mapme.biodiversity (version 0.9.2)

calc_exposed_population_acled: Calculate population exposed to violent conflict from ACLED

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
)

Value

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

Arguments

distance

A numeric vector indicating the buffer radius in meters. If length is 1, the same buffer size around included conflict events is drawn. Otherwise, it must be equal to the length of included categories selected with filter_types.

filter_category

A character indicating the categories to be used to calculate the exposed population by. Defaults to event_type meaning one estimation per event type will be returned.

filter_types

A character vector of event types of the respective category specified in filter_category to retain. Defaults to NULL, meaning that no filter is applied and all types are retained.

years

A numeric vector indicating for which years to calculate the exposed population. Restricted to available years for ACLED. For years not intersecting with available WorldPop layers, the first layer is used for earlier years and the last layer to more recent years.

precision_location

A numeric indicating precision value for the geolocation up to which events are included. Defaults to 1.

precision_time

A numeric indicating the precision value of the temporal coding up to which events are included. Defaults to 1.

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

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

# \dontshow{
mapme.biodiversity:::.copy_resource_dir(file.path(tempdir(), "mapme-data"))
# }
if (FALSE) {
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
}
}