Geographic Alignment — indicator_donor_geographic

Calculates the Jaccard similarity between donor-funded locations and reference locations.

Usage

indicator_donor_geographic_alignment(
  flows,
  priority_destinations,
  donors = NULL
)

Arguments

flows: A dataframe containing sourceObjects, destinationObjects.
priority_destinations: Character vector of priority locations.
donors: (optional) A dataframe with donor names to merge with the results.

Value

A list with a tibble with columns donor and Geographic_Alignment and a plot.

Examples


destinations <- flows |>
      tidyr::unnest(destinationObjects, names_repair = "unique", names_sep = "_") |>
      dplyr::filter(destinationObjects_type == "Location") |>
      dplyr::pull(destinationObjects_name) |>
      unlist() |>
      unique()
#dput(destinations)

# High-visibility, acute crises get funded
high_visibility <- c(
  "Ukraine", "Syrian Arab Republic", "Occupied Palestinian Territory",
  "Sudan", "South Sudan", "Yemen", "Afghanistan", "Myanmar",
  "Ethiopia", "Somalia", "Democratic Republic of the Congo",
  "Central African Republic", "Haiti"
)

# Long-standing crises with major donor attention maintain coverage
long_standing <- c(
  "Lebanon", "Jordan", "Iraq", "Türkiye", "Bangladesh", "Chad",
  "Cameroon", "Niger", "Burkina Faso", "Mali", "Nigeria",
  "Colombia", "Venezuela, Bolivarian Republic of"
)

# Regional refugee responses collapse
regional_refugee <- c(
  "Region - Southern and Eastern Africa", "Region - Middle East and Northern Africa",
  "Region - West and Central Africa", "Region - Latin America and the Caribbean",
  "Region - Asia and the Pacific", "Uganda", "Kenya", "Tanzania, United Republic of",
  "Djibouti", "Mauritania", "Cameroon", "Moldova, Republic of"
)

# Lower-profile crises get abandoned
lower_profile <- c(
  "Burundi", "Rwanda", "Eritrea", "Nepal", "Georgia", "Cuba",
  "Dominican Republic", "Madagascar", "Mozambique", "Angola", "Malawi",
  "Comoros", "Liberia", "Côte d'Ivoire", "Senegal", "Ghana", "Guinea",
  "Guinea-Bissau", "Lesotho", "Eswatini", "Zambia", "Cambodia",
  "Lao People's Democratic Republic", "Philippines", "Timor-Leste",
  "Sri Lanka", "Togo", "Benin", "Gambia", "Nicaragua", "Honduras",
  "El Salvador", "Guatemala", "Costa Rica", "Peru", "Ecuador", "Panama",
  "Bolivia, Plurinational State of", "Papua New Guinea", "Solomon Islands",
  "Kiribati", "Vanuatu", "Micronesia, Federated States of", "Tonga",
  "Fiji", "Cook Islands", "Tuvalu", "Samoa", "Nauru", "Niue", "Palau",
  "Maldives", "Armenia", "Uzbekistan", "Kazakhstan", "Turkmenistan",
  "Azerbaijan", "Tajikistan", "Kyrgyzstan", "Mongolia", "Mauritius",
  "Cape Verde", "Equatorial Guinea", "Gabon", "Botswana", "Namibia",
  "Seychelles", "Trinidad and Tobago", "Grenada", "Saint Lucia",
  "Saint Vincent and the Grenadines", "Barbados", "Guyana", "Suriname",
  "Belize", "Anguilla (United Kingdom)", "Aruba (Netherlands)",
  "Curaçao (Netherlands)"
)


result <- indicator_donor_geographic_alignment(flows, 
                       priority_destinations = high_visibility)
print(result$plot)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.