CACE Conficts and Deaths Comparison
This R notebook compares Cities and Armed Conflicts Events (CACE) Conflicts and Death markers. The study region is all countries in the African continent, including Madagascar.
The following graphics evaluate conflicts deaths by type of UCDP organized violence.
- State-based conflict - A state is either an internationally recognized sovereign government controlling a specified territory, or an internationally unrecognized government controlling a specified territory whose sovereignty is not disputed by another internationally recognized sovereign government previously controlling the same territory.Â
- Non-state conflict - The use of armed force between two organised armed groups, neither of which is the government of a state, which results in at least 25 battle-related deaths in a year.
- One-sided violence - The deliberate use of armed force by the government of a state or by a formally organised group against civilians which results in at least 25 deaths in a year.
Full definitions for Type of Violence can be found at https://www.pcr.uu.se/research/ucdp/definitions.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ purrr 1.2.0
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(biscale)## Warning: package 'biscale' was built under R version 4.5.3library(cowplot)## Warning: package 'cowplot' was built under R version 4.5.3##
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## discard
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## col_factorlibrary(rjson)
library(sf)## Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUElibrary(geojsonsf)world.data <- fromJSON(file = "world-data.geojson")
features <- world.data[['features']]
continent <- sapply(features, function(x) x$properties[['continent']])
geo <- sapply(features, function(x) x$properties[['iso_a3']]) %>%
str_to_lower()
continents <- as.data.frame(cbind(geo, continent))
continent.countries <- continents[continents$continent == "Africa", c("geo")]
countries.json <- fromJSON(file = "countries.geojson")
countries <- geojson_sf(toJSON(countries.json))
countries$geo <- str_to_lower(countries$iso_a3)
countries <- subset(countries, geo %in% continent.countries)conflict.data <- read.csv(file = "cace_conflicts.csv") %>%
st_as_sf(., wkt = "geom_wkt", crs = 4326) %>%
st_join(., countries, left = FALSE)
conflict.data$violence_type <-
with(conflict.data, ifelse(type_of_violence == 1, "1. State-based",
ifelse(type_of_violence == 2, "2. Non-state",
"3. One-sided")))
subset(
conflict.data,
select = c("id", "year", "geo", "country_name", "city_name", "violence_type")
) %>%
head()## Simple feature collection with 6 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -1.72753 ymin: 34.84969 xmax: 3.666667 ymax: 36.75
## Geodetic CRS: WGS 84
## id year geo country_name city_name violence_type
## 604 1489 1998 dza Algeria 1. State-based
## 605 1490 1996 dza Algeria 1. State-based
## 606 1491 2000 dza Algeria 1. State-based
## 607 1492 1994 dza Algeria 1. State-based
## 608 1493 1997 dza Algeria 1. State-based
## 609 1496 1995 dza Algeria 1. State-based
## geom_wkt
## 604 POINT (-1.72753 34.84969)
## 605 POINT (-0.5 35.66667)
## 606 POINT (0.166667 35.41667)
## 607 POINT (3 36.08333)
## 608 POINT (3 36.08333)
## 609 POINT (3.666667 36.75)colnames(conflict.data)## [1] "id" "active_year" "type_of_violence"
## [4] "conflict_new_id" "conflict_name" "dyad_new_id"
## [7] "dyad_name" "side_a_new_id" "gwnoa"
## [10] "side_a" "side_b_new_id" "gwnob"
## [13] "side_b" "where_prec" "where_coordinates"
## [16] "city" "city_name" "capital"
## [19] "major_city" "top3_cities" "comment"
## [22] "adm_1" "adm_2" "latitude"
## [25] "longitude" "priogrid_gid" "country_name"
## [28] "country_id" "region" "event_clarity"
## [31] "date_prec" "year" "date_start"
## [34] "date_end" "deaths_a" "deaths_b"
## [37] "deaths_civilians" "deaths_unknown" "best"
## [40] "low" "high" "name"
## [43] "iso_a3" "geo" "geom_wkt"
## [46] "violence_type"# Create map of Type of Violence markers
ggplot() +
geom_sf(
data = countries,
fill = "lightgray"
) +
geom_sf(
data = conflict.data,
mapping = aes(col = as.factor(violence_type)),
size = 0.75
) +
labs(
title = "CACE Type of Violence",
col = "Type"
) +
theme_bw() +
theme(
plot.title = element_text(hjust = 0.5)
)
sum.data <- conflict.data %>%
group_by(
geo
) %>%
summarise(
iso_a3 = toupper(first(geo)),
Conflicts = n(),
Deaths = sum(deaths_a) + sum(deaths_b)
) %>%
as.data.frame(
.
) %>%
subset(
.,
select = c("iso_a3", "Conflicts", "Deaths")
)
head(sum.data, 5)## iso_a3 Conflicts Deaths
## 1 AGO 1945 9022
## 2 BDI 1453 5959
## 3 BFA 21 5
## 4 CAF 1208 875
## 5 CIV 293 506ggplot(
data = sum.data
) +
geom_point(
mapping = aes(x = Conflicts, y = Deaths),
col = "royalblue"
) +
labs(
title = "CACE Conflicts vs Deaths per Country"
) +
scale_x_continuous(
labels = number_format(big.mark = ",")
) +
scale_y_continuous(
labels = number_format(big.mark = ",")
) +
theme_bw() +
theme(
plot.title = element_text(hjust = 0.5)
)
Create a bi-variate map comparing conflicts and deaths by country boundary.
map.data <- bi_class(
sum.data,
x = Conflicts, y = Deaths,
style = "quantile", dim = 3
) %>%
merge(
countries, .,
by.x = "iso_a3", by.y = "iso_a3",
all.x = TRUE
)
head(map.data)## Simple feature collection with 6 features and 6 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -5.470565 ymin: -26.82854 xmax: 30.75226 ymax: 15.11616
## Geodetic CRS: WGS 84
## iso_a3 name geo Conflicts Deaths bi_class
## 1 AGO Angola ago 1945 9022 3-3
## 2 BDI Burundi bdi 1453 5959 3-3
## 3 BEN Benin ben NA NA <NA>
## 4 BFA Burkina Faso bfa 21 5 1-1
## 5 BWA Botswana bwa NA NA <NA>
## 6 CAF Central African Republic caf 1208 875 3-2
## geometry
## 1 MULTIPOLYGON (((16.32653 -5...
## 2 POLYGON ((29.34 -4.499983, ...
## 3 POLYGON ((2.691702 6.258817...
## 4 POLYGON ((-2.827496 9.64246...
## 5 POLYGON ((25.64916 -18.5360...
## 6 POLYGON ((15.27946 7.421925...# Create the map
map <- ggplot() +
geom_sf(
data = map.data,
mapping = aes(fill = bi_class),
color = "black",
size = 0.1,
show.legend = FALSE
) +
bi_scale_fill(
pal = "DkBlue",
dim = 3
) +
labs(
title = "CACE Conflicts and Deaths",
subtitle = "Comparision by Country"
) +
theme_bw() +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)
)
# Create the legend
legend <- bi_legend(
pal = "DkBlue",
dim = 3,
xlab = "Conflicts",
ylab = "Deaths",
size = 8
)
# Combine map with legend
ggdraw() +
draw_plot(map, 0, 0, 1, 1) +
draw_plot(legend, 0.0, 0.2, 0.2, 0.2)