for small counties. In the case of small populations at risk, more confidence is placed in either the global rate for the study area as a whole, or for local Empirical Bayes estimates, in rates for a larger moving window including the neighbours of the county being estimated. The function used for this in spdep is EBlocal(), initially contributed by Marilia Carvalho. It parallels a similar function in GeoDa, but uses the Bailey and Gatrell (1995) interpretation of Marshall (1991), rather than that in GeoDa (Anselin, Syabri, and Smirnov 2002).

global_rate <- sum(nc$SID74)/sum(nc$BIR74)
nc$Expected <- global_rate * nc$BIR74
res <- EBlocal(nc$SID74, nc$Expected, ncCC89, zero.policy=TRUE)
nc$EB_loc <- res$est

brks <- c(0, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5)
nc_miss <- st_centroid(st_geometry(nc[card(ncCC89) == 0,]), of_largest_polygon)
if (tmap4) {
  tm_shape(nc) + tm_polygons(fill="stdres", fill.scale = tm_scale(values="brewer.rd_bu", midpoint=0.5, breaks=brks), fill.legend = tm_legend(frame=FALSE, item.r = 0, position = tm_pos_out("right", "center")), lwd=0.01) + tm_layout(component.autoscale=FALSE) + tm_shape(nc_miss) + tm_symbols(shape=8, size=0.5)
} else {
tm_shape(nc) + tm_fill("EB_loc", breaks=brks, midpoint=1, palette="RdBu") + tm_layout(legend.outside=TRUE) + tm_shape(nc_miss) + tm_symbols(shape=8, size=0.5)
}

The results are shown in Figure . Like other relevant functions in spdep,EBlocal() takes a zero.policy argument to allow missing values to be passed through. In this case, no local estimate is available for the two counties with no neighbours, marked by stars.

In addition to Empirical Bayes smoothing globally, used both for disease mapping and the Assuncão and Reis correction to Moran’s $I$ for rates data (to shrink towards the global rate when the population at risk is small, here as a Monte Carlo test), lists of local neighbours can be used to shrink towards a local rate.

set.seed(1)
EBImoran.mc(nc$SID74, nc$BIR74, nb2listw(ncCC89, style="B", zero.policy=TRUE), nsim=999, zero.policy=TRUE)

## 
##  Monte-Carlo simulation of Empirical Bayes Index (mean subtracted)
## 
## data:  cases: nc$SID74, risk population: nc$BIR74
## weights: nb2listw(ncCC89, style = "B", zero.policy = TRUE)
## number of simulations + 1: 1000
## 
## statistic = 0.25789, observed rank = 998, p-value = 0.002
## alternative hypothesis: greater

Exploration and modelling of the data

One of the first steps taken by Cressie and Read (1985) is to try to bring out spatial trends by dividing North Carolina up into $4\times4$ rough rectangles. Just to see how this works, let us map these rough rectangles before proceeding further.

nc$both <- factor(paste(nc$L_id, nc$M_id, sep=":"))
nboth <- length(table(unclass(nc$both)))

if (tmap4) {
  tm_shape(nc) + tm_polygons(fill="both", fill.scale=tm_scale(values="brewer.set1"), fill.legend = tm_legend("rough\nrectangles", frame=FALSE, item.r = 0, position = tm_pos_out("right", "center")), lwd=0.01) + tm_layout(component.autoscale=FALSE)
} else {
tm_shape(nc) + tm_fill("both", palette="Set1", title="rough\nrectangles") + tm_layout(legend.outside=TRUE)
}

Cressie constructs a transformed SIDS rates variable, 1974–78, for his analyses (with co-workers). We can replicate his stem-and-leaf figure on p. 396 in the book, taken from Cressie and Read (1989):

nc$ft.SID74 <- sqrt(1000)*(sqrt(nc$SID74/nc$BIR74) + sqrt((nc$SID74+1)/nc$BIR74))
stem(round(nc$ft.SID74, 1), scale=2)

## 
##   The decimal point is at the |
## 
##   0 | 9
##   1 | 111244
##   1 | 567789999
##   2 | 0011111222334444
##   2 | 55555666677778999999999
##   3 | 000111122333333344444444
##   3 | 5568999
##   4 | 013344
##   4 | 555557
##   5 | 2
##   5 | 
##   6 | 3

Median polish smoothing

Cressie (1991, 46–48, 393–400) discusses in some detail how smoothing may be used to partition the variation in the data into smooth and rough. In order to try it out on the North Carolina SIDS data set, we will use a coarse gridding into four columns and four rows given by Cressie (1991, 553–54), where four grid cells are empty; these are given by variables L_id and M_id in object nc. Next we aggregate the number of live births and the number of SIDS cases 1974–1978 for the grid cells:

mBIR74 <- tapply(nc$BIR74, nc$both, sum)
mSID74 <- tapply(nc$SID74, nc$both, sum)

Using the same Freeman-Tukey transformation as is used for the county data, we coerce the data into a correctly configured matrix, some of the cells of which are empty. The medpolish function is applied to the matrix, being told to remove empty cells; the function iterates over the rows and columns of the matrix using median to extract an overall effect, row and column effects, and residuals:

mFT <- sqrt(1000)*(sqrt(mSID74/mBIR74) + sqrt((mSID74+1)/mBIR74))
# mFT1 <- t(matrix(mFT, 4, 4, byrow=TRUE))
# wrong assignment of 12 elements to a 4x4 matrix detected by CRAN test 2021-05-22
rc <- do.call("rbind", lapply(strsplit(names(mFT), ":"), as.integer))
mFT1 <- matrix(as.numeric(NA), 4, 4)
for (i in 1:nrow(rc)) mFT1[rc[i,1], rc[i,2]] <- mFT[i]
med <- medpolish(mFT1, na.rm=TRUE, trace.iter=FALSE)
med

## 
## Median Polish Results (Dataset: "mFT1")
## 
## Overall: 2.90965
## 
## Row Effects:
## [1] -0.05686791 -0.37236370  0.05686791  0.79541774
## 
## Column Effects:
## [1] -0.005484562 -0.446250551  0.003656375  0.726443256
## 
## Residuals:
##           [,1]     [,2]      [,3]     [,4]
## [1,]        NA -0.45800  0.000000  0.37556
## [2,] -0.092554  0.00000  0.101695  0.00000
## [3,]  0.092554  0.30464 -0.090726 -0.55364
## [4,]        NA       NA  0.000000       NA

Returning to the factors linking rows and columns to counties, and generating matrices of dummy variables using model.matrix, we can calculate fitted values of the Freeman-Tukey adjusted rate for each county, and residuals by subtracting the fitted value from the observed rate. Naturally, the fitted value will be the same for counties in the same grid cell:

mL_id <- model.matrix(~ as.factor(nc$L_id) -1)
mM_id <- model.matrix(~ as.factor(nc$M_id) -1)
nc$pred <- c(med$overall + mL_id %*% med$row + mM_id %*% med$col)
nc$mp_resid <- nc$ft.SID74 - nc$pred

if (tmap4) {
  out1 <- tm_shape(nc) + tm_polygons(fill=c("ft.SID74", "pred"), fill.scale=tm_scale(values="brewer.yl_or_br"), fill.legend=tm_legend(position=tm_pos_out("right", "center"), frame=FALSE, item.r = 0), fill.free=FALSE, lwd=0.01) + tm_layout(panel.labels=c("Observed", "Median polish prediction"))
  out2 <- tm_shape(nc) + tm_polygons(fill="mp_resid", fill.scale=tm_scale(values="brewer.rd_yl_gn", midpoint=0), fill.legend=tm_legend(position=tm_pos_out("right", "center"), frame=FALSE, item.r = 0), lwd=0.01)
} else {
out1 <- tm_shape(nc) + tm_fill(c("ft.SID74", "pred")) + tm_facets(free.scales=FALSE) + tm_layout(panel.labels=c("Observed", "Median polish prediction"))
out2 <- tm_shape(nc) + tm_fill("mp_resid", midpoint=0) + tm_layout(legend.outside=TRUE)
}
tmap_arrange(out1, out2, ncol=1)

The figure shows the median polish smoothing results as three maps, the observed Freeman-Tukey transformed SIDS rates, the fitted smoothed values, and the residuals. In addition, a plot for the median polish object is also shown, plotting the smooth residuals against the outer product of the row and column effects divided by the overall effect, which would indicate a lack of additivity between row and column if this was the case — this is more relevant for analysis of tables of covariates rather than geographical grids.

References

Anselin, Luc, Ibnu Syabri, and Oleg Smirnov. 2002. “Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows.” In University of California, Santa Barbara. CD-ROM.

Bailey, T. C., and A. C. Gatrell. 1995. Interactive Spatial Data Analysis. Harlow: Longman.

Choynowski, M. 1959. “Maps Based on Probabilities.” Journal of the American Statistical Association 54: 385–88.

Cressie, N. 1991. Statistics for Spatial Data. New York: Wiley.

Cressie, N., and N. H. Chan. 1989. “Spatial Modelling of Regional Variables.” Journal of the American Statistical Association 84: 393–401.

Cressie, N., and T. R. C. Read. 1985. “Do Sudden Infant Deaths Come in Clusters?” Statistics and Decisions Supplement Issue 2: 333–49.

———. 1989. “Spatial Data-Analysis of Regional Counts.” Biometrical Journal 31: 699–719.

Gómez-Rubio, V., J. Ferrándiz-Ferragud, and A. López-Quílez. 2005. “Detecting Clusters of Disease with R.” Journal of Geographical Systems 7: 189–206.

Kaluzny, S. P., S. C. Vega, T. P. Cardoso, and A. A. Shelly. 1996. S-PLUS SPATIALSTATS User’s Manual Version 1.0. Seattle: MathSoft Inc.

Marshall, R. M. 1991. “Mapping Disease and Mortality Rates Using Empirical Bayes Estimators.” Applied Statistics 40: 283–94.