Often, in an applied setting, it is desirable to change the spatial support of some variables in order to conduct either association or regression analysis. This package provides functions to deal with this problem under two different approaches, a model-based one and a (non-parametric) spatial interpolation.

The purpose of this vignette is to illustrate how to convert
`sf`

(Pebesma 2018) objects to
objects support by the `smile`

package. Besides these two
packages, we are going to use the `ggplot2`

(Wickham 2011) package for the data
visualization.

This vignette is useful when the user wants to pursue the model-based approach. The methodology is based on the assumption that areal data (e.g., variables measured at census tracts) are composed by averages of a continuous underlying process Johnson, Diggle, and Giorgi (2020). In practice, there exists an identifiability problem when estimating some variance parameters, which can be seen either as small scale variation (nugget effect) or measurement errors. When fitting models, the users may chose to ignore the measurement error associated with the problem. Model fitting and prediction are explained in the vignette available on this link.

To illustrate how to convert `sf`

polygons into
`spm`

objects, we are going to use the datasets provided by
Johnson, Diggle, and Giorgi (2020). For
this data, we have the Life Expectancy at Birth (LEB) and the Index of
Multiple Deprivation (IMD) in Liverpool. These variables are observed at
the Middle Super Output Areas (MSOA) and Lower Super Output Areas
(LSOA), respectively. After loading our package, the datasets can be
loaded by simply running the chunk of code below. Figure \(\ref{fig:leb-msoa}\) displays the LEB at
the MSOA.

```
data(liv_lsoa) # loading the LSOA data
data(liv_msoa) # loading the MSOA data
## workaround for compatibility with different PROJ versions
st_crs(liv_msoa) <-
st_crs(liv_msoa)$input
st_crs(liv_lsoa) <-
st_crs(liv_lsoa)$input
```

```
ggplot(data = liv_msoa,
aes(fill = leb_est)) +
geom_sf(color = "black",
lwd = .1) +
scale_fill_viridis_b(option = "H") +
theme_minimal()
```

Now, suppose we are interested in estimating the LEB at the LSOA, so we will be able to conduct association analysis between LEB and IMD at the MSOA level. We assume, that the LEB, denoted \(Y(\cdot)\), is driven by a stationary and isotropic continuous Gaussian process over the region of study, such that, the observed data at the \(i\)-th MSOA area (denoted \(A_i\)) is an average of this underlying process. If we knew the parameters and covariance function associated with this process, then the LEB at the \(i\)-th MSOA would be as follow \[ Y(A_i) = \frac{1}{\lvert A_i \rvert} \int_{A_i} Y(\mathbf{s}) \, {\rm d} \mathbf{s}, \] where \(\lvert A_i \rvert\) stands for the area associated with the region \(A_i\).

Similarly, \[\begin{align*} {\rm E}[Y(A_i)] & = \frac{1}{\lvert A_i \rvert} \int_{A_i} {\rm E}[Y(\mathbf{s})] \, {\rm d} \mathbf{s} \\ & = \frac{1}{\lvert A_i \rvert} \int_{A_i} \mu \, {\rm d} \mathbf{s} \\ & = \mu, \end{align*}\] and \[\begin{align*} {\rm Cov}[Y(A_i), Y(A_j)] & = \frac{1}{\lvert A_i \rvert \lvert A_j \rvert} \int_{A_i \times A_j} {\rm Cov}[Y(\mathbf{s}, Y(\mathbf{s}')] \, {\rm d} \mathbf{s} \, {\rm d} \mathbf{s'} \\ & = \frac{1}{\lvert A_i \rvert \lvert A_j \rvert} \int_{A_i \times A_j} {\rm C}( \lVert \mathbf{s} - \mathbf{s}' \rVert ; \boldsymbol{\theta}) \, {\rm d} \mathbf{s} \, {\rm d} \mathbf{s'}, \end{align*}\] where \(\lVert \mathbf{s} - \mathbf{s}' \rVert\) is the Euclidean distance between the coordinates \(\mathbf{s}\) and \(\mathbf{s}'\), and \({\rm C}( \lVert \mathbf{s} - \mathbf{s}' \rVert ; \boldsymbol{\theta})\) is an isotropic covariance function depending on the parameter \(\boldsymbol{\theta}\).

In practice, however, the integrals in the equation above are hard to
be evaluated analytically. A common workaround is to evaluate them
either numerically or by Monte Carlo integration (Gelfand, Zhu, and Carlin 2001). When using the
function `sf_to_spm`

, the parameter `"type"`

controls the method of integration. The options are
`"regular"`

(or `"hexagonal"`

) for numerical
integration, or `"random"`

for Monte Carlo integration.
Regardless of the `"type"`

chosen, we have to generate a grid
of points over the study region. When doing so, we may chose whether we
want to approximate the integral within each area with the same
precision or if we want the precision to vary according to the size of
the polygon. This is controlled by the parameter
`"by_polygon"`

, which is a boolean scalar. When set to
`TRUE`

, all the integrals will be estimated with the same
precision, regardless of the size of the polygon. On the other hand, if
this parameter is set to `FALSE`

, the grid of points will be
generated over the whole study region and, afterwards, the points will
be attributed to the areas they are contained in. This way, larger
polygons will contain more points and, thus, the respective integrals
will have a smaller numerical error. Lastly, there exists a parameter
called `"npts"`

. This parameter controls the number of points
used to compute this integrals. We may either input a vector with the
same length as the number of areas or a scalar. When inputting a scalar,
this scalar will stand for the number of points over the whole city if
`by_polygon = FALSE`

and the number of points per polygon
(area) otherwise.

If we wish to estimate the LEB in the LSOA areas, we will need to
create a `spm`

object associated with this variable, fit the
model, and then compute the predictions. The chunk of code below shows
how to convert the `liv_msoa`

(of class `sf`

) to a
`spm`

object. In this case, we are generating a grid of 1000
points over the whole city of Liverpool, then we will be attributing
each of these points to the area they are contained in. Also, the
`"poly_ids"`

argument is a string indicating the variable in
the `liv_msoa`

dataset that contains the unique identifier
associated with each area. The argument `"var_ids"`

is a
string as well but this indicates the “response variable”. That is, the
variable that for which we will be interested in fitting a model to.

```
msoa_spm <-
sf_to_spm(sf_obj = liv_msoa, n_pts = 1000,
type = "regular", by_polygon = FALSE,
poly_ids = "msoa11cd", var_ids = "leb_est")
```

Finally, the Figure below displays the grids associated with each of
the possible combinations of the parameters `type`

and
`by_polygon`

when calling the `sf_to_spm`

function.

For details on fitting models and making predictions, see this vignette.

Gelfand, Alan E, Li Zhu, and Bradley P Carlin. 2001. “On the
Change of Support Problem for Spatio-Temporal Data.”
*Biostatistics* 2 (1): 31–45.

Johnson, Olatunji, Peter Diggle, and Emanuele Giorgi. 2020.
“Dealing with Spatial Misalignment to Model the Relationship
Between Deprivation and Life Expectancy: A Model-Based
Geostatistical Approach.” *International Journal of Health
Geographics* 19 (1): 1–13.

Moraga, Paula, Susanna M Cramb, Kerrie L Mengersen, and Marcello Pagano.
2017. “A Geostatistical Model for Combined Analysis of Point-Level
and Area-Level Data Using INLA and SPDE.” *Spatial
Statistics* 21: 27–41.

Pebesma, Edzer. 2018. “Simple Features for R:
Standardized Support for Spatial Vector Data.”
*The R Journal* 10 (1): 439–46. https://doi.org/10.32614/RJ-2018-009.

Wickham, Hadley. 2011. “Ggplot2.” *Wiley
Interdisciplinary Reviews: Computational Statistics* 3 (2): 180–85.

Wilson, Katie, and Jon Wakefield. 2020. “Pointless Spatial
Modeling.” *Biostatistics* 21 (2): e17–32.