`vignettes/sids.Rmd`

`sids.Rmd`

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).

We will be using the **spdep** and **spreg** packages, here version: spdep, version 1.1-2, 2019-04-05, 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.

```
library(spdep)
nc <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
st_crs(nc) <- "+proj=longlat +datum=NAD27"
row.names(nc) <- as.character(nc$FIPSNO)
```

The shapefile format presupposes that you have 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 are required to have the same name apart from the extension, and are 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:

```
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"
## [6] "FIPS" "FIPSNO" "CRESS_ID" "BIR74" "SID74"
## [11] "NWBIR74" "BIR79" "SID79" "NWBIR79" "east"
## [16] "north" "x" "y" "lon" "lat"
## [21] "L_id" "M_id" "geometry"
```

`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
## Alamance : 1 37001 : 1 Min. :37001 Min. : 1.00
## Alexander: 1 37003 : 1 1st Qu.:37050 1st Qu.: 25.75
## Alleghany: 1 37005 : 1 Median :37100 Median : 50.50
## Anson : 1 37007 : 1 Mean :37100 Mean : 50.50
## Ashe : 1 37009 : 1 3rd Qu.:37150 3rd Qu.: 75.25
## Avery : 1 37011 : 1 Max. :37199 Max. :100.00
## (Other) :94 (Other):94
## 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 geometry
## 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:
## EPSG: 4267
## proj4string: "+proj=longlat +datum=NAD27 +no_defs"
```

```
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://s3.amazonaws.com/geoda/data/sids.zip", file.path(td, "sids.zip"), quiet=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://s3.amazonaws.com/geoda/data/sids2.zip", file.path(td, "sids2.zip"), quiet=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:
## EPSG: 4326
## proj4string: "+proj=longlat +datum=WGS84 +no_defs"
```

`st_crs(sids2_sf)`

```
## Coordinate Reference System:
## EPSG: 4326
## proj4string: "+proj=longlat +datum=WGS84 +no_defs"
```

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] TRUE`

```
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] TRUE`

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] TRUE`

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

`## [1] TRUE`

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
```

```
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.

`## [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.

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)
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 (http://colorbrewer.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)
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):

```
brks <- c(-4, -3, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 3, 4)
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)
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 = 1000, p-value = 0.001
## alternative hypothesis: greater
```

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.

`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
```

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:

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))
med <- medpolish(mFT1, na.rm=TRUE, trace.iter=FALSE)
med
```

```
##
## Median Polish Results (Dataset: "mFT1")
##
## Overall: 2.765802
##
## Row Effects:
## [1] -0.728192882 0.001560182 0.861279153 -0.001560182
##
## Column Effects:
## [1] 0.00000000 -0.03564965 0.44969186 0.00000000
##
## Residuals:
## [,1] [,2] [,3] [,4]
## [1,] -0.089076 0.089076 0.33761 -0.089076
## [2,] 0.089076 -0.089076 -0.33761 0.089076
## [3,] 0.327702 -0.327702 -0.93746 0.327702
## [4,] -0.324994 0.324994 0.49479 -0.324994
```

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
```

```
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.

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.

Symons, M. J., R. C. Grimson, and Y. C. Yuan. 1983. “Clustering of Rare Events.” *Biometrics* 39: 193–205.

These data were taken with permission from a now-offline link: [sal.agecon.uiuc.edu/datasets/sids.zip]; see also GeoDa Center for a contemporary source.↩