1. Input Data
1.1 Defining the Lattice Arena
To begin, we will establish a squared lattice arena, which will serve as the site for our mosquito introduction. The chosen dimensions are 5 km on each side with a resolution of 250 m (comprising 20 columns and 20 rows, totalling 400 cells).
# Libraries
library(gstat)
library(terra)
library(eesim)
library(dynamAedes)
library(ggplot2)
Sys.setlocale("LC_TIME", "en_GB.UTF-8")# [1] "en_GB.UTF-8"gridDim <- 20 # 5000m/250 m = 20 columns and rows
xy <- expand.grid(x=1:gridDim, y=1:gridDim)To infuse a spatial element into the lattice area, we will overlay a spatial pattern, which will subsequently be used to impart spatial correlation to the temperature time series. This spatially correlated pattern is derived using a semivariogram model with a specified sill and range. We then predict this model over the grid using unconditional Gaussian simulation.
varioMod <- vgm(psill=0.5, range=100, model='Exp') # psill = partial sill = (sill-nugget)
# Set up an additional variable from simple kriging
zDummy <- gstat(formula=z~1,
locations = ~x+y,
dummy=TRUE,
beta=1,
model=varioMod,
nmax=1)
# Generate a randomly autocorrelated predictor data field
set.seed(123)
xyz <- predict(zDummy, newdata=xy, nsim=1)# [using unconditional Gaussian simulation]Creating a spatially correlated raster with the SA variable enhances our understanding of environmental features like urban vegetation distribution.
utm32N <- "+proj=utm +zone=32 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
r <- terra::rast(nrow=gridDim, ncol=gridDim, crs=utm32N, ext=terra::ext(1220000,1225000, 5700000,5705000))
terra::values(r) <- xyz$sim1
plot(r, main="SAC landscape")
# convert to a data.frame
df <- data.frame("id"=1:nrow(xyz), terra::crds(r))
bbox <- terra::as.polygons(terra::ext(r), crs=utm32N)
# Store Parameters for autocorrelation
autocorr_factor <- terra::values(r)1.2 Simulate temperature data with seasonal trend
We simulate a 1-year temperature time series with seasonal trend. For the time series we consider a mean value of 16°C and standard deviation of 2°C.
ndays = 365
set.seed(123)
sim_temp <- eesim::create_sims(n_reps = 1,
n = ndays,
central = 16,
sd = 2,
exposure_type = "continuous",
exposure_trend = "cos1", exposure_amp = -1.0,
average_outcome = 12,
outcome_trend = "cos1",
outcome_amp = 0.8,
rr = 1.0055)A visualisation of the distribution of temperature values and temporal trend.
hist(sim_temp[[1]]$x,
xlab="Temperature (°C)",
main="Histogram of Simulated Temperatures")
plot(sim_temp[[1]]$date,
sim_temp[[1]]$x,
main="Simulated Temperature Seasonal Trend",
xlab="Date", ylab="Temperature (°C)"
)
We can then “expand onto space” the temperature time series by multiplying it with the autocorrelated surface simulated above.
mat <- do.call(rbind, lapply(1:ncell(r), function(x) {
d_t <- sim_temp[[1]]$x*autocorr_factor[[x]]
return(d_t)
}))oldpar <- par(mfrow = c(1,2)) A comparison between the distribution of the initial temperature time series and autocorrelated temperature surface
par(mfrow=c(2,1))
hist(mat, xlab="Temperature (°C)", main="Histogram of simulated spatial autocorreled temperature")
hist(sim_temp[[1]]$x, xlab="Temperature (°C)", main="Histogram of simulated temperatures", col="red")
par(mfrow=c(1,1))par(oldpar) 1.2.1 Format temperature data
names(mat) <- paste0("d_", 1:ndays)
df_temp <- cbind(df, mat)
