Package 'mapme.biodiversity'

Description

This resource provides global maps of travel time to cities of 50,000 or more people in year 2000. Accessibility refers to the ease with which larger cities can be reached from a certain location. This dataset represents travel time to major cities globally as of the year 2000, encoded in minutes. The data is essential for historical analyses, such as understanding the impact of accessibility on land use and socio-economic outcomes during this period.

Usage

get_accessibility_2000()

Value

A function that returns an sf footprint object.

Source

https://forobs.jrc.ec.europa.eu/gam

References

European Commission, Joint Research Centre (JRC), Global Accessibility Maps (GAM), 2000.

Description

From ACLED's homepage: The Armed Conflict Location & Event Data Project (ACLED) is a disaggregated data collection, analysis, and crisis mapping project. ACLED collects information on the dates, actors, locations, fatalities, and types of all reported political violence and protest events around the world. The ACLED team conducts analysis to describe, explore, and test conflict scenarios, and makes both data and analysis open for free use by the public.

Usage

get_acled(
  years = 2000,
  key = Sys.getenv("ACLED_ACCESS_KEY"),
  email = Sys.getenv("ACLED_ACCESS_EMAIL"),
  accept_terms = FALSE
)

Arguments

Details

In order to access data from the ACLED API, you first must register an an account. Note, that the ACLED API used here provides a living database with single events being altered or removed altogether over time.

Value

A function that returns an sf footprint object.

Source

Armed Conflict Location & Event Data Project (ACLED).

References

Raleigh, C., Kishi, R. & Linke, A. Political instability patterns are obscured by conflict dataset scope conditions, sources, and coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4

Description

This function calculates the mean biodiversity intactness index for a region.

Usage

calc_biodiversity_intactness_index()

Format

A function that returns an indicator tibble with variable biodiversity_intactness_index and corresponding values (unitless) as value.

Details

The required resources for this indicator are:

• biodiversity_intactness_index_resource

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

lbii <- system.file("res", "biodiversity_intactness_index", "lbii.asc",
                    package = "mapme.biodiversity")

aoi <- read_sf(
  system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
              package = "mapme.biodiversity"
))
aoi <- get_resources(aoi, get_biodiversity_intactness_index(lbii))
aoi <- calc_indicators(aoi, calc_biodiversity_intactness_index())
aoi <- portfolio_long(aoi)

aoi

## End(Not run)

Description

The variable is the modeled average abundance of originally-present species, relative to their abundance in an intact ecosystem. Please refer to Newbold et al. (2016) for all details, and please cite it when using these data.

Usage

get_biodiversity_intactness_index(path = NULL)

Arguments

Details

To use this data in mapme workflows, you will have to manually download the global data set and point towards the file path on your local machine. Please find the available data under the source link given below.

Value

A function that returns an sf footprints object.

References

Tim Newbold; Lawrence Hudson; Andy Arnell; Sara Contu et al. (2016). Global map of the Biodiversity Intactness Index, from Newbold et al. (2016) Science [Data set]. Natural History Museum. doi:10.5519/0009936

Description

This function allows to efficiently retrieve the name of the biomes and compute the corresponding area from Terrestrial Ecoregions of the World (TEOW) - World Wildlife Fund (WWF) for polygons. For each polygon, the name and area of the biomes (in hectare) is returned. The required resources for this indicator are:

• teow

Usage

calc_biome()

Value

A function that returns an indicator tibble with variable biome type and corresponding area (in ha) as values.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_teow()) %>%
  calc_indicators(calc_biome()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

Calculates Monthly Burned Area based on the Terra and Aqua combined MCD64A1 Version 6.1. which s a monthly, global gridded 500 meter (m) product containing per-pixel burned-area information.

Usage

calc_burned_area(engine = "extract")

Arguments

Details

The required resources for this indicator are:

• mcd64a1

Value

A function that returns an indicator tibble with variable burned area and corresponding area (in ha) as values.

References

Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_mcd64a1(years = 2010)) %>%
  calc_indicators(calc_burned_area(engine = "extract")) %>%
  portfolio_long()

aoi

## End(Not run)

Description

The indicator calculates the population exposed to conflict events within a specified buffer distance around events in ACLED. Per default, the first available WorldPop layer is used to estimate exposed populations for years before the respective year, while the most recent layer is used for years after.

Usage

calc_exposed_population_acled(
  distance = 5000,
  filter_category = c("event_type", "sub_event_type", "disorder_type"),
  filter_types = NULL,
  years = c(1997:2024),
  precision_location = 1,
  precision_time = 1
)

Arguments

Details

The indicator is inspired by the Conflict Exposure tool from ACLED (see citation below), but differs in the regard that we simply flatten our buffered event layer instead of applying voronoi tessellation.

The required resources for this indicator are:

• acled

• worldpop

Events in ACLED are classified according to the schema described extensively in their codebook. You may filter for certain types of events. The categories for which a filter can be applied are either "event_type", "event_sub_type", or "disorder_type". These are translated into the following categories:

• event_type:

  • battles

  • protests

  • riots

  • explosions/remote_violence

  • violence_against_civilians

  • strategic_developments

• event_sub_type:

  • government_regains_territory

  • non-state_actor_overtakes_territory

  • armed_clash

  • excessive_force_against_protesters

  • protest_with_intervention

  • peaceful_protest

  • violent_demonstration

  • mob_violence

  • chemical_weapon

  • air/drone_strike

  • suicide_bomb

  • shelling/artillery/missile_attack

  • remote_explosive/landmine/ied

  • grenade

  • sexual_violence

  • attack

  • abduction/forced_disappearance

  • agreement

  • arrests

  • change_to_group/activity

  • disrupted_weapons_use

  • headquarters_or_base_established

  • looting/property_destruction

  • non-violent_transfer_of_territory

  • other

• disorder_type:

  • political_violence

  • political_violence;_demonstrations

  • demonstrations

  • political_violence

  • strategic_developments

You may supply buffer distances for each of the event categories. Custom buffers will then be drawn per category. Supply a single value if you do not wish do differentiate between categories. Otherwise, supply a vector of distances equal to the length of included categories.

You may apply quality filters based on the precision of the geolocation of events and the temporal precision. By default, these are set to only include events with the highest precision scores.

For geo-precision there are levels 1 to 3 with decreasing accuracy:

• value 1: the source reporting indicates a particular town, and coordinates are available for that town

• value 2: the source material indicates that activity took place in a small part of a region, and mentions a general area or if an activity occurs near a town or a city, the event is coded to a town with geo-referenced coordinates to represent that area

• value 3: a larger region is mentioned, the closest natural location noted in reporting (like “border area,” “forest,” or “sea,” among others) – or a provincial capital is used if no other information at all is available

For temporal precision there are levels 1 to 3 with decreasing precision:

• value 1: the source material includes an actual date of an event

• value 2: the source material indicates that an event happened sometime during the week or within a similar period of time

• value 3: the source material only indicates that an event took place sometime during a month (i.e. in the past two or three weeks, or in January), without reference to the particular date, the month mid-point is chosen

Value

A function that returns an indicator tibble with conflict exposure as variable and precentage of the population as its value.

References

Raleigh, C; C Dowd; A Tatem; A Linke; N Tejedor-Garavito; M Bondarenko and K Kishi. 2023. Assessing and Mapping Global and Local Conflict Exposure. Working Paper.

Examples

## Not run: 
if (FALSE) {
  library(sf)
  library(mapme.biodiversity)

  outdir <- file.path(tempdir(), "mapme-data")
  dir.create(outdir, showWarnings = FALSE)

  mapme_options(
    outdir = outdir,
    verbose = FALSE,
    chunk_size = 1e8
  )

  aoi <- system.file("extdata", "burundi.gpkg",
    package = "mapme.biodiversity"
  ) %>%
    read_sf() %>%
    get_resources(
      get_acled(year = 2000),
      get_worldpop(years = 2000)
    ) %>%
    calc_indicators(
      conflict_exposure_acled(
        distance = 5000,
        years = 2000,
        precision_location = 1,
        precision_time = 1
      )
    ) %>%
    portfolio_long()

  aoi
}

## End(Not run)

Description

These functions allow to calculated statistics based on the harmonized carbon layers for 2010 and 2018 by Noon et al. (2022).

Usage

calc_irr_carbon(
  type = c("total", "soil", "biomass", "all"),
  engine = "extract",
  stats = "mean"
)

calc_man_carbon(
  type = c("total", "soil", "biomass", "all"),
  engine = "extract",
  stats = "mean"
)

calc_vul_carbon(
  type = c("total", "soil", "biomass", "all"),
  engine = "extract",
  stats = "mean"
)

Arguments

Details

The required resources for these indicators are:

• carbon_resources

Irrecoverable carbon is the amount of carbon that, if lost today, could not be recovered until 2050. It can be calculated for above- and below-ground carbon, the total amount of carbon, or for all layers.

Manageable carbon is the amount of carbon that, in principle, is manageable by human activities, e.g. its release to the atmosphere can be prevented. It can be calculated for above- and below-ground carbon, the total amount of carbon, or for all layers.

Vulnerable carbon is the amount of carbon that would be released in a typical land conversion activity. It can be calculated for above- and below-ground carbon, the total amount of carbon, or for all layers.

Value

A function that returns an indicator tibble with ⁠(type)_carbon_(stat)⁠ as variable and the respective statistic (in Mg) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(
    get_man_carbon(),
    get_vul_carbon(),
    get_irr_carbon()
  ) %>%
  calc_indicators(
    calc_man_carbon(stats = "sum"),
    calc_vul_carbon(stats = "sum"),
    calc_irr_carbon(stats = "sum")
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

These resources are from the publication by Noon et al. (2022) "Mapping the irrecoverable carbon in Earth’s ecosystems". This publication differentiates between 3 different kinds of carbon with varying degrees of manageability by humans. All three layers are available for above and below ground carbon, as well as a layer combining the two.

Usage

get_irr_carbon()

get_vul_carbon()

get_man_carbon()

Details

It may be required to increase the timeout option to successfully download theses layers from their source location via e.g. options(timeout = 600).

Irrecoverable carbon is defined as the amount of carbon, that, if lost today, cannot be recovered until mid 21st century (so within 30 years, considering the publication date).

Vulnerable carbon is defined as the amount of carbon that would be lost in a hypothetical but typical conversion event (without including information of the probability of such an event to be actually occurring).

Manageable carbon is defined as all land areas, expect cyrosols, because carbon loss is driven by direct land-use conversion which could be halted or because climate change impacts affecting the area can potentially be directly mitigated through adaptive management.

Value

A function that returns an sf footprint object.

Source

https://zenodo.org/records/4091029

References

Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth’s ecosystems. Nat Sustain 5, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6

Description

Use this function to check if a specifed vector of years intersects with the yearly availablity of a resource.

Usage

check_available_years(target_years, available_years, indicator)

Arguments

Description

Use this function if your resource/indicator function requires the namespace of a certain package to be available. An informative error/warning message is printed if that is not the case.

Usage

check_namespace(pkg, error = TRUE)

Arguments

Value

TRUE, invisible, if the namespace is available. An error message if error = TRUE, FALSE and a warning otherwise.

Description

The CHELSA data (Karger et al. 2017) consists of downscaled model output temperature and precipitation estimates at a horizontal resolution of 30 arc sec. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The spatial resolution is about 1-arc second (~1km at the equator). This resource makes V2 available.

Usage

get_chelsa(years = 1979:2019)

Arguments

Value

A function that returns an sf footprint object.

Source

https://envicloud.wsl.ch/#/?prefix=chelsa/chelsa_V2/GLOBAL/

References

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2021) Climatologies at high resolution for the earth’s land surface areas. EnviDat. doi:10.16904/envidat.228.v2.1

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. doi:10.1038/sdata.2017.122

Description

This resource is published by Funk et al. (2015) and represents a quasi-global (50°S-50°S) rainfall estimation at a monthly resolution starting with the year 1981 to the near-present. It has a spatial resolution of 0.05°. The data can be used to retrieve information on the amount of rainfall. Due to the availability of +30 years, anomaly detection and long-term average analysis is also possible. The routine will download the complete archive in order to support long-term average and anomaly calculations with respect to the 1981 - 2010 climate normal period. Thus no additional arguments need to be specified.

Usage

get_chirps(years = 1981:2020)

Arguments

Value

A function that returns an sf footprint object.

Source

https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/cogs/

References

Funk, C., Peterson, P., Landsfeld, M. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2, 150066 (2015). doi:10.1038/sdata.2015.66

Description

This function extracts areal statistics for the drivers of deforestation based on the data source produced by Fritz et al (2022).

Usage

calc_deforestation_drivers()

Details

The required resource for this indicator is:

• fritz_et_al

Value

A function that returns an indicator tibble with deforestation drivers as variable and corresponding area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_fritz_et_al(resolution = 100)) %>%
  calc_indicators(calc_deforestation_drivers()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to efficiently calculate the relative wetness in the shallow groundwater section with regard to the the 1948-2012 reference period. The values represent the wetness percentile a given area achieves at a given point in time in regard to the reference period. For each polygon, the desired statistic/s (mean, median or sd) is/are returned.

Usage

calc_drought_indicator(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• nasa_grace

Value

A function that returns an indicator tibble with specified drought indicator statistics as variable and corresponding values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_nasa_grace(years = 2022)) %>%
  calc_indicators(
    calc_drought_indicator(
      engine = "extract",
      stats = c("mean", "median")
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to efficiently retrieve the name of the ecoregions and compute the corresponding area from Terrestrial Ecoregions of the World (TEOW) - World Wildlife Fund (WWF) for polygons. For each polygon, the name and area of the ecoregions (in hectare) is returned. The required resources for this indicator are:

• teow

Usage

calc_ecoregion()

Value

A function that returns an indicator tibble with ecoregion type as variable and corresponding area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_teow()) %>%
  calc_indicators(calc_ecoregion()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to calculate elevation statistics for polygons. For each polygon, the desired statistic(s) are returned.

Usage

calc_elevation(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• nasa_srtm

Value

A function that returns an indicator tibble with specified elevation statistics as variable and corresponding values (in meters) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_nasa_srtm()) %>%
  calc_indicators(
    calc_elevation(engine = "extract", stats = c("mean", "median", "sd", "var"))
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

check_engine() checks if an extraction engine for zonal vector-raster operations is supported by the backend.

check_stats checks if one or multiple statistics are supported for zonal vector-raster extraction by the backend.

select_engine extracts zonal vector-raster statistics for supported engine and for one or more statistics. Columns are named according to the argument name plus the respective stat. Both portfolio and asset modes are supported.

Usage

check_engine(queried_engine)

check_stats(queried_stats)

select_engine(x, raster, stats, engine, name = NULL, mode = "asset")

Arguments

Value

check_engine() returns the character of the queried engine, if supported. Throws an error otherwise.

check_stats returns a character vector of supported statistics. Throws an error if any of the queried statistics is not supported.

select_engine returns a tibble.

Description

This 100 meter spatial resolution land cover resource is published by Buchhorn et al. (2020) "Copernicus Global Land Cover Layers—Collection 2". The resource represents the actual surface cover of ground available annually for the period 2015 to 2019. The cell values range from 0 to 200, representing total of 23 discrete classifications from ESA.

Usage

get_esalandcover(years = 2015:2019)

Arguments

Value

A function that returns an sf footprint object.

Source

https://lcviewer.vito.be/download

References

© European Union, Copernicus Land Monitoring Service (year), European Environment Agency (EEA)", f.ex. in 2018: “© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)

Description

The indicator calculates the population exposed to conflict events within a specified buffer distance around violent events in UCDP GED. Per default, the first available WorldPop layer is used to estimate exposed populations for years before the respective year, while the most recent layer is used for years after.

Usage

calc_exposed_population_ucdp(
  distance = 5000,
  violence_types = 1:3,
  years = c(1989:2023),
  precision_location = 1,
  precision_time = 1
)

Arguments

Details

The indicator is inspired by the Conflict Exposure tool from ACLED (see citation below), but differs in the regard that we simply flatten our buffered event layer instead of applying voronoi tessellation.

The required resources for this indicator are:

• ucdp_ged

• worldpop

You may filter for certain types of violence. The coded types according to the UCDP codebook are: value 1: state-based conflict value 2: non-state conflict value 3: one-sided conflict

You may apply quality filters based on the precision of the geolocation of events and the temporal precision. By default, these are set to only include events with the highest precision scores.

For geo-precision there are levels 1 to 7 with decreasing accuracy:

• value 1: the location information corresponds exactly to the geographical coordinates available

• value 2: the location information refers to a limited area around a specified location

• value 3: the source refers to or can be specified to a larger location at the level of second order administrative divisions (ADM2), such as district or municipality, the GED uses centroid point coordinates for that ADM2.

• value 4: the location information refers to a first order administrative division, such as a province (ADM1), the GED uses the coordinates for the centroid point of ADM1

• value 5: is used in different cases if the source refers to parts of a country which are larger than ADM1, but smaller than the entire country; if two locations are mentioned a representiative point in between is selected; if the location mentioned is an non-independend island; if the location is not very specifically mentioned or in relation to another location

• value 6: the location mentioned refers to an entire country and its centroid is used

• value 7: If the event takes place over water or in international airspace, the geographical coordinates in the dataset either represent the centroid point of a certain water area or estimated coordinates

For temporal precision there are levels 1 to 5 with decreasing precision:

• value 1: if the exact date of an event is known

• value 2: if start and enddates for events are of unspecified character, spanning more than one calendar day though no longer than six days

• value 3: if when start and end dates for events are specified to a certain week, but specific dates are not provided

• value 4: if start and end dates for events are specified to a certain month

• value 5: if start and enddates for events are specified to a certain year, but specific dates are not provided

Value

A function that returns an indicator tibble with conflict exposure as variable and precentage of the population as its value.

References

Raleigh, C; C Dowd; A Tatem; A Linke; N Tejedor-Garavito; M Bondarenko and K Kishi. 2023. Assessing and Mapping Global and Local Conflict Exposure. Working Paper.

Examples

## Not run: 
if (FALSE) {
  library(sf)
  library(mapme.biodiversity)

  outdir <- file.path(tempdir(), "mapme-data")
  dir.create(outdir, showWarnings = FALSE)

  mapme_options(
    outdir = outdir,
    verbose = FALSE,
    chunk_size = 1e8
  )

  aoi <- system.file("extdata", "burundi.gpkg",
    package = "mapme.biodiversity"
  ) %>%
    read_sf() %>%
    get_resources(
      get_ucdp_ged(version = "22.1"),
      get_worldpop(years = 2000)
    ) %>%
    calc_indicators(
      conflict_exposure(
        distance = 5000,
        violence_types = 1:3,
        years = 2000,
        precision_location = 1,
        precision_time = 1
      )
    ) %>%
    portfolio_long()

  aoi
}

## End(Not run)

Description

The indicator aggregated the number of fatalities within a given asset on a monthly cadence stratified either by event type, sub-event type or disorder type. To learn about the different categorisation ACLED uses to encode events please consult ACLED's codebook.

Usage

calc_fatalities_acled(
  years = 2000,
  stratum = c("event_type", "sub_event_type", "disorder_type"),
  precision_location = 1,
  precision_time = 1
)

Arguments

Details

The required resources for this indicator are:

• acled

You may apply quality filters based on the precision of the geolocation of events and the temporal precision. By default, these are set to only include events with the highest precision scores.

For geo-precision there are levels 1 to 3 with decreasing accuracy:

• value 1: the source reporting indicates a particular town, and coordinates are available for that town

• value 2: the source material indicates that activity took place in a small part of a region, and mentions a general area or if an activity occurs near a town or a city, the event is coded to a town with geo-referenced coordinates to represent that area

• value 3: a larger region is mentioned, the closest natural location noted in reporting (like “border area,” “forest,” or “sea,” among others) – or a provincial capital is used if no other information at all is available

For temporal precision there are levels 1 to 3 with decreasing precision:

• value 1: the source material includes an actual date of an event

• value 2: the source material indicates that an event happened sometime during the week or within a similar period of time

• value 3: the source material only indicates that an event took place sometime during a month (i.e. in the past two or three weeks, or in January), without reference to the particular date, the month mid-point is chosen

Value

A function that returns an indicator tibble with the type of violence as variable and counts of civilian fatalities as value.

References

Raleigh, C., Kishi, R. & Linke, A. Political instability patterns are obscured by conflict dataset scope conditions, sources, and coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE,
  chunk_size = 1e8
)

aoi <- system.file("extdata", "burundi.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_acled(years = 2020)) %>%
  calc_indicators(
    calc_fatalities_acled(
      years = 2020,
      precision_location = 1,
      precision_time = 1
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

The indicator aggregated the number of fatalities within a given asset on a monthly cadence stratified by the type of conflict. The different types of conflicts encoded in the UCDP GED database are:

• state-based conflict

• non-state conflict

• one-sided violence

Usage

calc_fatalities_ucdp(
  years = 1989:2023,
  precision_location = 1,
  precision_time = 1
)

Arguments

Details

The required resources for this indicator are:

• ucdp_ged

You may apply quality filters based on the precision of the geolocation of events and the temporal precision. By default, these are set to only include events with the highest precision scores.

For geo-precision there are levels 1 to 7 with decreasing accuracy:

• value 1: the location information corresponds exactly to the geographical coordinates available

• value 2: the location information refers to a limited area around a specified location

• value 3: the source refers to or can be specified to a larger location at the level of second order administrative divisions (ADM2), such as district or municipality, the GED uses centroid point coordinates for that ADM2.

• value 4: the location information refers to a first order administrative division, such as a province (ADM1), the GED uses the coordinates for the centroid point of ADM1

• value 5: is used in different cases if the source refers to parts of a country which are larger than ADM1, but smaller than the entire country; if two locations are mentioned a representiative point in between is selected; if the location mentioned is an non-independend island; if the location is not very specifically mentioned or in relation to another location

• value 6: the location mentioned refers to an entire country and its centroid is used

• value 7: If the event takes place over water or in international airspace, the geographical coordinates in the dataset either represent the centroid point of a certain water area or estimated coordinates

For temporal precision there are levels 1 to 5 with decreasing precision:

• value 1: if the exact date of an event is known

• value 2: if start and enddates for events are of unspecified character, spanning more than one calendar day though no longer than six days

• value 3: if when start and end dates for events are specified to a certain week, but specific dates are not provided

• value 4: if start and end dates for events are specified to a certain month

• value 5: if start and enddates for events are specified to a certain year, but specific dates are not provided

Value

A function that returns an indicator tibble with the type of violence as variable and counts of civilian fatalities as value.

References

Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE,
  chunk_size = 1e8
)

aoi <- system.file("extdata", "burundi.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_ucdp_ged(version = "22.1")) %>%
  calc_indicators(
    calc_fatalities(
      years = 1991:1992,
      precision_location = 1,
      precision_time = 1
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This resource is produced by a neirest-neighbour matching of a crowd-sourced campaign to map dominant driver of forest loss based on visual interpretation of VHR images matched with Global Forest Loss data by Hansen (2013) version 1.7 The forest loss layer was re sampled to a resolution of 100 and 1.000 meters. Dominant drivers were determined for the period 2008 to 2009.

Usage

get_fritz_et_al(resolution = 100)

Arguments

Details

It indicates 9 different classes:

• commercial agriculture

• commercial oil palm plantations

• managed forests

• mining

• natural disturbances

• pasture

• roads

• wildfire

• other subsistence agriculture

• shifting cultivation

Value

A function that returns an sf footprint object.

Source

https://zenodo.org/record/7997885

References

Steffen, F., Carlos, J.C.L., See. L., Schepaschenko D., Hofhansl F., Jung M., Dürauer M., Georgieva I., Danylo O., Lesiv M., McCallum I. (2022) A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas. F.Cos.Sc.(3) doi:10.3389/fcosc.2022.830248

Description

This resource is part of the publication by Harris et al. (2021) "Global maps of twenty-first century forest carbon fluxes.". It represents "the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modelled year (megagrams CO2 emissions/ha, between 2001 and 2023). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) and greenhouse gases (CO2, CH4, N2O)." The area unit that is downloaded here corresponds to the "megagrams of CO2 emissions/pixel" layer, in order to support the calculation of area-wise emissions.

Usage

get_gfw_emissions()

Details

There are no arguments users need to specify. However, users should note that the spatial extent for this dataset does not totally cover the same extent as the treecover2000 and lossyear resources by Hansen et al. (2013). A missing value (NA) will be inserted for greenhouse gas emissions for areas where no data is available.

Value

A function that returns an sf footprint object.

Source

https://data.globalforestwatch.org/datasets/gfw::forest-greenhouse-gas-emissions/about

References

Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021). https://doi.org/10.1038/s41558-020-00976-6

Description

This resource is part of the publication by Hansen et al. (2013) "High-Resolution Global Maps of 21st-Century Forest Cover Change". It represents "Forest loss during the period 2000–2021, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1–20, representing loss detected primarily in the year 2001–2021, respectively." Due to changes in the satellites products used in the compilation of the tree loss product, results before the year 2011 and afterwards are not directly comparable until reprocessing has finished. Users should be aware of this limitation, especially when the timeframe of the analysis spans over the two periods delimited by the year 2011.

Usage

get_gfw_lossyear(version = "GFC-2023-v1.11")

Arguments

Value

A function that returns an sf footprint object.

Source

https://data.globalforestwatch.org/documents/tree-cover-loss/explore

References

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.

Description

This resource is part of the publication by Hansen et al. (2013) represents "tree cover in the year 2000, defined as canopy closure for all vegetation taller than 5m in height. Encoded as a percentage per output grid cell, in the range 0–100." Due to changes in the satellites products used in the compilation of the treecover product, results before the year 2011 and afterwards are not directly comparable until reprocessing has finished. Users should be aware of this limitation, especially when the timeframe of the analysis spans over the two periods delimited by the year 2011.

Usage

get_gfw_treecover(version = "GFC-2023-v1.11")

Arguments

Value

A function that returns an sf footprint object.

Source

https://data.globalforestwatch.org/documents/tree-cover-2000/explore

References

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.

Description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre in the framework of the Copernicus Programme. It maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades and provides statistics on their extent and change. It is provisioned as a global tiled raster resource available for all land areas. The reported data represent aggregated observations between 1984 - 2021.

Usage

get_global_surface_water_change(version = "v1_4_2021")

Arguments

Details

The change in water occurrence intensity between the two periods is derived from homologous pairs of months (i.e. same months containing valid observations in both periods). The difference in the occurrence of surface water was calculated for each homologous pair of months. The average of all of these differences constitutes the Surface Water Occurrence change intensity. The raster files have integer cell values between [0, 200] where 0 represents surface water loss and 200 represents surface water gain.

Value

A function that returns an sf footprint object.

Source

https://global-surface-water.appspot.com/

References

Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584

Description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre in the framework of the Copernicus Programme. It maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades and provides statistics on their extent and change. It is provisioned as a global tiled raster resource available for all land areas. The reported data represent aggregated observations between 1984 - 2021.

Usage

get_global_surface_water_occurrence(version = "v1_4_2021")

Arguments

Details

GSW occurrence raw data comes in raster files with integer cell values between [0, 100]. This value gives the percentage of the time that a given pixel was classified as water during the entire observation period. So a 0 denotes a pixel that was never classified as water, 100 denotes a pixel with permanent water.

Value

A character of file paths.

Source

https://global-surface-water.appspot.com/

References

Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584

Description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre in the framework of the Copernicus Programme. It maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades and provides statistics on their extent and change. It is provisioned as a global tiled raster resource available for all land areas. The reported data represent aggregated observations between 1984 - 2021.

Usage

get_global_surface_water_recurrence(version = "v1_4_2021")

Arguments

Details

Water Recurrence is a measurement of the degree of variability in the presence of water from year to year. It describes the frequency with which water returned to a particular location from one year to another, and is expressed as a percentage. The raster files have integer cell values between [0, 100], where 100 represents that water reoccurs predictably every year, whereas lower values indicate that water only occurs episodically.

Value

A character of file paths.

Source

https://global-surface-water.appspot.com/

References

Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584

Description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre in the framework of the Copernicus Programme. It maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades and provides statistics on their extent and change. It is provisioned as a global tiled raster resource available for all land areas. The reported data represent aggregated observations between 1984 - 2021.

Usage

get_global_surface_water_seasonality(version = "v1_4_2021")

Arguments

Details

GSW seasonality describes the intra-annual distribution of surface water for each pixel. The raster files have integer cell values between [0, 12], indicating how many months per year the pixel was classified as water.

Value

A character of file paths.

Source

https://global-surface-water.appspot.com/

References

Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584

Description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre in the framework of the Copernicus Programme. It maps the location and temporal distribution of water surfaces at the global scale over the past 3.8 decades and provides statistics on their extent and change. It is provisioned as a global tiled raster resource available for all land areas. The reported data represent aggregated observations between 1984 - 2021.

Usage

get_global_surface_water_transitions(version = "v1_4_2021")

Arguments

Details

GSW transition data contains information about the type of surface water change for each pixel. The raster files have integer cell values between [0, 10] that code for different transition classes:

Value

A character of file paths.

Source

https://global-surface-water.appspot.com/

References

Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584

Description

This resource is part of the publication by Bunting et al. (2018) "The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent". The polygons represent the mangrove, which is tropical coastal vegetation and considered the most significant part of the marine ecosystem. This resource is available for the selected years in the period 1996- 2020 from Global Mangrove Watch (GMW), providing geospatial information about global mangrove extent.

Usage

get_gmw(years = c(1996, 2007:2010, 2015:2020))

Arguments

Value

A function that returns an sf footprint object.

Source

https://data.unep-wcmc.org/datasets/45

References

Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi:10.3390/rs10101669.

Description

The change in water occurrence intensity between the two periods is derived from homologous pairs of months (i.e. same months containing valid observations in both periods). The difference in the occurrence of surface water was calculated for each homologous pair of months. The average of all of these differences constitutes the Surface Water Occurrence change intensity. The raster files have integer cell values between ⁠[0, 200]⁠ where 0 represents surface water loss and 200 represents surface water gain.

Usage

calc_gsw_change(engine = "extract", stats = "mean")

Arguments

Details

The pixel values are aggregated using method provided via the stats parameter using the specified engine.

The required resources for this indicator are:

• global_surface_water_change

Value

A function that returns an indicator tibble with change intensity as variable and corresponding (unitless) values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_global_surface_water_change()) %>%
  calc_indicators(
    calc_gsw_change(engine = "extract", stats = "mean")
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

GSW occurrence raw data comes in raster files with integer cell values between ⁠[0, 100]⁠. This value gives the percentage of the time that a given pixel was classified as water during the entire observation period. So a 0 denotes a pixel that was never classified as water, 100 denotes a pixel with permanent water.

Usage

calc_gsw_occurrence(engine = "extract", min_occurrence = NULL)

Arguments

Details

The raw data values are aggregated based on a provided threshold parameter min_occurrence, the function returns the area covered by values greater or equal than this threshold.

The required resources for this indicator are:

• global_surface_water_occurrence

Value

A function that returns an indicator tibble with occurrence as variable and the corresponding area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_global_surface_water_occurrence()) %>%
  calc_indicators(
    calc_gsw_occurrence(engine = "extract", min_occurrence = 10)
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

Water Recurrence is a measurement of the degree of variability in the presence of water from year to year. It describes the frequency with which water returned to a particular location from one year to another, and is expressed as a percentage. The raster files have integer cell values between ⁠[0, 100]⁠, where 100 represents that water reoccurs predictably every year, whereas lower values indicate that water only occurs episodically.

Usage

calc_gsw_recurrence(engine = "extract", min_recurrence = NULL)

Arguments

Details

The raw data values are aggregated based on a provided threshold parameter min_recurrence, the function returns the area covered by values greater or equal than this threshold.

The required resources for this indicator are:

• global_surface_water_recurrence

Value

A function that returns an indicator tibble with recurrence as variable and the corresponding area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_global_surface_water_recurrence()) %>%
  calc_indicators(
    calc_gsw_recurrence(engine = "extract", min_recurrence = 10)
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

GSW seasonality describes the intra-annual distribution of surface water for each pixel. The raster files have integer cell values between [0, 12], indicating how many months per year the pixel was classified as water.

Usage

calc_gsw_seasonality()

Details

The pixel values are aggregated using method provided via the stats parameter.

The required resources for this indicator are:

• global_surface_water_seasonality

Value

A function that returns an indicator tibble with seasonality categories as variables and corresponding areas (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_global_surface_water_seasonality()) %>%
  calc_indicators(calc_gsw_seasonality()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function calculates the total area of the global surface water time series data, separated by the following classes:

Usage

calc_gsw_time_series()

Format

A function returning a tibble with time series of global surface water data classes.

Details

• No Observation: It was not possible to determine whether a pixel was water (this may be the case for frozen areas or during the polar night in extreme latitudes).

• Permanent Water: Water was detected in twelve months per year or in a combination of permanent and no observation.

• Seasonal Water: Water and no water was detected.

• No Water: No Water was detected.

The required resources for this indicator are:

• gsw_time_series_resource

Examples

## Not run: 
library(mapme.biodiversity)
library(sf)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- read_sf(
  system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
              package = "mapme.biodiversity"
))
aoi <- get_resources(aoi, get_gsw_time_series (years = 2000:2001))
aoi <- calc_indicators(aoi, calc_gsw_time_series())
aoi <- portfolio_long(aoi)

aoi

## End(Not run)

Description

This function constructs the necessary data URLs for a given data set, version and polygon and downloads them for further processing with the mapme.biodiversity package.

Usage

get_gsw_time_series(years, version = "LATEST")

Arguments

Details

The available surface water classes for a given pixel are the following:

• No Observation: It was not possible to determine whether a pixel was water (this may be the case for frozen areas or during the polar night in extreme latitudes).

• Permanent Water: Water was detected in twelve months per year or in a combination of permanent and no observation.

• Seasonal Water: Water and no water was detected.

• No Water: No Water was detected.

Value

A function that returns a character vector of file paths.

Source

Raw Data: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GSWE/YearlyClassification/LATEST/tiles/

References

• Global Surface Water Explorer: https://global-surface-water.appspot.com/

• Data Users Guide: https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2021.pdf

• Research Article: https://www.nature.com/articles/nature20584

Description

GSW transition data contains information about the type of surface water change for each pixel. The raster files have integer cell values between [0, 10] that code for different transition classes:

Usage

calc_gsw_transitions()

Details

To aggregate, we sum up the area of each transition class for a given region.

The required resources for this indicator are:

• global_surface_water_transitions

Value

A function that returns an indicator tibble with transition classes as variable and corresponding areas (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_global_surface_water_transitions()) %>%
  calc_indicators(calc_gsw_transitions()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

Human footprint data measures the pressure imposed on the natural environment by different dimensions of human actions. The theoretical maximum value, representing the highest level of human pressure, is 50. This routine allows to extract zonal statistics of the human footprint data.

Usage

calc_humanfootprint(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• humanfootprint_resource

Value

A function that returns an indicator tibble the humanfootprint as variable and the associated value (unitless) per year.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_humanfootprint(years = 2010)) %>%
  calc_indicators(calc_humanfootprint(stats = "median")) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This resource is part of the publication by Mu et al. (2022) "A global record of annual terrestrial Human Footprint dataset from 2000 to 2018". It is calculated based on 8 variables representing human pressures on natural ecosystems collected at a yearly cadence between 2000 and 2020 sampled at a 1km spatial resolution. The variables are used are the expansion of built environments (expressed as percentage of built-up areas within a grid cell), population density (aggregated at the gridd cell), nighttime lights, crop and pasture lands, roads and railways (excluding trails and minor roads), and navigable waterways (compares waterways with nighttime lights dataset). The human footprint was then calculated based on a weighting scheme proposed by Venter et al. (2016), assigning each pixel a value between 0 and 50, with 50 representing the theoretical value of the highest human pressure.

Usage

get_humanfootprint(years = 2000:2020)

Arguments

Value

A function that returns an sf footprint object.

Note

It may be required to increase the timeout option to successfully download theses layers from their source location via e.g. options(timeout = 600). In case an 403 error occurs, you can create an account with Figshare and create an personal access token. If set as FIGSHARE_PAT environment variable, it will be used to authenticate.

Source

https://figshare.com/articles/figure/An_annual_global_terrestrial_Human_Footprint_dataset_from_2000_to_2018/16571064

References

Mu, H., Li, X., Wen, Y. et al. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci Data 9, 176 (2022). doi:10.1038/s41597-022-01284-8

Description

register_indicator() is used to register a new indicator function with base information to the package's internal environment used to inform users about available indicators. Note, registering a custom indicator will only have effect for the current R session.

available_indicators() returns a tibble of registered indicators with basic information such as the required resources.

Usage

register_indicator(name = NULL, description = NULL, resources = NULL)

available_indicators(indicators = NULL)

Arguments

Value

register_indicator() is called for the side-effect of registering an indicator

available_resources() returns a tibble listing available indicators.

Examples

## Not run: 
register_indicator(
  name = "treecover_area",
  description = "Area of forest cover by year",
  resources = c(
    "gfw_treecover",
    "gfw_lossyear"
  )
)

## End(Not run)
available_indicators()

Description

This indicator calculates the areal distribution of different biome classes within an asset based on the IBPES biomes dataset.

Usage

calc_ipbes_biomes()

Details

The required resources for this indicator are:

• ipbes_biomes

Value

A function that returns an indicator tibble with the biome class as variable and the respective area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_ipbes_biomes()) %>%
  calc_indicators(calc_ipbes_biomes()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This resource is part of the Global Assessment Report on Biodiversity and Ecosystem Services and represents a division of the Earth's surface into several subcategories. The classification differentiates between biomes and anthromes. Biomes are differentiated between terrestrial and aquatic biomes.

Usage

get_ipbes_biomes()

Details

Terrestrial biomes include:

• Tropical and subtropical dry and humid forests

• Temperate and boreal forests and woodlands

• Mediterranean forests, woodlands and scrub

• Tundra and High Mountain habitats

• Tropical and subtropical savannas and grasslands

• Temperate Grasslands

• Deserts and xeric shrublands

• Wetlands – peatlands, mires, bogs

Aquatic biomes include:

• Cryosphere

• Aquaculture areas

• Inland surface waters and water bodies/freshwater

• Shelf ecosystems (neritic and intertidal/littoral zone)

• Open ocean pelagic systems (euphotic zone)

Value

A function that returns an sf footprint object.

Source

https://zenodo.org/records/3975694

References

IPBES (2019): Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio, H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579

Description

This resource is part of the spatial data set Red List of Threatened Species released by IUCN. It is free to use under a non-commercial licence. For commercial uses, a request has to be sent to Integrated Biodiversity Assessment Tool (IBAT).

Usage

get_iucn(paths = NULL)

Arguments

Details

To use this data in mapme workflows, you will have to manually download the global data set and point towards the file path on your local machine. Please find the available data under the source link given below.

Value

A function that returns an sf footprint object.

Source

https://www.iucnredlist.org/resources/other-spatial-downloads

References

IUCN (2024). The IUCN Red List of Threatened Species. https://www.iucnredlist.org doi:10.1038/s41597-022-01284-8

Description

This function calculates the total area of key biodiversity areas for a given input polygon.

Usage

calc_key_biodiversity_area()

Format

A function returning an indicator tibble with key_biodiversity_area as variable and the total overlap area (in ha) as value.

Details

The required resources for this indicator are:

• key_biodiversity_areas_resource

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- read_sf(
  system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
              package = "mapme.biodiversity"
))
kbas <- system.file("res", "key_biodiversity_areas", "kbas.gpkg",
                    package = "mapme.biodiversity")
aoi <- get_resources(aoi, get_key_biodiversity_areas(kbas))
aoi <- calc_indicators(aoi, calc_key_biodiversity_area())
aoi <- portfolio_long(aoi)

aoi

## End(Not run)

Description

This resource contains outlines of key biodiversity areas, which are areas representing sites with specific importance for nature conservation.

Usage

get_key_biodiversity_areas(path = NULL)

Arguments

Details

To use this data in mapme workflows, you will have to manually download the global data set and point towards its file path on your local machine. Please find the available data under the source link given below.

Value

A function that returns an sf footprints object.

Source

https://www.keybiodiversityareas.org/kba-data

References

BirdLife International (2024). The World Database of Key Biodiversity Areas. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Re:wild, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. Available at www.keybiodiversityareas.org.

Description

The land cover data shows us how much of the region is covered by forests, rivers, wetlands, barren land, or urban infrastructure thus allowing the observation of land cover dynamics over a period of time. This function allows to efficiently calculate area of different landcover classes for polygons. For each polygon, the area of the classes in hectare(ha) is returned.

Usage

calc_landcover()

Details

The required resources for this indicator are:

• esalandcover

Value

A function that returns an indicator tibble with landcover classes as variables and corresponding areas (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_esalandcover(years = 2016:2017)) %>%
  calc_indicators(calc_landcover()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

With this function you can create footprints for vector or raster datasets. Specify a character vector of GDAL readable sources of either vector or raster type. Internally, GDAL will be used to create an sf object with a single column indicating the source and the geometry indicating the bounding box of the respective source. Note, the performance for remote sources is dependent on your connection to the server. If you have other means to create footprints in your resource function (e.g. by using the output {rstac::items_bbox()}) you should prefer those means over this function for remote files.

Usage

make_footprints(
  srcs = NULL,
  filenames = if (inherits(srcs, "sf")) basename(srcs[["source"]]) else basename(srcs),
  what = c("vector", "raster"),
  oo = NULL,
  co = NULL,
  precision = 1e+05
)

Arguments

Value

An sf object with a the files sources and the geometry indicating their spatial footprint.

Examples

# a vector resource
# requires GDAL >= 3.7.0
if (FALSE) {
  vec <- system.file("shape/nc.shp", package = "sf")
  make_footprints(vec, what = "vector")
}

# a raster resource
ras <- system.file("ex/elev.tif", package = "terra")
make_footprints(ras, what = "raster")

Description

Use this function to create a regular grid in a custom CRS. This is used e.g. to create the tile grid for Global Forest Watch in order to retrieve the intersecting tiles with a given portfolio.

Usage

make_global_grid(
  xmin = -180,
  xmax = 170,
  dx = 10,
  ymin = -50,
  ymax = 80,
  dy = 10,
  proj = NULL
)

Arguments

Value

An sf object with a defined grid.

Description

This function allows to efficiently calculate area of mangrove from Global Mangrove Watch - World Conservation Monitoring Centre (WCMC) for polygons. For each polygon, the area of the mangrove (in hectare) for desired year is returned.

Usage

calc_mangroves_area()

Details

The required resources for this indicator are:

• gmw

Value

A function that returns an indicator tibble with mangroves as variable and corresponding areas (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "shell_beach_protected_area_41057_B.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_gmw(years = c(1996, 2016))) %>%
  calc_indicators(calc_mangroves_area()) %>%
  portfolio_long()

aoi

## End(Not run)

Description

mapme_options() sets default options for mapme.biodiversity to control the behavior of downstream functions. Mainly, the output path as well as the chunk size (in ha), can be set. Additionally, the verbosity can be set and the path to a log directory can be controlled. Might be extended by other options in the future.

get_resources() data sets required for the calculation of indicators can be made available. The function supports the specification of several resource functions. To determine the output path, temporary directory and verbosity, the output of mapme_options() is used.

calc_indicators() calculates specific biodiversity indicators. A requirement is that the resources that are mandatory inputs for the requested indicators are available locally. Multiple indicators and their respective additional arguments can be supplied.

This function reads and crops available resources to the extent of a single asset. Specific resources can be queried. If not supplied (the default), all available resources will be prepared.

Usage

mapme_options(..., outdir, chunk_size, retries, verbose, log_dir)

get_resources(x, ...)

calc_indicators(x, ...)

prep_resources(
  x,
  avail_resources = NULL,
  resources = NULL,
  mode = c("portfolio", "asset")
)

Arguments

Value

mapme_options() returns a list of options if no arguments are specified. Otherwise sets matching arguments to new values in the package's internal environment.

get_resources() is called for its side effect of making resources available in the package environment. Returns x, invisibly.

calc_indicators() returns x, invisibly, with an additional nested list column per requested indicator.

prep_resources() returns a list with prepared vector and raster resources as sf and SpatRaster-objects.

Examples

library(mapme.biodiversity)
mapme_options()

Description

The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations.

Usage

get_mcd64a1(years = 2000:2022)

Arguments

Details

The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells.

Value

A function that returns an sf footprint object.

Source

https://planetarycomputer.microsoft.com/dataset/modis-64A1-061

References

Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061

Description

The resource is published by NASA GRACE Tellus. This data set reflects on potential drought conditions in the shallow groundwater section relative to a reference period spanning from 1948 to 2012. It is available as a global raster with a weekly temporal resolution starting with the year 2003. The value indicates the wetness percentile of a given pixel with regard to the reference period.

Usage

get_nasa_grace(years = 2003:2022)

Arguments

Value

A function that returns an sf footprint object.

Description

This resource is processed by the Land Processes Distributed Active Archive Center (LP DAAC) and made available at the Microsoft Planetery Computer. NASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60° N and 56° S latitude. This accounts for about 80% of Earth’s total landmass.

Usage

get_nasa_srtm()

Value

A function that returns an sf footprint object.

Source

https://planetarycomputer.microsoft.com/dataset/nasadem

References

NASA JPL (2020). NASADEM Merged DEM Global 1 arc second V001. NASA EOSDIS Land Processes DAAC. Accessed 2023-07-01 from doi:10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001

Description

This resource is published by Weiss et al. (2018) "A global map of travel time to cities to assess inequalities in accessibility in 2015" on journal nature. Accessibility is the ease with which larger cities can be reached from a certain location. This resource represents the travel time to major cities in the year 2015. Encoded as minutes, representing the time needed to reach that particular cell from nearby city of target population range. The following ranges to nearby cities are available:

• "5k_10k"

• "10k_20k"

• "20k_50k"

• "50k_100k"

• "100k_200k"

• "200k_500k"

• "500k_1mio"

• "1mio_5mio"

• "50k_50mio"

• "5k_110mio"

• "20k_110mio"

• "5mio_50mio"

Usage

get_nelson_et_al(ranges = "20k_50k")

Arguments

Value

A function that returns an sf footprint object.

Note

Note, that the figshare server applies a rather restrictive rate limit thus frequently resulting in opaque error codes (see https://github.com/mapme-initiative/mapme.biodiversity/issues/308). Please set GDAL configuration options to sensible values in case you are running into this issue, e.g.: Sys.setenv("GDAL_HTTP_MAX_RETRY" = "5", "GDAL_HTTP_RETRY_DELAY" = "15").

Source

https://figshare.com/articles/dataset/Travel_time_to_cities_and_ports_in_the_year_2015/7638134/3

References

Weiss, D. J., Nelson, A., Gibson, H. S., Temperley, W., Peedell, S., Lieber, A., … & Gething, P. W. (2018). A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688), 333-336.

Description

WorldPop, which was initiated in 2013, offers easy access to spatial demographic datasets, claiming to use peer-reviewed and fully transparent methods to create global mosaics for the years 2000 to 2020. This function allows to efficiently calculate population count statistics (e.g. total number of population) for polygons. For each polygon, the desired statistic/s (min, max, sum, mean, median, sd or var) is/are returned.

Usage

calc_population_count(engine = "extract", stats = "sum")

Arguments

Details

The required resources for this indicator are:

• worldpop

Value

A function that returns an indicator tibble with the specified populations statistics as variable and the corresponding values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_worldpop(years = 2010:2020)) %>%
  calc_indicators(
    calc_population_count(engine = "extract", stats = c("sum", "median"))
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

write_portfolio() writes a processed biodiversity portfolio to disk. Portfolio data will only be serialized to disk as a GeoPackage including two tables: metadata and indicators. The metadata tables includes, among other simple variables the geometries and a primary key called assetid. The 'indicators' tables includes the foreign key assetid, a column called indicator giving the name of the original indicator as well as the standard indicator columns datetime, variable, unit, and value. For convenience, use read_portfolio() to read such a portfolio GeoPackage back into R.

portfolio_long() transforms a portfolio to long-format, potentially dropping geometries in the process.

portfolio_wide() transforms a portfolio to wide-format, potentially dropping geometries in the process.

Usage

write_portfolio(x, dsn, ...)

read_portfolio(src, ...)

portfolio_long(x, indicators = NULL, drop_geoms = FALSE)

portfolio_wide(x, indicators = NULL, drop_geoms = FALSE)

Arguments

Value

write_portfolio() returns dsn, invisibly.

read_portfolio() returns an sf object object with nested list columns for every indicator found in the GeoPackage source file.

portfolio_long() returns the portfolio object in long-format.

portfolio_wide() returns the portfolio object in wide-format.

Description

This functions allows to calculate averaged precipitation from the CHELSA downscaled precipitation layers. Based on user-selected years, monthly averages of precipitation are calculated.

Usage

calc_precipitation_chelsa(years = 1979:2018, engine = "extract")

Arguments

Details

The required resources for this indicator are:

• chelsa

Value

A function that returns an indicator tibble with variable precipitation and sum of precipitation (in mm/m^2) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_chelsa(years = 2010)) %>%
  calc_indicators(
    calc_precipitation_chelsa(
      years = 2010,
      engine = "extract"
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This functions allows to calculate precipitation sums based on the CHIRPS rainfall estimates. Corresponding to the time-frame of the analysis of the portfolio, monthly precipitation sums are calculated.

Usage

calc_precipitation_chirps(years = 1981:2020, engine = "extract")

Arguments

Details

The required resources for this indicator are:

• chirps

Value

A function that returns an indicator tibble with variable precipitation and sum of precipitation (in mm) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_chirps(years = 2010)) %>%
  calc_indicators(
    calc_precipitation_chirps(
      years = 2010,
      engine = "extract"
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to efficiently calculate precipitation statistics from Worldclim for polygons. For each polygon, the desired statistic/s (min, max, sum, mean, median, sd or var) is/are returned.

Usage

calc_precipitation_wc(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• precipitation layer from worldclim_precipitation

Value

A function that returns an indicator tibble with precipition statistics as variable and corresponding values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_worldclim_precipitation(years = 2018)) %>%
  calc_indicators(
    calc_precipitation_wc(
      engine = "extract",
      stats = c("mean", "median")
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

register_resource() is used to register a new resource function with base information to the package's internal environment used to inform users about available resources. Note, registering a custom resource will only have effect for the current R session.

available_resources() returns a tibble of registered resources with basic information such as the source and the licence.

Usage

register_resource(
  name = NULL,
  description = NULL,
  licence = NULL,
  source = NULL,
  type = NULL
)

available_resources(resources = NULL)

Arguments

Value

register_resource() is called for the side-effect of registering a resource.

available_resources() returns a tibble listing available resources.

Examples

## Not run: 
register_resource(
  name = "gfw_treecover",
  description = "Global Forest Watch - Percentage of canopy closure in 2000",
  licence = "CC-BY 4.0",
  source = "https://data.globalforestwatch.org/documents/tree-cover-2000/explore",
  type = "raster"
)

## End(Not run)
available_resources()

Description

This function allows to calculate slope statistics for polygons. For each polygon, the desired statistic(s) are returned.

Usage

calc_slope(engine = "exactextract", stats = "mean")

Arguments

Details

The required resource for this indicator is:

• nasa_srtm

Value

A function that returns an indicator tibble with specified slope statistics as variables and corresponding values (in degrees).

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_nasa_srtm()) %>%
  calc_indicators(
    calc_slope(stats = c("mean", "median", "sd", "var"), engine = "extract")
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

SoilGrids is a project combining global observation data with machine learning to map the spatial distribution of soil properties across the globe. It is produced at a spatial resolution of 250 meters and each parameters is mapped at different depths. In order to be able to assess prediction uncertainty, besides the mean and median prediction, the 0.05 and 0.95 percentile predictions are available. The following parameters are available:

bdod

Bulk density of the fine earth fraction (kg/dm3)

cec

Cation Exchange Capacity of the soil (cmol(c)/kg)

cfvo

Volumetric fraction of coarse fragments > 2 mm (cm3/100cm3 (volPerc))

clay

Proportion of clay particles < 0.002 mm in the fine earth fraction (g/100g)

nitrogen

Total nitrogen (g/kg)

phh2o

Soil pH (pH)

sand

Proportion of sand particles > 0.05 mm in the fine earth fraction (g/100g)

silt

Proportion of silt particles >= 0.002 mm and <= 0.05 mm in the fine earth fraction (g/100g)

soc

Soil organic carbon content in the fine earth fraction (g/kg)

ocd

Organic carbon density (kg/m3)

ocs

Organic carbon stocks (kg/m²)

Usage

get_soilgrids(layers, depths, stats)

Arguments

Details

Except for ocs, which is only available for a depth of "0-30cm", all other parameters are available at the following depths:

• "0-5cm"

• "5-15cm"

• "15-30cm"

• "30-60cm"

• "60-100cm"

• "100-200cm"

Each parameter and depth is available for the following statistics:

• "Q0.05"

• "Q0.50"

• "mean"

• "Q0.95"

Value

A function that returns an sf footprint object.

Source

https://www.isric.org/explore/soilgrids

References

Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748

Description

This indicator allows the extraction of zonal statistics for resource layers previously downloaded from SoilGrids, thus in total supporting the calculation of zonal statistics for 10 different soil properties at 6 different depths for a total of 4 different model outputs (stat). Zonal statistics will be calculated for all SoilGrid layers that have been previously made available vie get_resources().

Usage

calc_soilproperties(engine = "extract", stats = "mean")

Arguments

Details

The required resource for this indicator is:

• soilgrids

Value

A function that returns an indicator tibble with soilgrid layers and statistics as variables and the corresponding statistics as value.

Examples

if (FALSE) {
  library(sf)
  library(mapme.biodiversity)

  mapme_options(
    outdir = NULL,
    verbose = FALSE
  )

  aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
    package = "mapme.biodiversity"
  ) %>%
    read_sf() %>%
    get_resources(
      get_soilgrids(
        layers = "clay",
        depths = "0-5cm",
        stats = "mean"
      )
    ) %>%
    calc_indicators(
      calc_soilproperties(engine = "extract", stats = c("mean", "median"))
    ) %>%
    portfolio_long()

  aoi
}

Description

This function uses a file path readable by GDAL to check if it can query it for information. Note, this should also work for remote files, e.g. in an S3 bucket. You can use this function in your custom resource function to query if a file is already present at the destination. Note, that performance will be dependent on your connection to the server. It can also be used for files on the local file system.

Usage

spds_exists(path, oo = character(0), what = c("vector", "raster"))

Arguments

Value

A logical, TRUE if the file exists, FALSE if it does not.

Examples

# a vector resource
vec <- system.file("shape/nc.shp", package = "sf")
spds_exists(vec, what = "vector")

# a raster resource
ras <- system.file("ex/elev.tif", package = "terra")
spds_exists(ras, what = "raster")

# a non existing file
spds_exists("not-here.gpkg", what = "vector")

Description

Species richness counts the number of potential species intersecting with a polygon grouped by the IUCN threat categorization. Note, that this indicator function requires the manual download of the respective raster files.

Usage

calc_species_richness(engine = "extract", stats = "mean")

Arguments

Details

The specific meaning of the species richness indicator depends on the supplied raster file.

The required resources for this indicator are:

• iucn

Value

A function that returns an indicator tibble with IUCN layers with specified statistics as variable and respective species richness (count) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

iucn_dir <- system.file("res", "iucn", package = "mapme.biodiversity")
sr_rasters <- list.files(iucn_dir, pattern = "*_SR_*", full.names = TRUE)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_iucn(sr_rasters)) %>%
  calc_indicators(calc_species_richness(stats = "median")) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to efficiently calculate maximum temperature statistics from Worldclim for polygons. For each polygon, the desired statistic/s (min, max, sum, mean, median, sd or var) is/are returned.

Usage

calc_temperature_max_wc(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• maximum temperature layer from worldclim_max_temperature

Value

A function that returns an indicator tibble with maximum temperature statistics as variables and corresponding values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_worldclim_max_temperature(years = 2018)) %>%
  calc_indicators(
    calc_temperature_max_wc(
      engine = "extract",
      stats = c("mean", "median")
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This function allows to efficiently calculate minimum temperature statistics from Worldclim for polygons. For each polygon, the desired statistic/s (min, max, sum, mean, median, sd or var) is/are returned.

Usage

calc_temperature_min_wc(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• minimum temperature layer from worldclim_min_temperature

Value

A function that returns an indicator tibble with minimum temperature statistics as variables and corresponding values as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_worldclim_min_temperature(years = 2018)) %>%
  calc_indicators(
    calc_temperature_min_wc(
      engine = "extract",
      stats = c("mean", "median")
    )
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This resource is part of the publication by Olson et al. (2004) "Terrestrial Ecosystems of the World (TEOW) from WWF-US (Olson)". It depicts 867 terrestrial ecoregions around the world classified into 14 different terrestrial biomes such as forests, grasslands, or deserts. The polygons represent the ecoregions, defined as relatively large units of land or inland water sharing a large majority of biodiversity. The datasets is made available from World Wildlife Fund (WWF) for the year 2001.

Usage

get_teow()

Value

A function that returns an sf footprint object.

References

Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. doi:10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2

Description

Accessibility is the ease with which larger cities can be reached from a certain location. This function allows to efficiently calculate accessibility statistics (i.e. travel time to nearby major cities) for polygons. For each polygon, the desired statistic/s (mean, median or sd) is/are returned.

Usage

calc_traveltime(engine = "extract", stats = "mean")

Arguments

Details

The required resources for this indicator are:

• nelson_et_al

Value

A function that returns an indicator tibble with city ranges and statisics as variable and corresponding values (in minutes) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_nelson_et_al(ranges = "100k_200k")) %>%
  calc_indicators(
    calc_traveltime(engine = "extract", stats = c("min", "max"))
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

Accessibility refers to the ease with which cities can be reached from a certain location. This function allows efficient calculation of accessibility statistics (i.e., travel time to the nearest city) for polygons. For each polygon, the desired statistic/s (mean, median or sd) is/are returned.

Usage

calc_traveltime_2000(engine = "extract", stats = "mean")

Arguments

Details

The required resource for this indicator is:

• accessibility_2000

Value

A function that returns an indicator tibble with accessibility statistics for the year 2000 as variables and corresponding values (in minutes) as values.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_accessibility_2000()) %>%
  calc_indicators(
    calc_traveltime_2000(stats = c("mean", "median", "sd"), engine = "extract")
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This functions allows to efficiently calculate treecover statistics for polygons. For each year in the analysis timeframe, the forest losses in preceding and the current years are subtracted from the treecover in the year 2000 and actual treecover figures within the polygon are returned.

Usage

calc_treecover_area(years = 2000:2023, min_size = 10, min_cover = 35)

Arguments

Details

The required resources for this indicator are:

• gfw_treecover

• gfw_lossyear

Value

A function that returns an indicator tibble with variable treecover and corresponding area (in ha) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(
    get_gfw_treecover(version = "GFC-2023-v1.11"),
    get_gfw_lossyear(version = "GFC-2023-v1.11")
  ) %>%
  calc_indicators(calc_treecover_area(years = 2016:2017, min_size = 1, min_cover = 30)) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This functions allows to efficiently calculate the treecover and emissions indicators in a single function call together. Since most of the pre-processing operations for treecover and emissions are the same, it is more efficient to calculate them in one run if users are actually interested in both statistics. Otherwise users are advised to use the respective single indicator functions.

Usage

calc_treecover_area_and_emissions(
  years = 2000:2023,
  min_size = 10,
  min_cover = 35
)

Arguments

Details

The required resources for this indicator are:

• gfw_treecover

• gfw_lossyear

• gfw_emissions

Value

A function that returns an indicator tibble with variables treecover and emissions ind corresponding values (in ha and Mg) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(
    get_gfw_treecover(version = "GFC-2023-v1.11"),
    get_gfw_lossyear(version = "GFC-2023-v1.11"),
    get_gfw_emissions()
  ) %>%
  calc_indicators(
    calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This functions allows to efficiently calculate emission statistics for areas of interest. For each year in the analysis timeframe, the forest losses from Hansen et al. (2013) are overlayed with the respective emission layer from Harris et al. (2021) and area-wise emission statistics are calculated for each year.

Usage

calc_treecoverloss_emissions(years = 2000:2023, min_size = 10, min_cover = 35)

Arguments

Details

The required resources for this indicator are:

• gfw_treecover

• gfw_lossyear

• gfw_emissions

Value

A function that returns an indicator tibble with emissions as variable and emitted CO2 equivalent (in Mg) as value.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(
    get_gfw_treecover(version = "GFC-2023-v1.11"),
    get_gfw_lossyear(version = "GFC-2023-v1.11"),
    get_gfw_emissions()
  ) %>%
  calc_indicators(
    calc_treecoverloss_emissions(years = 2016:2017, min_size = 1, min_cover = 30)
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

Terrain Ruggedness Index is a measurement developed by Riley, et al. (1999). The elevation difference between the centre pixel and its eight immediate pixels are squared and then averaged and its square root is taken to get the TRI value. This function allows to calculate terrain ruggedness index (tri) statistics for polygons. For each polygon, the desired statistic(s) are returned.

Usage

calc_tri(engine = "extract", stats = "mean")

Arguments

Details

The range of index values and corresponding meaning:

• 0-80 m - level surface

• 81-116 m - nearly level surface

• 117-161 m - slightly rugged surface

• 162-239 m - intermediately rugged surface

• 240-497 m - moderately rugged surface

• 498-958 m - highly rugged surface

• 959-4367 m extremely rugged surface

The required resources for this indicator are:

• nasa_srtm

Value

A function that returns an indicator tibble with tri as variable and the respective statistic as value.

References

Riley, S. J., DeGloria, S. D., & Elliot, R. (1999). Index that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 5(1-4), 23-27.

Examples

## Not run: 
library(sf)
library(mapme.biodiversity)

outdir <- file.path(tempdir(), "mapme-data")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg",
  package = "mapme.biodiversity"
) %>%
  read_sf() %>%
  get_resources(get_nasa_srtm()) %>%
  calc_indicators(
    calc_tri(stats = c("mean", "median", "sd", "var"), engine = "extract")
  ) %>%
  portfolio_long()

aoi

## End(Not run)

Description

This resource distributed by the Uppsala Conflict Data Program (UCDP) constitutes its most diaggregated dataset on individual events of organized violence. It encodes the different actors involved, is spatially disaggregated down to village levels anc currently covers the time period of 1989 to 2021. Older versions of the data set can be downloaded, but users are recommended to download the latest data set.

Usage

get_ucdp_ged(version = "latest")

Arguments

Details

The following versions are available:

• 5.0

• 17.1

• 17.2

• 18.1

• 19.1

• 20.1

• 21.1

• 22.1

• 23.1

• 24.1

• latest

Value

A function that returns an sf footprint object.

Source

https://ucdp.uu.se/downloads/

References

Davies, Shawn, Therese Pettersson & Magnus Öberg (2022). Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4). doi:10.1177/00223433221108428

Description

This resource is published by Fick et al. (2017) "WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas" and represents multiple climatic variables from which we will be requiring minimum temperature, maximum temperature, and mean precipitation layers. The layers are available to download for the period 1960 - 2021 on monthly basis from WorldClim.

Usage

get_worldclim_max_temperature(
  years = 2000:2018,
  resolution = c("2.5m", "5m", "10m")
)

Arguments

Details

This resource represents the maximum temperature, layers available to download for the period 1960 - 2021 on monthly basis from WorldClim. Encoded as (°C), representing the maximum temperature per output grid cell.

Value

A character of file paths.

Source

https://www.worldclim.org/data/index.html

Description

This resource is published by Fick et al. (2017) "WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas" and represents multiple climatic variables from which we will be requiring minimum temperature, maximum temperature, and mean precipitation layers. The layers are available to download for the period 1960 - 2021 on monthly basis from WorldClim.

Usage

get_worldclim_min_temperature(
  years = 2000:2018,
  resolution = c("2.5m", "5m", "10m")
)

Arguments

Details

This resource represents the minimum temperature, layers available to download for the period 1960 - 2021 on monthly basis from WorldClim. Encoded as (°C), representing the minimum temperature per output grid cell.

Value

A function that returns a character of file paths.

Source

https://www.worldclim.org/data/index.html

Description

This resource is published by Fick et al. (2017) "WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas" and represents multiple climatic variables from which we will be requiring minimum temperature, maximum temperature, and mean precipitation layers. The layers are available to download for the period 1960 - 2021 on monthly basis from WorldClim.

Usage

get_worldclim_precipitation(
  years = 1960:2021,
  resolution = c("2.5m", "5m", "10m")
)

Arguments

Details

This resource represents the average precipitation, layers available to download for the period 1960 - 2021 on monthly basis from WorldClim. Encoded as (mm), representing the mean precipitation per output grid cell.

Value

A function that returns an sf footprint object.

Source

https://www.worldclim.org/data/index.html

Description

This resource is published by open spatial demographic data and research organization called WorldPop. This resource represents the population count, 1 km spatial resolution layers available to download from the year 2000 to 2020. The dataset is called as WorldPop Unconstrained Global Mosaics. The encoded cell value represents the total number of people in that particular grid cell.

Usage

get_worldpop(years = 2000)

Arguments

Details

It may be required to increase the timeout option to successfully download theses WorldPop layers from their source location via e.g. options(timeout = 600).

Value

A function that returns an sf footprint object.

Source

https://www.worldpop.org/