Most spatial modeling approaches work under the assumption that the data used for modeling are stationary. That is to say, the mean and variance of the response variable are constant in space. This assumption is often violated, especially when modeling large areas. Sometimes, a nonstationary process can be transformed into a stationary one by modeling external trends; however, this is not possible to achieve if the external trends are not constant over space. In such cases, an alternative approach is to partition the space into stationary sub-regions and model each region separately. The problem with this approach is that continuous response variables will have discontinuities in the prediction surface at the borders of the regions.

The package `remap`

is an implementation of a regional
modeling with border smoothing method that results in a continuous
global model. Border smoothing is accomplished by taking a weighted
average of regional model predictions near region borders. The
`remap`

function is also a convenient way to build
independent models in a partitioned space, even if no border smoothing
is required for the problem.

```
library(magrittr) # For pipe %>% functionality
library(tibble) # For light data wrangling
library(dplyr) # For light data wrangling
library(ggplot2) # For plots
library(maps) # For a polygon of the state of Utah
library(sf) # For spatial data manipulation
library(mgcv) # For GAM modeling
library(remap)
data(utsnow)
data(utws)
```

To introduce the functionality of remap, we will look at a modeling
problem for estimating snow water content in the state of Utah using
water equivalent of snow density (WESD) measurements. The
`utsnow`

data that is part of the package `remap`

contains WESD in mm water measured on April 1st, 2011 at 394 locations
within and near the state of Utah. The `utws`

data in
`remap`

is a set of polygons representing watersheds defined
by the US Geological Survey. These watersheds are defined by a hierarchy
of hydrologic unit cods (HUC) with a two-digit designation for
continental scale watersheds (HUC2). We will build a regionalized model
with `remap`

using HUC2 watershed regions.

```
<- maps::map("state", region = "utah", plot = FALSE, fill = TRUE) %>%
utstate ::st_as_sf() %>%
sf::st_transform(crs = 4326)
sf
ggplot(utws, aes(fill = HUC2)) +
geom_sf(alpha = 0.5) +
geom_sf(data = utstate, fill = "NA", size = 1) +
geom_sf(data = utsnow) +
ggtitle("Modeling Data and Regions",
"HUC2 regions are made up of smaller HUC4 regions.") +
theme_void()
```

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\(~\)

The `remap`

function requires the following 4
parameters:

`data`

- Observations with a spatial component used for modeling.`regions`

- Polygons describing the regions used to build each model.`model_function`

- A function that takes a subset of the`data`

and returns a model.`buffer`

- Observations are located within and near a region are used to build each model. The`buffer`

parameter dictates how near an observation must be to a location to be included in that regionâ€™s model.

The following parameters are optional:

`region_id`

- A column name of`regions`

which describes which polygons to include in each region. This is helpful if any region has polygons described in multiple rows in the`sf`

object. The`region_id`

allows for combinations of smaller polygons into larger regions.`min_n`

- The minimum number of observations required to build a model in each region. If a region does not contain enough observations within its border and buffer zone, the nearest`min_n`

observations will be used for modeling.

In this section, we use some simple linear models to model snow water
content in Utah. First a global model using all data, then a regional
model using `remap`

. The WESD measurement in our example data
commonly shares a log-linear relationship with elevation in mountainous
western states. Many locations in Utah have a value of 0 for WESD on
April first. Since zero snow values in log transformed variables add
another level of complexity to the modeling process, we remove them for
now and make a new dataset called `utsnz`

.

`<- utsnow %>% dplyr::filter(WESD > 0) utsnz `

The relationship between the log transformed WESD and elevation of
`utsnz`

is visualized as:

```
ggplot(utsnz, aes(x = ELEVATION, y = WESD)) +
geom_point() +
geom_smooth(method = "lm") +
scale_y_log10() +
theme_minimal()
#> `geom_smooth()` using formula 'y ~ x'
```

The resubstitution mean squared error (MSE) for such a model is:

```
<- lm(log(WESD) ~ ELEVATION, data = utsnz)
lm_global
<- mean((utsnz$WESD - exp(predict(lm_global, utsnz)))^2)
lm_global_mse
lm_global_mse#> [1] 121370.7
```

The `utsnz`

data describes which watersheds each location
falls in with the `HUC2`

columns. (Note that
`remap`

does not require these columns in the data when
building models using the `utws`

regions.) Here is what the
relationship between log(WESD) and elevation looks like for each of the
HUC2 regions:

```
ggplot(utsnz, aes(x = ELEVATION, y = WESD)) +
facet_grid(HUC2 ~ .) +
geom_point() +
geom_smooth(method = "lm") +
scale_y_log10() +
theme_minimal()
#> `geom_smooth()` using formula 'y ~ x'
```

The linear models for each HUC2 region seem to fit a little better
than the global linear model; however, it looks like HUC2 region 15 does
not have enough data to build a very confident model. Using
`remap`

to build models for each region, some of the nearest
observations to HUC2 region 15 are added to build a better model by
using the `min_n`

parameter to set the minimum number of
observations per region to 10.

For this modeling task, any observation within 20km of a region is to
be included in that regionâ€™s model using the `buffer`

parameter. The `lm`

function requires a `formula`

,
so `formula`

is added as an extra parameter in remap. Since
`remap`

makes smooth predictions over the entire surface, a
`smooth`

parameter is required in the `predict`

function that dictates the distance from region borders where the
smoothing process will start. We set `smooth`

to 10km for
this example.

```
<- Sys.time()
t1 <- remap::remap(
lm_huc2 region_id = HUC2,
utsnz, utws, buffer = 20, min_n = 10,
model_function = lm,
formula = log(WESD) ~ ELEVATION
)
<- mean((utsnz$WESD - exp(predict(lm_huc2, utsnz, smooth = 10)))^2)
lm_huc2_mse <- Sys.time()
t2
# mse
lm_huc2_mse#> [1] 85725.65
# runtime
round(difftime(t2, t1), 1)
#> Time difference of 0.5 secs
```

The output of `remap`

returns a list that contains a list
of models built in each region and an `sf`

data frame storing
the region polygons. The models for each region can be accessed
directly. For example, the coefficients for each model can be accessed
with the following code:

```
sapply(lm_huc2$models, function(x) x$coefficients)
#> 14 15 16 17
#> (Intercept) 0.557864695 -2.292085274 0.564719359 1.8300546
#> ELEVATION 0.001914744 0.003114987 0.002140786 0.0018686
```

The resulting remap model has a 29.4% reduction in resubstitution MSE using separate linear models for each of the HUC2 regions rather than the global linear model. This increase in accuracy is likely to be even more drastic when problems span greater areas, such as a model for an entire continent.

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\(~\)

\(~\)

Many modeling techniques that partition space depend on the distance
between prediction locations and the *center* of each region.
However, the center is a poor characterization of irregularly shaped
regions. The border smoothing method in `remap`

uses the
distances to region *borders* rather than region centers. This
allows for regions of any shape being used for modeling. Keep in mind
that `remap`

requires distance calculations between each
observation and the region boundaries in both the modeling and
prediction steps. This tends to be computationally expensive with large
numbers of points and/or complex region boundaries. This section
outlines the steps taken to ameliorate the computational burden of
`remap`

which allow `remap`

to scale to large
problems.

Many spatial processes require continuous polygons (i.e.Â no gaps in region boundaries), which limits the degree of polygon simplification that can be achieved. One desirable feature of remap is the ability of each model to make smooth predict outside of polygons within a smoothing zone, which smooths over any small gaps in polygons that occurs in an aggressive geometrical simplification. Thus, by only preserving the macro structure of the input polygons, we can greatly speed up distance computations without losing fidelity in model predictions.

Distance calculation time can be dramatically reduced by simplifying
the polygons passed to `remap`

using the `sf`

package `st_simplify`

function. The function gives a warning
that can be ignored for our purposes, we are only trying to preserve the
macro structure and the regions are not near any poles. Gaps at region
borders appear, but remap has the ability to predict outside of regions
and predictions will remain smooth as long as the gaps arenâ€™t wider than
two times the `smooth`

parameter. Notice how the simplified
polygons retain the basic structure of the original regions:

```
<- utws %>% sf::st_simplify(dTolerance = 5000)
utws_simp
rbind(
%>% dplyr::mutate(TYPE = "Original Watershed Polygons"),
utws %>% dplyr::mutate(TYPE = "Simplified Watershed Polygons")
utws_simp %>%
) ggplot() +
facet_grid(.~TYPE) +
geom_sf() +
theme_void()
```

Simplifying the polygons doesnâ€™t drastically change the computation time in this particular example, but some regions can contain massive polygons with details that are unnecessary for regional modeling.

`redist`

The `remap`

and `predict`

functions both
internally call a function called `redist`

to calculate
distances from points to polygons. The user can directly use the
function `redist`

to pre-compute distances from points to
polygons and use the pre-computed distances as direct inputs in the
`remap`

function. The pre-computing step greatly reduces
computational costs if multiple regional models must be made with the
same input data and polygons.

Distances from prediction locations need only be computed to polygon
boundaries for which the prediction location falls within their
smoothing zone. Buffered polygons can be used to quickly determine
candidate observations for distance calculations in each region. The
`max_dist`

parameter of `redist`

can be used to
make these buffered polygons. This drastically reduces the number of
points for which distance calculations must be performed and greatly
improves computation times.

```
<- remap::redist(utsnz, utws_simp, region_id = HUC2)
huc2_dist_nz head(huc2_dist_nz)
#> Units: [km]
#> 14 15 16 17
#> [1,] 0.00000 333.2641 273.7183 438.11364
#> [2,] 0.00000 430.5133 247.8148 357.75421
#> [3,] 0.00000 240.2121 285.3032 527.65980
#> [4,] 75.49279 387.8302 0.0000 64.65256
#> [5,] 80.50456 325.9622 0.0000 152.99741
#> [6,] 97.42136 175.9546 0.0000 287.59656
```

The newly created distance matrix can be sent to `remap`

and `predict`

as a parameter. Run the following code and
notice how much faster the `remap`

and `predict`

functions run when the distances are pre-calculated.

```
<- Sys.time()
t1 <- remap(
lm_huc2 region_id = HUC2,
utsnz, utws_simp, buffer = 20, min_n = 10,
model_function = lm,
formula = log(WESD) ~ ELEVATION,
distances = huc2_dist_nz
)
<- mean(
lm_huc2_mse $WESD -
(utsnzexp(predict(lm_huc2, utsnz, smooth = 10,
distances = huc2_dist_nz)))^2
)<- Sys.time()
t2
# mse
lm_huc2_mse#> [1] 87730.86
# runtime
round(difftime(t2, t1), 1)
#> Time difference of 0 secs
```

The MSE for this model is slightly different than previous regional linear model since the simplified polygons are being used. This is a small problem so simplifying polygons and precomputing distances might be unnecessary; however, these steps can make a large modeling and mapping problem feasible with limited computing resources.

Since calculating distances to polygons is independent for each
polygon, `redist`

can be run in parallel by setting the
`cores`

to a number greater than one. Models are built and
make predictions independent of one another so the `remap`

function and `predict`

method also have a `cores`

parameter for parallel computing. This means that distance calculations,
modeling, and predicting processes can all be run in parallel.

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\(~\)

While linear models are great for illustration, many spatial modeling
approaches will require more complex regional model forms. The
`remap`

function is flexible enough to handle arbitrary model
inputs and outputs, with the only requirement being that the model
output must be compatible for use with the generic `predict`

function and return a numeric output.

The `remap`

function takes a `model_function`

as a parameter. The `model_function`

must have the following
two features:

- Its first unnamed function argument must take a subset of an
`sf`

data point object, - The function must output an object with a generic predict function that returns a vector of numeric values.

Note that named function arguments can be supplied at the end of the
remap function, as was the case with the `formula`

argument
in the linear model example shown previously. The key is that the first
unnamed parameter be dedicated to data input.

Suppose we want to make a generalized additive model (GAM) that is
limited to only making positive predictions. We also donâ€™t want to
predict a number greater than the highest observed response in each
region to avoid over extrapolation. This can be accomplished through
wrapper functions for both `gam`

and `predict.gam`

that restrict predicted values to a specified range. We can also make
the option to return standard errors rather than predictions.
`remap`

has an option to combine standard errors on region
borders to find an upper bound of combined standard errors.

```
<- function(data, fml) {
gam_limit <- mgcv::gam(fml, data = data)
g_model <- max(data$WESD)
upper_lim
<- list(g_model = g_model, upper_lim = upper_lim)
out class(out) <- "gam_limit"
return(out)
}
<- function(object, newobs, se.fit = FALSE) {
predict.gam_limit if (nrow(newobs) != 0) {
if (se.fit) {
return(predict(object$g_model, newobs, se.fit = TRUE)$se.fit)
else {
} <- predict(object$g_model, newobs)
preds
< 0] <- 0
preds[preds > object$upper_lim] <- object$upper_lim
preds[preds
return(preds)
}
}return(NULL)
}
```

The following code tests a GAM model where elevation and splines on the sphere are used as predictors. We can use the functions written to do a GAM with remap to easily do cross validation on a global model:

```
# Create vector for cross-validation
set.seed(42)
<- sample(1:10, nrow(utsnow), replace = TRUE)
cv_k
# Initialize predictions
<- rep(as.numeric(NA), nrow(utsnow))
gam_global_preds
# Formula for global GAM
<- WESD ~ s(ELEVATION, k = 5) + s(LATITUDE, LONGITUDE, bs = "sos", k = 50)
global_fml
# Build and test models with 10 fold cross-validation
for (i in 1:10) {
<- cv_k == i
index <- gam_limit(utsnow[!index, ], fml = global_fml)
gam_global <- predict(gam_global, utsnow[index, ])
gam_global_preds[index]
}
# Calculate MSE
<- mean((utsnow$WESD - gam_global_preds)^2)
gam_global_mse
gam_global_mse#> [1] 23875.94
```

This model is much better than either of the basic linear models, even though the GAM accuracy is measured on cross-validation rather than resubstitution error and the GAM model is also modeling the zero valued observations.

First, the distances are pre-calculated so the distance calculations
arenâ€™t repeated 10 different times when doing cross validation. The
distances return a matrix where each row corresponds to each observation
in the data. The cross validation only uses a subset of the data, so the
corresponding subset of distances should be passed to `remap`

during each modeling step.

`<- remap::redist(utsnow, utws, region_id = HUC2) huc2_dist `

HUC2 regions are used to build a regionalized GAM models with
`remap`

. We will reduce the knots on the splines on the
sphere from 50 to 25 so we donâ€™t need so many degrees of freedom for
each model. The `min_n`

can be set to 35 to allow at least 5
degrees of freedom per model.

```
# Initialize predictions
<- rep(as.numeric(NA), nrow(utsnow))
gam_huc2_preds
# Formula for regional GAMs
<- WESD ~ s(ELEVATION, k = 5) + s(LATITUDE, LONGITUDE, bs = "sos", k = 25)
gam_huc2_fml
# Build and test models with 10 fold cross-validation
for (i in 1:10) {
<- cv_k == i
index
<- remap::remap(
gam_huc2 !index, ], utws, region_id = HUC2,
utsnow[model_function = gam_limit,
buffer = 20, min_n = 35,
distances = huc2_dist[!index, ],
fml = gam_huc2_fml
)
<- predict(
gam_huc2_preds[index]
gam_huc2, utsnow[index, ],smooth = 10,
distances = huc2_dist[index, ]
)
}
# Calculate MSE
<- mean((utsnow$WESD - gam_huc2_preds)^2)
gam_huc2_mse
gam_huc2_mse#> [1] 16620.73
```

The HUC2 regionalized GAM has 30.4% better MSE than the global GAM model. With the custom functions that we wrote, we can get both smooth predictions and smoothed combined standard errors.

```
<- remap::remap(
gam_huc2 region_id = HUC2,
utsnow, utws, model_function = gam_limit,
buffer = 20, min_n = 35,
distances = huc2_dist,
fml = gam_huc2_fml
)
predict(gam_huc2, utsnow[1:3, ], smooth = 25)
#> [1] 7.629856 248.959425 8.694482
predict(gam_huc2, utsnow[1:3, ], smooth = 25, se = TRUE, se.fit = TRUE)
#> Upper bound for standard error calculated at each location.
#> Reminder: make sure that the predict function outputs a vector of standard error values for each regional model in your remap object.
#> [1] 35.12763 42.04082 33.69042
```

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\(~\)

\(~\)

A toy model is best used to show how smooth predictions work since the Utah snow water content models have extreme values and sharp changes with elevation. The toy model has 3 regional models with contrived response variables that consists of an affine combination of longitude and latitude values. The left region predicts \(lat - lon\), the bottom region predicts \(lon - lat - 0.4\), and the right region predicts \(lat - lon + 0.3\)

```
# Make regions
<- tibble::tribble(
toy_regions ~id, ~geometry,
"a", sf::st_polygon(list(matrix(c(0, 0, 2, 0, 6, 3, 4, 10, 0, 10, 0, 0)*.1, ncol = 2, byrow = TRUE))),
"b", sf::st_polygon(list(matrix(c(2, 0, 10, 0, 10, 4, 6, 3, 2, 0)*.1, ncol = 2, byrow = TRUE))),
"c", sf::st_polygon(list(matrix(c(4, 10, 6, 3, 10, 4, 10, 10, 4, 10)*.1, ncol = 2, byrow = TRUE)))
%>%
) ::st_as_sf(crs = 4326)
sf
# Manually make a toy remap model
<- function(x) {
make_toy class(x) <- "toy_model"
return(x)
}<- list(
remap_toy_model models = list("a" = make_toy("a"),
"b" = make_toy("b"),
"c" = make_toy("c")),
regions = toy_regions,
region_id = "id"
)class(remap_toy_model) <- "remap"
# Make a prediction method for toy_model
<- function(object, data) {
predict.toy_model <- sf::st_coordinates(data)[, "X"]
x <- sf::st_coordinates(data)[, "Y"]
y if (object == "a") {
- x
y else if (object == "b") {
} - y - 0.4
x else {
} - x + 0.3
y
}
}
# Make a grid over the regions for predictions
<- sf::st_make_grid(toy_regions, cellsize = .01, what = "corners") %>%
grd ::st_sf() sf
```

The regions cover the following area:

```
::ggplot(toy_regions, aes(fill = id)) +
ggplot2geom_sf(color = "black", size = 1) +
ggtitle("Toy Regions") +
theme_bw()
```

The `remap_toy_model`

object can now be used to make
predictions on the `grd`

object. There are 10201 points in
the `grd`

object but the regions are simple, so it will not
take long to find distances. Two predictions will be made, the
`SHARP`

predictions will have a smoothing parameter of zero
and the `SMOOTH`

predictions will have a smoothing parameter
set to 30km.

```
<- grd %>%
grd_pred ::mutate(SHARP = predict(remap_toy_model, grd, smooth = 0),
dplyrSMOOTH = predict(remap_toy_model, grd, smooth = 30),
LON = sf::st_coordinates(.)[, "X"],
LAT = sf::st_coordinates(.)[, "Y"])
```

The smooth predictions from the `remap`

object start to
become a weighted average of regional predictions when the predictions
are within 30km of a region border. The following plots show what is
happening with both the `SHARP`

and `SMOOTH`

predictions at predicted values at all locations and specific plots
along the 0.8 degree N line. Notice how the predictions at the borders
of the toy regions are smoothed:

```
ggplot(toy_regions) +
geom_sf() +
geom_tile(data = grd_pred, aes(x = LON, y = LAT, fill = SHARP)) +
scale_fill_viridis_c(limits = c(-0.3, 1)) +
geom_hline(yintercept = 0.8) +
ggtitle("Sharp Predictions", "Black line corresponds to x-axis of the next plot.") +
xlab("") + ylab("") +
theme_bw()
```

```
ggplot(grd_pred %>% dplyr::filter(LAT == 0.8),
aes(x = LON, y = SHARP)) +
geom_line(size = 1) +
ggtitle("Sharp Predictions at 0.8 degrees N") +
theme_minimal()
```

```
ggplot(toy_regions) +
geom_sf() +
geom_tile(data = grd_pred, aes(x = LON, y = LAT, fill = SMOOTH)) +
scale_fill_viridis_c(limits = c(-0.3, 1)) +
geom_hline(yintercept = 0.8) +
ggtitle("Smooth Predictions", "Black line corresponds to x-axis of the next plot.") +
xlab("") + ylab("") +
theme_bw()
```

```
ggplot(grd_pred %>% dplyr::filter(LAT == 0.8),
aes(x = LON, y = SMOOTH)) +
geom_line(size = 1) +
ggtitle("Smooth Predictions at 0.8 degrees N") +
theme_minimal()
```

The `remap`

package provides a way to build regional
spatial models given a set of observations and a set of regions. The
resulting model can make predictions that have no discontinuities at
region borders and scales well to large problems.