The global historical climatological network is composed of a series of measurement locations scattered across the United States. These measurements are commonly used to make maps of climate measurements for various regions. These maps play a vital role in understanding the nature and trends of weather in the United States. Two of the primary measurements in this network are minimum and maximum temperature. Indeed, millions of Americans take interest in the forecasts of minimum and maximum temperature when deciding how to dress each day. (DeGaetano and Belcher 2007) used various interpolation approaches to create minimum and maximum temperature maps of the northeastern United States. Their techniques treated minimum and maximum temperatures as separate observations. In a different framework, minimum and maximum temperatures could be considered as two elements of one and the same observation, which is daily temperature. In other words, these daily temperature measurements create a single interval-valued observation rather than two individual observations. This holistic view of temperature motivates the need for methods to analyze the interaction of temperature magnitude and range across space. The intkrige package provides a way to analyze and interpolate interval-valued data. A simple demonstration of the workflow underlying these interval-valued temperature predictions is provided in this vignette for the Ohio river basin. The package README also demonstrates this workflow by predicting interval-valued design ground snow loads in Utah. Further details regarding these snow load predictions are provided in (Bean, Sun, and Maguire 2019a) and (Bean, Sun, and Maguire 2019b).
The Ohio river basin includes most of the states of Ohio, West Virginia, Kentucky, and Indiana, as well as parts of Pennsylvania, Illinois, and Tennessee. The region is approximately 204,000 square miles and home to nearly 25 million people (Ohio River Basin Consortium 2019). The region was selected due its relatively homogeneous elevation profile when compared to the western United States. We wish to map the magnitude and range of temperatures across this region. This is done by creating intervals of the 30 year mean maximum and minimum daily temperatures for available measurement locations.
Measurement locations were downloaded from National Oceanic and Atmospheric Administration’s (NOAA) Global Historical Climatological Network (GHCN) (Menne et al. 2012). The 161 remaining observations in the dataset all contained at least 30 years of record with at least 300 daily observation for calendar years 1988 to 2018. Intervals were created using a 5% trimmed 30 year mean for maximum and minimum temperatures respectively. These data are included in the package and can be accessed with the commands
library(intkrige)
#> Loading required package: sp
#> Loading required package: gstat
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
#> Loading required package: raster
data(ohtemp)
head(ohtemp)
#> ID NAME STATE LONGITUDE LATITUDE ELEVATION
#> 1 USC00111302 CARMI 3 IL -88.1805 38.0733 102.1
#> 2 USC00111436 CHARLESTON IL -88.1653 39.4761 213.4
#> 3 USC00112140 DANVILLE IL -87.6478 40.1392 169.2
#> 4 USC00112931 FAIRFIELD RADIO WFIW IL -88.3264 38.3806 131.1
#> 5 USC00114198 HOOPESTON IL -87.6850 40.4664 216.4
#> 6 USC00116446 OLNEY 2S IL -88.0817 38.7003 139.9
#> minm maxm
#> 1 76.11001 196.7273
#> 2 67.56999 180.6998
#> 3 57.90647 178.5897
#> 4 75.05457 197.1208
#> 5 57.97599 170.2541
#> 6 72.36740 191.8014
The following figure shows a map of this region with the 161 stations locations overlaid. The code for this map makes use of the ggmap package (Kahle and Wickham 2013). The code to generate the map is not provided as the map rendering requires a registered Google API account.
Our goal is to create an interval-valued map of the mean maximum and minimum temperatures across the region. Doing so allows us to simultaneously compare spatial patterns in the magnitude and range of average temperatures experienced in the region. The intkrige package provides a formal workflow for analyzing interval valued data by extending SpatialPointsDataFrame and SpatialPixelsDataFrame objects from the sp package (Bivand et al. 2013). These objects are created using the interval() function as demonstrated below.
# First, create a SpatialPointsDataFrame in the usual way
sp::coordinates(ohtemp) <- c("LONGITUDE", "LATITUDE")
sp::proj4string(ohtemp) <- CRS("+proj=longlat +ellps=WGS84")
interval(ohtemp) <- c("minm", "maxm")
head(ohtemp)
#> coordinates interval ID
#> 1 (-88.1805, 38.0733) [76.11001, 196.7273] USC00111302
#> 2 (-88.1653, 39.4761) [67.56999, 180.6998] USC00111436
#> 3 (-87.6478, 40.1392) [57.90647, 178.5897] USC00112140
#> 4 (-88.3264, 38.3806) [75.05457, 197.1208] USC00112931
#> 5 (-87.685, 40.4664) [57.97599, 170.2541] USC00114198
#> 6 (-88.0817, 38.7003) [72.3674, 191.8014] USC00116446
#> NAME STATE ELEVATION
#> 1 CARMI 3 IL 102.1
#> 2 CHARLESTON IL 213.4
#> 3 DANVILLE IL 169.2
#> 4 FAIRFIELD RADIO WFIW IL 131.1
#> 5 HOOPESTON IL 216.4
#> 6 OLNEY 2S IL 139.9
This function creates an intsp object. Use of the interval function on SpatialPixelsDataFrame objects creates intgrd objects. Both of these objects contain an interval slot that extend their parent classes. The interval slot is filled by specifying column names within the data frame, or specifying a two-column matrix with the same number of rows as the object data. These two forms of specification allow for convenient transformations of the interval slot values with the appropriate calls.
interval(ohtemp) <- log(interval(ohtemp))
head(ohtemp)
#> coordinates interval ID
#> 1 (-88.1805, 38.0733) [4.33218, 5.281819] USC00111302
#> 2 (-88.1653, 39.4761) [4.213164, 5.196837] USC00111436
#> 3 (-87.6478, 40.1392) [4.058829, 5.185091] USC00112140
#> 4 (-88.3264, 38.3806) [4.318215, 5.283817] USC00112931
#> 5 (-87.685, 40.4664) [4.060029, 5.137292] USC00114198
#> 6 (-88.0817, 38.7003) [4.281756, 5.25646] USC00116446
#> NAME STATE ELEVATION
#> 1 CARMI 3 IL 102.1
#> 2 CHARLESTON IL 213.4
#> 3 DANVILLE IL 169.2
#> 4 FAIRFIELD RADIO WFIW IL 131.1
#> 5 HOOPESTON IL 216.4
#> 6 OLNEY 2S IL 139.9
Note that intervals must be defined by their endpoints with the lower endpoint appearing in the first column. The interval() function will throw an error if it detects any instance of a lower endpoint greater than its corresponding upper endpoint. The function will also throw an error if a proposed transformation of the interval causes a lower endpoint to be greater than an upper endpoint. Intervals can also be characterized by their center and radius. It is easier to describe and characterize intervals by their endpoints, but it is easier to visualize and analyze spatial intervals using the center and radius. Thus interval objects must be initialized using their endpoints, but all interval-valued kriging and variogram functions automatically determine and use the center/radius form in analysis.
The interval-valued kriging functions require variograms for the center and radius, while a cross-variogram for the center/radius interaction is optional. The most common way of exploring these spatial relationships is through the use of an empirical variogram, which measures pairwise differences in measurements \(z\) with locations \(\mathbf{u}_\alpha\) as a function of the difference in location \(\mathbf{h}\). This is defined symbolically as
\[\begin{equation} \gamma(\mathbf{h}) = \frac{1}{2N(\mathbf{h})}\sum_{\alpha=1}^{N(\mathbf{h})}\left[z(\mathbf{u}_\alpha) - z(\mathbf{u}_\alpha+h)\right]^2 \label{eqn:vario} \end{equation}\]where \(N(\mathbf{h})\) represents the number of station pairs with location difference \(\mathbf{h}\) (Goovaerts 1997).
We define empirical variograms for the center, radius, and center/radius interaction using (1). The intvariogram() function provides wrappers to variogram functionality in the gstat package. These wrapper functions exploit the predictable structure of the interval slot in the intsp and intgrd objects to calculate the three empirical variograms simultaneously. The output of this function is an object of class intvariogram. This new S3 class distinguishes this output from traditional lists of empirical variograms.
# Revert back to the standard interval
interval(ohtemp) <- exp(interval(ohtemp))
varios <- intvariogram(ohtemp, cutoff = 500)
plot(varios)
It seems that the radius and center/radius interaction could be appropriately modeled with spherical variograms while the center could be modeled with a linear variogram. The fit.intvariogram function provides a way to fit theoretical variograms to the three empirical variograms generated by the intvariogram function. The fit.intvariogram function uses the existing functionality of the fit.variogram function in the gstat package. The fit.intvariogram function only accepts objects of class intvariogram and is designed to be used in tandem with the intvariogram function. The intvcheck function allows for a quick evaluation of the model output from fit.intvariogram.
varioFit <- fit.intvariogram(varios, models = gstat::vgm(c("Lin", "Sph", "Sph")))
#> Warning in gstat::fit.variogram(x_sub, model = models[[i]], ...): singular
#> model in variogram fit
varioFit
#> [[1]]
#> model psill range
#> 1 Nug 1.003235 0.0000
#> 2 Lin 361.958967 497.7719
#>
#> [[2]]
#> model psill range
#> 1 Nug 0.0000 0.00000
#> 2 Sph 23.1517 80.56012
#>
#> [[3]]
#> model psill range
#> 1 Nug -8.460132 0.000
#> 2 Sph 41.454114 470.397
intvCheck(varios, varioFit)
Note that for the observed data, the theoretical variogram models provide a decent fit to the data. However, the “singular model” warning reminds us that well-defined covariances cannot be recovered from linear variograms. The current interval-valued kriging models rely on the covariance equivalence of the variograms (which is calculated using variogramLine(covariance = TRUE)). A common practice in this case is to fit a spherical variogram with an arbitrary sill and range. These values are selected to create a spherical variogram that looks similar to the linear variogram within the viewing window.
# Replace non-convergent variogram fit with a surrogate that
# contains a reasonable range and sill.
varioFit[[1]] <- gstat::vgm(psill = 350, nugget = 4.608542,
range = 600, model = "Sph")
intvCheck(varios, varioFit)
We understand and acknowledge the shortcomings of this variogram fitting approach as articulated in (Loquin and Dubois 2010). Despite its shortcomings, this common approach to variogram fitting works well for many spatial projects. Our substitution of a spherical variogram for a linear one certainly would demand more attention in a rigorous analysis of temperatures. However, for the purposes of demonstration, this model substitution is sufficient to demonstrate the interval-valued kriging workflow.
where \(C^{C,C}, C^{R,R}, and C^{C,R}\) are the covariances associated with the center, radius, and center/radius interaction. This is can be equivalently defined using the corresponding variograms. However, the current optimization relies on the use of the covariance definitions. As previously mentioned, the function assumes that the covariances exist and calculates them from the variograms using gstat::variogramLine(covariance = TRUE).
The \(A_{11}, A_{12}, \text{ and } A_{22}\) parameters are used to weight the importance of different components of the covariance. These three arguments are specified with the A parameter in the intkrige function. The default option assumes no interaction between center and radius (i.e. A[3] = 0). A[3] is also effectively set to zero when a third variogram model is not specified.
The eta, thresh, tolq, maxq, tolp, maxp, and r variables all directly relate to the Penalized Newton-Raphson technique used minimize \(E\left[\rho^2_K\left([\hat{Z}(x^*)], [Z(x^*)]\right)\right]\). The details of this optimization technique are provided in a companion paper to this package (Bean, Sun, and Maguire 2019b) and will not be discussed further in this vignette. These optimization parameters provide the user the necessary flexibility to adjust the optimization technique as needed to produce convergent results.
These interval-valued kriging models are designed to predict hundreds, if not thousands, of locations in a single function call. Providing a separate initial guess for each prediction location quickly becomes impractical in such a context. As such, the initial guess for the kriging weights at each locations are derived from the traditional simple kriging weights obtained using only the interval centers. It is expected that the final weights will be close to this initial guess.
Once the user has selected proper optimization parameters, fast, useR, and cores variables all allow for potential speedups in the computation time. More about these arguments are found in the function documentation.Each new dataset brings with it a new set of considerations for the optimization algorithm. The R version of the models (invoked when useR = TRUE, the default) will print warnings to the screen when the optimization fails. Both the R and c++ versions will flag troublesome predictions by setting warn = 1 in the list of return arguments.
Determining the right optimization parameters to ensure a successful optimization for all locations can be tricky, especially when faced with a large number of prediction locations. It is recommended that the model first be run on a small set of prediction using the function defaults. Then, adjust the optimization parameters as needed based on the recommended guidelines in the list at the end of the section. Once the optimization is working correctly for a subset of locations, deploy the model on all locations. Movement to the c++ version (via useR = FALSE) should only happen after the optimization parameters have been appropriately tuned. Below is a list of common solutions for various optimization issues.left feasible region<> is best handled by increasing the value of r and/or increasing the value of eta to be closer to 1.
convergent, feasible solution not obtained<> is likely best handled by invoking the fast = TRUE option. The fast = FALSE is intended to prevent weights from converging to zero and is only relevant when the number of measurement locations is small (around 50 or less). In situations with more stations, the prevention of zero valued weights can cause a failure to converge. If this warning continues to be obtained. Consider increasing the value of eta closer to 1, or increasing the tolerance levels for the convergence criteria.
feasible solution obtained from a non-convergent optimization step<> is likely best handled by lowering the value of eta, which controls the rate of imposition of the penalty parameter.
As previously mentioned, we can accept the default arguments for the majority of parameters in the intkrige model for our predictions of Ohio temperatures. Because no closed form solution exists for these interval-valued kriging models, the computational time for fine maps can be tremendous. This is one of the reasons for the parallel processing option when the number of prediction locations is large. The parallel version of the model makes use of multiple packages included in the ``Suggests’’ field of the package description file. Once these packages are installed, the argument cores = -1 will use all but one of the available cores to make predictions at the specified locations. Alternatively, the user can explicitly define the number of cores to use in parallel with n =
# Include the Ohio river basin shapefile
data(ohMap)
# New location data preparation
lon <- seq(-89.26637, -77.83937, length.out = 10)
lat <- seq(35.31332, 42.44983, length.out = 10)
newlocations <- expand.grid(lon, lat)
colnames(newlocations) <- c("lon", "lat")
sp::coordinates(newlocations) <- c("lon", "lat")
sp::proj4string(newlocations) <- sp::proj4string(ohtemp)
sp::gridded(newlocations) <- TRUE
# Adjust r and theta to ensure answers remain in feasible region.
preds <- intkrige(ohtemp, newlocations, varioFit,
A = c(1, 1, 0.5), r = 200, eta = 0.9, maxp = 225)
plot(preds, beside = FALSE, circleCol = "gray") +
latticeExtra::layer(sp.lines(ohMap, col = "white"))
plot(preds, beside = TRUE)
The first plot visualizes the center and radius of the interval-predictions in a single image. The reference circles in the image provide the circle sizes corresponding to the maximum and minimum radius. The numeric values of the maximum and minimum radius (rounded to three decimal places) are also given along the top of the plot.
The second plot visualizes the components of the interval in a more traditional side by side plotting approach. These side by side plots make use of the gridArrange() function in the gridExtra<> package. Both plotting approaches use the lattice package approach to plotting. This means that the user cannot make adjustments to the plotting window using par() as they would in base R. Both plotting styles are intended to give users a quick way to visualize the spatial patterns in predictions.
These default plots help us to draw some quick conclusions from the data. First, notice that the highest temperature magnitudes occur in the southern portion of the basin while the largest temperature ranges occur in the eastern portion of the basin. The eastern border of the basin runs through the Appalachian mountains and mountainous regions are notorious for experiencing larger night and day swings in temperature. Conversely, warmer temperatures are more likely to persist throughout the night at lower elevations, farther south. This likely explains why the southern corner of the basin has higher temperature magnitudes and smaller temperature ranges. It is also noteworthy that the smallest predicted interval radii occur along the northern border of the basin, possibly due to the moderating effect of lake Erie on extreme daily temperatures.
More important than the analysis of Ohio river basin temperatures is the establishment of a pattern for exploring interval-valued spatial data. This demonstration has illustrated the use of a series of functions designed simplify and enhance interval-valued spatial analysis. We anticipate that future iterations of this package will result in an continual increase of functionality and applications.
Bean, Brennan, Yan Sun, and Marc Maguire. 2019a. “Interval-Valued Kriging Models for Geostatistical Mapping with Uncertain Inputs.”
———. 2019b. “Supplement to ‘Interval-Valued Kriging Models for Geostatistical Mapping with Uncertain Inputs’.”
Bivand, Roger S, Edzer J Pebesma, Virgilio Gomez-Rubio, and Edzer Jan Pebesma. 2013. Applied Spatial Data Analysis with R. 2nd ed. Vol. 747248717. Springer.
DeGaetano, Arthur T., and Brian N. Belcher. 2007. “Spatial Interpolation of Daily Maximum and Minimum Air Temperature Based on Meteorological Model Analyses and Independent Observations.” Journal of Applied Meteorology and Climatology 46 (11): 1981–92. doi:10.1175/2007JAMC1536.1.
Goovaerts, Pierre. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press.
Kahle, David, and Hadley Wickham. 2013. “Ggmap: Spatial Visualization with Ggplot2.” The R Journal 5 (1): 144–61. https://journal.r-project.org/archive/2013-1/kahle-wickham.pdf.
Loquin, Kevin, and Didier Dubois. 2010. “Kriging and Epistemic Uncertainty: A Critical Discussion.” In Methods for Handling Imperfect Spatial Information, 269–305. Springer.
Menne, M.J., I. Durre, B. Korzeniewski, R.S. Vose, B.E. Gleason, and T.G. Houston. 2012. “Global Historical Climatology Network - Daily (Ghcn-Daily), Version 3.26.” NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ.
Ohio River Basin Consortium. 2019. https://www.ohio.edu/orbcre/basin/index.html.