model {
for(i in 1:73) {
Y[i] ~ dpois(lambda[i])
log(lambda[i]) <- log(t[i]) + beta + S[i]
}
# spatial field representing true log contamination intensity
S[1:73] ~ spatial.exp(mu[], x[], y[],
tau, phi, kappa)
for(i in 1:73) {
mu[i] <- 0
}
# mean log contamination intensity
beta ~ dunif(-3, 7)
# priors on parameters of spatial covariance matrix
phi ~ dunif(0, 120)
kappa ~ dunif(0.1, 1.95)
sigma ~ dnorm(0, 1)I(0,)
tau <- 1/pow(sigma, 2)
for(j in 1:93) { # prediction
T[j] ~ spatial.unipred(mu.pred[j], x.pred[j],
y.pred[j], S[])
exp.T[j] <- exp(T[j] + beta) # predicted intensity
mu.pred[j] <- 0
}
# combine observed and predicted locations
for(i in 1:73) {
pred[i] <- exp(S[i])
}
for(i in 74:166) {
pred[i] <- exp.T[i-73]
}
# max value of contamination
max.level <- ranked(pred[], 166)
for(i in 1:166) { # location of maximum
pred.rank[i] <- rank(pred[], i)
prob.max[i] <- equals(pred.rank[i], 166)
}
# prob that count/sec > 15 at location i
for(i in 1:166) {
prob.exceeds.15[i] <- step(pred[i] - 15)
}
}

Inits:
list(beta = 1.5, sigma = 1, phi = 10, kappa = 0.7)

Data:
list(
x = c(-4.033, -4.033, -3.95, -3.95, -3.867, -3.867,
-3.75, -3.8, -3.8, -3.8, -3.8, -3.8, -3.773, -3.773, -3.773,
-3.773, -3.773, -3.747, -3.747, -3.747, -3.747, -3.747, -3.72,
-3.72, -3.72, -3.72, -3.72, -3.693, -3.693, -3.693, -3.693, -3.693,
-3.633, -3.633, -3.553, -3.553, -3.553, -3.553, -3.553, -3.527,
-3.527, -3.527, -3.527, -3.527, -3.5, -3.5, -3.5, -3.5, -3.5,
-3.473, -3.473, -3.473, -3.473, -3.473, -3.447, -3.447, -3.447,
-3.447, -3.447, -3.367, -3.367, -3.267, -3.267, -3.133, -3, -3,
-2.867, -2.733, -2.6, -2.467, -2.333, -2.2, -2.067),

y = c(-2.18, -2.11, -2.213, -2.11, -2.233, -2.11, -2.233, -2.173, -2.147,
-2.12, -2.093, -2.067, -2.173, -2.147, -2.12, -2.093, -2.067,
-2.173, -2.147, -2.12, -2.093, -2.067, -2.173, -2.147, -2.12,
-2.093, -2.067, -2.173, -2.147, -2.12, -2.093, -2.067, -2.233,
-2.11, -2.287, -2.26, -2.233, -2.207, -2.18, -2.287, -2.26, -2.233,
-2.207, -2.18, -2.287, -2.26, -2.233, -2.207, -2.18, -2.287,
-2.26, -2.233, -2.207, -2.18, -2.287, -2.26, -2.233, -2.207,
-2.18, -2.2, -2.11, -2.133, -2.067, -2.017, -2, -1.947, -1.907,
-1.807, -1.773, -1.707, -1.673, -1.64, -1.627),

Y = c(75, 371,1931, 4357, 2114, 2318, 1975, 1912, 1902, 1882, 3017, 2403, 2616,
2275, 2432, 2754, 3452, 1726, 2118, 2269, 4629, 1840, 2691, 4084,
2187, 4531, 2022, 1822, 2800, 2722, 4083, 1650, 2890, 2776, 2525,
3580, 2960, 3100, 3611, 1813, 2560, 3733, 2273, 3437, 2603, 2148,
2360, 3050, 3287, 2603, 4044, 2560, 3019, 2407, 2735, 3020, 2215,
3414, 1688, 3447, 1091, 3279, 3101, 2370, 2902, 1401, 892, 1872,
2610, 3548, 2652, 2625, 2314),

t = c(300, 300, 300, 300, 300,
300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 400,
300, 300, 400, 300, 360, 300, 400, 300, 300, 300, 300, 300, 300,
400, 300, 300, 300, 300, 300, 300, 300, 200, 300, 300, 300, 300,
300, 200, 300, 300, 300, 300, 300, 300, 300, 300, 200, 300, 300,
300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300,
300, 300, 300),

x.pred = c(-4.173, -4.12, -4.12, -4.12, -4.066,
-4.066, -4.066, -4.013, -4.013, -4.013, -3.96, -3.96, -3.96,
-3.906, -3.906, -3.906, -3.906, -3.906, -3.906, -3.853, -3.853,
-3.853, -3.853, -3.8, -3.8, -3.8, -3.8, -3.746, -3.746, -3.693,
-3.693, -3.693, -3.693, -3.64, -3.64, -3.64, -3.64, -3.586, -3.586,
-3.586, -3.586, -3.586, -3.533, -3.533, -3.48, -3.48, -3.426,
-3.426, -3.373, -3.373, -3.32, -3.32, -3.32, -3.32, -3.266, -3.266,
-3.213, -3.213, -3.213, -3.16, -3.106, -3.106, -3.106, -3.053,
-3.053, -2.946, -2.893, -2.84, -2.84, -2.786, -2.733, -2.68,
-2.68, -2.573, -2.573, -2.52, -2.52, -2.413, -2.413, -2.36, -2.306,
-2.306, -2.253, -2.253, -2.2, -2.146, -2.146, -2.093, -2.093,
-2.04, -2.04, -1.986, -1.986),

y.pred = c(-2.095, -2.042, -2.095,
-2.148, -2.042, -2.095, -2.148, -1.988, -2.042, -2.148, -1.988,
-2.042, -2.148, -1.988, -2.042, -2.095, -2.148, -2.201, -2.255,
-1.988, -2.042, -2.148, -2.201, -1.988, -2.201, -2.255, -2.308,
-2.201, -2.308, -2.201, -2.255, -2.308, -2.361, -2.042, -2.148,
-2.201, -2.361, -2.095, -2.148, -2.201, -2.255, -2.308, -2.095,
-2.148, -2.095, -2.148, -2.095, -2.148, -2.148, -2.255, -2.042,
-2.095, -2.148, -2.201, -1.988, -2.095, -1.988, -2.042, -2.095,
-1.988, -1.935, -1.988, -2.042, -1.935, -1.988, -1.935, -1.935,
-1.828, -1.882, -1.828, -1.775, -1.775, -1.828, -1.722, -1.775,
-1.722, -1.775, -1.668, -1.722, -1.722, -1.615, -1.668, -1.615,
-1.668, -1.668, -1.615, -1.668, -1.562, -1.668, -1.562, -1.615,
-1.562, -1.615))

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   beta   1.841   0.3141   0.014   1.228   1.856   2.491   10001   250000
   kappa   0.9327   0.2239   0.002256   0.4973   0.9317   1.368   10001   250000
   max.level   32.72   12.44   0.08155   18.93   29.82   63.6   10001   250000
   phi   8.547   6.106   0.1143   1.008   7.58   21.0   10001   250000
   sigma   0.8377   0.2211   0.004254   0.5677   0.787   1.413   10001   250000

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   beta   1.873   0.3431   0.01078   1.242   1.869   2.673   10001   1000000
   kappa   0.9312   0.2246   0.001621   0.4954   0.9301   1.369   10001   1000000
   max.level   32.83   12.58   0.0579   18.99   29.88   64.09   10001   1000000
   phi   8.445   6.092   0.08313   0.8851   7.485   20.88   10001   1000000
   sigma   0.8459   0.2311   0.00342   0.5683   0.7903   1.452   10001   1000000