# Quickstart

# Introduction

In the following we will demonstrate an idealized workflow based on a subset of the ESA Landcover dataset that is delivered together with this package. You can follow along the code snippets below to reproduce the results. Please note that to reduce the time it takes to process this vignette, we will not download any resources from the internet. In a real use case, thus processing time might substantially increase because resources have to be downloaded and real portfolios might be larger than the one created in this example.

This vignette assumes that you have already followed the steps in Installation and have familiarized yourself with the terminology used in the package. If you are unfamiliar with the terminology used here, please head over to the Terminology article to learn about the most important concepts.

The idealized workflow for using `{mapme.biodiversity}` consists of the following steps:

- initialize your portfolio using an sf-object containing only geometries of type POLYGON
- decide which indicator(s) you wish to calculate and download the required resource(s)
- conduct your indicator calculation, which adds a nested list column to your portfolio object
- continue your analysis in R or decide to export your results to a GeoPackage to use it with other geospatial software

# Getting started

First, we will load the `{mapme.biodiversity}` and the `{sf}` package for handling spatial vector data. For tabular data handling, we will also load the `{dplyr}` and `{tidyr}`packages. Then, we will read an internal GeoPackage which includes the geometry of a protected area in the Dominican Republic from the WDPA database.

    library(mapme.biodiversity)
    library(sf)
    library(dplyr)
    library(tidyr)

    aoi_path <- system.file("extdata", "sierra_de_neiba_478140.gpkg", package = "mapme.biodiversity")
    (aoi <- read_sf(aoi_path))
    #> Simple feature collection with 1 feature and 4 fields
    #> Geometry type: MULTIPOLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 1 × 5
    #>   WDPAID NAME            DESIG_ENG     ISO3                                 geom
    #>    <dbl> <chr>           <chr>         <chr>                  <MULTIPOLYGON [°]>
    #> 1 478140 Sierra de Neiba National Park DOM   (((-71.76134 18.66333, -71.76067 1…

The sf-object contains a single object of geometry type `MULTIPOLYGON`. The `{mapme.biodiversity}` package, however, only supports geometries of type `POLYGON`, thus we need to cast the geometry before we advance. The resulting sf object also contains some metadata, that will be retained throughout the complete workflow. Because some of the cast geometries represent artefacts of the digitization process, in this example we will subset to only the largest polygon.

    (aoi <- st_cast(aoi, to = "POLYGON")[1, ])
    #> Warning in st_cast.sf(aoi, to = "POLYGON"): repeating attributes for all
    #> sub-geometries for which they may not be constant
    #> Simple feature collection with 1 feature and 4 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 1 × 5
    #>   WDPAID NAME            DESIG_ENG     ISO3                                 geom
    #>    <dbl> <chr>           <chr>         <chr>                       <POLYGON [°]>
    #> 1 478140 Sierra de Neiba National Park DOM   ((-71.76134 18.66333, -71.76067 18…

In the following, we will simulate a portfolio consisting of several polygons (assets, in the jargon of this package). To this end, we create smaller polygons within the original extent of the main polygon. This way, we can showcase the behavior of the `{mapme.biodiversity}` package for portfolios that contain multiple assets. We will only select single assets with geometry type `POLYGON` that lie within the original boundary of the protected area.

    aoi_gridded <- st_make_grid(
      x = st_bbox(aoi),
      n = c(10, 10),
      square = FALSE) %>%
      st_intersection(aoi) %>%
      st_as_sf() %>%
      mutate(geom_type = st_geometry_type(x)) %>%
      filter(geom_type == "POLYGON") %>%
      select(-geom_type, geom = x) %>%
      st_as_sf()

    metanames <- names(st_drop_geometry(aoi))
    aoi_gridded[metanames] <- st_drop_geometry(aoi)

# Initialization of a portfolio

Now, we are ready to initiate a portfolio object containing multiple assets. We use the `init_portfolio()` function and set some attributes that are important for the subsequent processing. The function will add a unique identifier column called ‘assetid’ that is used to uniquely identify each asset in the portfolio.

    # copying package internal resource to a temporary location
    outdir <- file.path(tempdir(), "mapme.biodiversity")
    dir.create(outdir)
    resource_dir <- system.file("res", package = "mapme.biodiversity")
    file.copy(resource_dir, outdir, recursive = TRUE)
    #> [1] TRUE

    sample_portfolio <- init_portfolio(
      x = aoi_gridded,
      years = 2010:2015,
      outdir = file.path(outdir, "res"),
      tmpdir = outdir,
      verbose = TRUE
    )
    plot(sample_portfolio["assetid"])

The first argument, `x`, is the sf-object that we want to turn into a portfolio. The argument `years` allows us to restrict our analysis to certain years only. Certain resources with a temporal dimension are only processed for the portfolio’s temporal extent. All resource and indicator functions will inform the user if the portfolio’s temporal extent does not intersect. The `outdir` and `tmpdir` arguments point towards directories on the local file system of your machine. If these directories do not exist, the package attempts to create them. The `outdir` cannot be equal to the `tmpdir` argument. All downloaded resources will be written to respective directories nested within `outdir`. Any intermediate files during the calculation of an indicator will be written to `tmpdir`. Thus, please ensure that you have write access to both directories and that there is sufficient free disk space to support the analysis of your portfolio.

Once you request a specific resource for your portfolio, only those files will be downloaded that are missing to match its spatio-temporal extent. This behavior is beneficial, e.g. in case you share the `outdir` between different projects to ensure that only resources matching your current portfolio are returned.

Finally, the `verbose` logical controls whether or not the package will print informative messages during the calculations. Note, that even if set to `FALSE`, the package will inform users about any potential errors or warnings.

# Getting the right resources

You can check which indicators are available via the `available_indicators()` function. For this, we inspect the names of the returned object:

    names(available_indicators())
    #>  [1] "active_fire_counts"           "active_fire_properties"
    #>  [3] "biome"                        "deforestation_drivers"
    #>  [5] "drought_indicator"            "ecoregion"
    #>  [7] "elevation"                    "fatalities"
    #>  [9] "landcover"                    "mangroves_area"
    #> [11] "population_count"             "precipitation_chirps"
    #> [13] "precipitation_wc"             "soilproperties"
    #> [15] "temperature_max_wc"           "temperature_min_wc"
    #> [17] "traveltime"                   "treecover_area"
    #> [19] "treecover_area_and_emissions" "treecoverloss_emissions"
    #> [21] "tri"

Say, we are interested in the `population_count` indicator. We can learn more about this indicator and its required resources by using either of the commands below or, if you are viewing the online version, head over to the population_count documentation.

    ?population_count
    help(population_count)

By inspecting the help page we learned that this indicator requires the `worldpop` resource and it requires to specify two extra arguments: the population statistic to calculate and the eninge to be used for the calculation (learn more about engines here).

With that information at hand, we can start to retrieve the required resource. We can learn about all available resources using the `available_resources()` function:

    names(available_resources())
    #>  [1] "chirps"                    "esalandcover"
    #>  [3] "fritz_et_al"               "gfw_emissions"
    #>  [5] "gfw_lossyear"              "gfw_treecover"
    #>  [7] "gmw"                       "nasa_firms"
    #>  [9] "nasa_grace"                "nasa_srtm"
    #> [11] "nelson_et_al"              "soilgrids"
    #> [13] "teow"                      "ucdp_ged"
    #> [15] "worldclim_max_temperature" "worldclim_min_temperature"
    #> [17] "worldclim_precipitation"   "worldpop"

For the purpose of this vignette, we are going to download the `worldpop` resource. We can get more detailed information about a given resource, by using either of the commands below to open up the help page. If you are viewing the online version of this documentation, you can simply head over to the worldpop resource documentation.

    ?worldpop
    help(worldpop)

We can now make the `worldpop` resource available for our portfolio. We will use a common interface that is used for all resources, called `get_resources()`. We have to specify our portfolio object and the names of the resource(s) we wish to download. Additional arguments for the specific resource can be specified. The output of the function is the portfolio object with its attributes appended for the new resource, thus we simply can overwrite the `sample_portfolio` variable.

    sample_portfolio <- get_resources(x = sample_portfolio, resources = "worldpop")
    #> Starting process to download resource 'worldpop'........
    #> Skipping existing files in output directory.

In case you want to download more than one resource, you can use the same interface and the resources will be made available sequentially. Required arguments for a resource are simply added as usual:

    sample_portfolio <- get_resources(
      x = sample_portfolio,
      resources = c("worldpop", "gfw_treecover"),
      vers_treecover = "GFC-2021-v1.9"
    )

# Calculate specific indicators

The next step consists of calculating specific indicators. Note that each indicator requires one or more resources that were made available via the `get_resources()` function explained above. Here, we are going to calculate the `population_count` indicator which is based on the `worldpop` resource. Since the resource has been made available in the previous step, we can continue requesting the calculation of our desired indicator. Note the command below would issue an error in case a required resource has not been made available via `get_resources()` beforehand.

    sample_portfolio <- calc_indicators(sample_portfolio, indicators = "population_count",
                                        stats_popcount = "sum", engine = "zonal")

Now let’s take a look at the results. We will select only some of the metadata and the output indicator column to get a clearer picture of what has happened.

    (sample_portfolio <- sample_portfolio %>% select(assetid, WDPAID, population_count))
    #> Simple feature collection with 20 features and 3 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 20 × 4
    #>    assetid WDPAID population_count                                          geom
    #>      <int>  <dbl> <list>                                           <POLYGON [°]>
    #>  1       1 478140 <tibble [6 × 2]> ((-71.78546 18.658, -71.78546 18.67313, -71.…
    #>  2       2 478140 <tibble [6 × 2]> ((-71.80292 18.68321, -71.78546 18.67313, -7…
    #>  3       3 478140 <tibble [6 × 2]> ((-71.76134 18.66333, -71.76067 18.66267, -7…
    #>  4       4 478140 <tibble [6 × 2]> ((-71.7616 18.69374, -71.7616 18.68691, -71.…
    #>  5       5 478140 <tibble [6 × 2]> ((-71.73417 18.64352, -71.71386 18.63179, -7…
    #>  6       6 478140 <tibble [6 × 2]> ((-71.66801 18.63288, -71.69 18.64557, -71.7…
    #>  7       7 478140 <tibble [6 × 2]> ((-71.71386 18.68865, -71.71386 18.68691, -7…
    #>  8       8 478140 <tibble [6 × 2]> ((-71.65801 18.68222, -71.66613 18.68691, -7…
    #>  9       9 478140 <tibble [6 × 2]> ((-71.66613 18.63079, -71.66613 18.62665, -7…
    #> 10      10 478140 <tibble [6 × 2]> ((-71.63562 18.67697, -71.64227 18.67313, -7…
    #> 11      11 478140 <tibble [6 × 2]> ((-71.59453 18.66749, -71.59453 18.64815, -7…
    #> 12      12 478140 <tibble [6 × 2]> ((-71.52294 18.61972, -71.52294 18.63179, -7…
    #> 13      13 478140 <tibble [6 × 2]> ((-71.54943 18.67465, -71.5468 18.67313, -71…
    #> 14      14 478140 <tibble [6 × 2]> ((-71.49535 18.64342, -71.49907 18.64557, -7…
    #> 15      15 478140 <tibble [6 × 2]> ((-71.46559 18.63735, -71.4752 18.63179, -71…
    #> 16      16 478140 <tibble [6 × 2]> ((-71.43534 18.63634, -71.43264 18.63478, -7…
    #> 17      17 478140 <tibble [6 × 2]> ((-71.42747 18.62066, -71.42747 18.60424, -7…
    #> 18      18 478140 <tibble [6 × 2]> ((-71.35587 18.582, -71.35587 18.59046, -71.…
    #> 19      19 478140 <tibble [6 × 2]> ((-71.37974 18.61442, -71.37974 18.60424, -7…
    #> 20      20 478140 <tibble [6 × 2]> ((-71.34017 18.59953, -71.35587 18.59046, -7…

We obtained a new listed column in our sf object that is called like the requested indicator. For each asset in our portfolio, this column contains a tibble with 6 rows and two columns. Let’s have a closer look at one of these objects.

    sample_portfolio$population_count[10]
    #> [[1]]
    #> # A tibble: 6 × 2
    #>   popcount_sum year
    #>          <dbl> <chr>
    #> 1         302. 2010
    #> 2         326. 2011
    #> 3         299. 2012
    #> 4         282. 2013
    #> 5         345. 2014
    #> 6         398. 2015

For each asset, the result is a tibble in long format indicating the population sum per year (make sure to read the detailed indicator documentation via `?population_count`). Let’s quickly visualize the results for a single asset:

If you wish to conduct your statistical analysis in R, you can use `{tidyr}` functionality to unnest one or multiple columns. Especially for large portfolios, it is usually a good idea to keep the geometry information in a separated variable to keep the size of the data object relatively small.

    geometries <- select(sample_portfolio, assetid)
    sample_portfolio %>%
      st_drop_geometry() %>%
      tidyr::unnest(population_count) %>%
      filter(assetid == 3)
    #> # A tibble: 6 × 4
    #>   assetid WDPAID popcount_sum year
    #>     <int>  <dbl>        <dbl> <chr>
    #> 1       3 478140         153. 2010
    #> 2       3 478140         186. 2011
    #> 3       3 478140         187. 2012
    #> 4       3 478140         184. 2013
    #> 5       3 478140         165. 2014
    #> 6       3 478140         161. 2015

# Enabling parallel computing

{mapme.biodiversity} follows the parallelization paradigm of the {future} package. That means that you as a user are in the control if and how you would like to set up parallel processing. Currently, `{mapme.biodiversity}` supports parallel processing on the asset level of the `calc_indicators()` function only. We also currently assume that parallel processing is done on the cores of a single machine. In future developments, we would like to support distributed processing. If you are working on a distributed use-cases, please contact the developers, e.g. via the discussion board or mail.

To process e.g. 6 assets in parallel and report a progress bar you will have to set up the following in your code:

    library(future)
    library(progressr)

    plan(multisession, workers = 6) # set up parallel plan with 6 concurrent threads

    with_progress({
      portfolio <- calc_indicators(
        sample_portfolio,
        indicators = "population_count",
        stats_popcount = "sum",
        engine = "zonal"
      )
    })

    plan(sequential) # close child processes

Note, that the above code uses `future::multisession()` as the parallel backend. This backend will resolve the calculation in multiple background R sessions. You should use that backend if you are operating on Windows, using R Studio or otherwise are not sure about which backend to use. In case you are operating on a system that allows process forking and are not using R Studio, consider using `future::multicore()` for more efficient parallel processing.

# Exporting an portfolio object

You can use the `write_portfolio()` function to save a processed portfolio object to disk as a GeoPackage. This allows sharing your data with others who might not be using R, but any other geospatial software. Simply point towards a non-existing file on your local disk to write the portfolio. The function will create an individual table for all processed indicators. Via the `read_portfolio()` function, a portfolio which has been written to disk in such a way can be read back into R. However, users should note that the portfolio-wide arguments that were set during the portfolio initialization are not reconstructed (e.g. the temporal extent, `outdir` and `tmpdir`, etc.). Thus if you wish to continue to use `{mapme.biodiversity}` functionality on such a portfolio object, make sure to re-run `init_portfolio()` on it.

    tmp_output <- tempfile(fileext = ".gpkg")
    write_portfolio(
      x = sample_portfolio,
      dsn = tmp_output
    )
    #> Writing layer `metadata' to data source
    #>   `/tmp/RtmpxVvcsL/file992711042f60.gpkg' using driver `GPKG'
    #> Writing 20 features with 2 fields and geometry type Polygon.
    #> Updating layer `population_count' to data source `/tmp/RtmpxVvcsL/file992711042f60.gpkg' using driver `GPKG'
    #> Writing 120 features with 3 fields without geometries.
    (portfolio_from_disk <- read_portfolio(tmp_output))
    #> Simple feature collection with 20 features and 3 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 20 × 4
    #>    assetid WDPAID population_count                                          geom
    #>      <int>  <dbl> <list>                                           <POLYGON [°]>
    #>  1       1 478140 <tibble [6 × 2]> ((-71.78546 18.658, -71.78546 18.67313, -71.…
    #>  2       2 478140 <tibble [6 × 2]> ((-71.80292 18.68321, -71.78546 18.67313, -7…
    #>  3       3 478140 <tibble [6 × 2]> ((-71.76134 18.66333, -71.76067 18.66267, -7…
    #>  4       4 478140 <tibble [6 × 2]> ((-71.7616 18.69374, -71.7616 18.68691, -71.…
    #>  5       5 478140 <tibble [6 × 2]> ((-71.73417 18.64352, -71.71386 18.63179, -7…
    #>  6       6 478140 <tibble [6 × 2]> ((-71.66801 18.63288, -71.69 18.64557, -71.7…
    #>  7       7 478140 <tibble [6 × 2]> ((-71.71386 18.68865, -71.71386 18.68691, -7…
    #>  8       8 478140 <tibble [6 × 2]> ((-71.65801 18.68222, -71.66613 18.68691, -7…
    #>  9       9 478140 <tibble [6 × 2]> ((-71.66613 18.63079, -71.66613 18.62665, -7…
    #> 10      10 478140 <tibble [6 × 2]> ((-71.63562 18.67697, -71.64227 18.67313, -7…
    #> 11      11 478140 <tibble [6 × 2]> ((-71.59453 18.66749, -71.59453 18.64815, -7…
    #> 12      12 478140 <tibble [6 × 2]> ((-71.52294 18.61972, -71.52294 18.63179, -7…
    #> 13      13 478140 <tibble [6 × 2]> ((-71.54943 18.67465, -71.5468 18.67313, -71…
    #> 14      14 478140 <tibble [6 × 2]> ((-71.49535 18.64342, -71.49907 18.64557, -7…
    #> 15      15 478140 <tibble [6 × 2]> ((-71.46559 18.63735, -71.4752 18.63179, -71…
    #> 16      16 478140 <tibble [6 × 2]> ((-71.43534 18.63634, -71.43264 18.63478, -7…
    #> 17      17 478140 <tibble [6 × 2]> ((-71.42747 18.62066, -71.42747 18.60424, -7…
    #> 18      18 478140 <tibble [6 × 2]> ((-71.35587 18.582, -71.35587 18.59046, -71.…
    #> 19      19 478140 <tibble [6 × 2]> ((-71.37974 18.61442, -71.37974 18.60424, -7…
    #> 20      20 478140 <tibble [6 × 2]> ((-71.34017 18.59953, -71.35587 18.59046, -7…