This is is an introductory presentation of the `somspace` package, as implemented in the study of Markonis and Strnad [2019], to detect spatial drought patterns in Europe.

## Data import

In this study the Old World Drought Atlas [Cook et al., 2016] was used, as pre-processed by Markonis et al. [2018]. The file can be downloaded here, and is a `data.table` object saved as an `.rds` file. Alternatively, the `owda` data object that is included in the package can be used, which covers only period 1500-2012.

    library(somspace)
    str(owda)

## Data preparation

First of all, we have to transform the raw data from `data.table` to `somin` class. If the original data have different structure/type them it is important to transform them to a 4-column `data.table`, containing time, lat, lon and the variable.

    inp <- sominp(owda) #depending on data set size may take some time

The `somin` class is a list with three elements. The first one, `input_for_som`, is a `matrix` that will be used by `kohonen::som` function to generate the SOM. The second one, `coords`, is a `data.table` with the point ids and their coordinates. The last one, `input_dt` is also a `data.table` with the raw data that were used to generate `input_for_som`.

    str(inp)

## SOM generation

The generation of SOM is straightforward:

    my_som <- somspa(inp)

Which creates a `somsp` object that contains the som, the nodes properties and the original data.

    str(somsp)

Since `somspa()` uses the function `som()` from `kohonen` package it can also take `som()` arguments. For instance, to create a 6 x 6 map after 1000 iterations:

    my_som <- somspa(inp, grid = somgrid(6, 6), rlen = 1000)

## SOM analysis

The `somsp` objects can be easily plotte by `plot()` or summarized by `summary()` functions.

    plot(my_som)
    summary(my_som)

Additionaly, the average time series of specific SOMs can be plotted by `plot_ts`:

    plot_ts(my_som, 12)
    plot_ts(my_som, c(1, 12, 21, 39))
    plot_ts(my_som, 1:max(my_som$summary$node)) #plots all soms

## Further classification of SOMs in regions

To further reduce the number of regions with similar characteristics `somregs()` is applied. It uses the `hclust()` function for Hierarchical cluster analysis and its default arguments can be changed accordingly. Please not that the `n` argument refers to the maximum number of regions and not to a single one. `somregs()` will merge SOMs to similar regions starting from 2 and reachinh `n` and will keep them all in the resulting `regs` object.

    my_regions <- somregs(my_som, 12)
    my_regions <- somregs(my_som, 17, method = "ward.D2")

`regs` objects which are two-element lists. The `regions` element contains the information about the regions and the `input_dt` the time series of the original dataset. Similarly to `somsp` objects, `regs` can be plot as maps or time series.

    plot(my_regions, c(2, 5, 9, 13), 2, 2)
    plot_ts(my_regions, 2)

## Complex network analysis

Finally, a specific classification of certain number of regions can be analysed through canonical networks and ploted as map with `cnet()`. The threshold, `thres`, refers to the cross-correlation coefficient between the averaged time series of each region; only values above `thres` are considered to be network connections.

    cnet(my_regions, 12, 0.3)