Exploring the Geo-PKO dataset
Last updated: 2020-07-10
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Knit directory: GeoPKO/
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This document contains a series of steps that the project members have performed to extract meaningful information the Geo-PKO dataset. More details on the dataset, as well as the version used here, can be found on its homepage.
Setting up
Load packages.
library(tidyverse)
library(readr)
library(ggthemes)
library(knitr)
library(kableExtra)Import the dataset.
GeoPKO <- read_csv("data/geopko.csv")An overview
Let’s have a quick look at the first few rows of the dataset.
kable(GeoPKO[1:5,]) %>% kable_styling() %>%
scroll_box(width = "100%", height = "200px")| Source | Mission | year | month | location | country | latitude | longitude | No.troops | RPF | RPF_No | RES | RES_No | FP | FP_No | No.TCC | name.of.TCC1 | No.troops.per.TCC1 | name.of.TCC2 | No.troops.per.TCC2 | name.of.TCC3 | No.troops.per.TCC3 | name.of.TCC4 | No.troops.per.TCC4 | name.of.TCC5 | No.troops.per.TCC5 | name.of.TCC6 | No.troops.per.TCC6 | name.of.TCC7 | No.troops.per.TCC7 | name.of.TCC8 | No.troops.per.TCC8 | name.of.TCC9 | No.troops.per.TCC9 | name.of.TCC10 | No.troops.per.TCC10 | name.of.TCC11 | No.troops.per.TCC11 | name.of.TCC12 | No.troops.per.TCC12 | name.of.TCC13 | No.troops.per.TCC13 | name.of.TCC14 | No.troops.per.TCC14 | UNPOL..dummy. | UNMO..dummy. | HQ | LO | comments | cow_code | gwno | name | TCC1 | TCC2 | TCC3 | TCC4 | TCC5 | TCC6 | TCC7 | TCC8 | TCC9 | TCC10 | TCC11 | TCC12 | TCC13 | TCC14 | ADM1_id | ADM1_name | ADM2_id | ADM2_name | PRIOID |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Map no. 4309 | BINUB | 2007 | 3 | Bujumbura | Burundi | -3.382200 | 29.364400 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 3 | 0 | NA | 516 | 516 | Burundi | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3348 | Bujumbura Mairie | 20147 | Roherero | 124979 |
| Map no. 4309 Rev. 1 | BINUB | 2009 | 8 | Bujumbura | Burundi | -3.382200 | 29.364400 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 3 | 0 | NA | 516 | 516 | Burundi | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3348 | Bujumbura Mairie | 20147 | Roherero | 124979 |
| Map no. 4203 Rev. 2 | MINUCI | 2003 | 8 | Abidjan | Ivory Coast | 5.309657 | -4.012656 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 3 | 0 | MINUCI HQ; Also ECOWAS main HQ; France HQ | 437 | 437 | Cote D’Ivoire | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3411 | Lagunes | 40389 | Abidjan | 137152 |
| Map no. 4203 Rev. 3 | MINUCI | 2003 | 11 | Abidjan | Ivory Coast | 5.309657 | -4.012656 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 3 | 0 | MINUCI HQ; Also ECOWAS main HQ; France HQ; Fanci HQ | 437 | 437 | Cote D’Ivoire | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3411 | Lagunes | 40389 | Abidjan | 137152 |
| Map no. 4203 Rev. 4 | MINUCI | 2004 | 1 | Abidjan | Ivory Coast | 5.309657 | -4.012656 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 3 | 0 | MINUCI HQ; ECOWAS main HQ; France HQ; Fanci HQ | 437 | 437 | Cote D’Ivoire | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3411 | Lagunes | 40389 | Abidjan | 137152 |
The dataset covers UN peacekeeping missions in Africa between 1994 and 2018. We can use the dataset to extract the number of active missions during this period.
NoMission <- GeoPKO %>% select(year, Mission) %>% distinct(year, Mission) %>% count(year)
Plot1 <- ggplot(NoMission, aes(x=(as.numeric(year)), y=n)) + geom_point() + geom_line(size=0.5) +
scale_x_continuous("Year", breaks=seq(1994, 2018, 1))+theme_classic()+
scale_y_continuous("Number of missions", breaks=seq(0,10,1)) +
theme(panel.grid=element_blank(),
axis.text.x=element_text(angle=45, vjust=0.5))
Plot1
### What missions are included?
The following table provides users with a list of missions and the duration for which they are covered by the dataset.
GeoPKO$Start <- ave(GeoPKO$year, GeoPKO$Mission, FUN = min)
GeoPKO$End <- ave(GeoPKO$year, GeoPKO$Mission, FUN = max)
Years <- GeoPKO %>% select(Mission, Start, End)
Years <- Years %>% group_by(Start, End, Mission) %>% distinct()
kable(Years[,], caption = "UN Missions in the GeoPKO dataset") %>% kable_styling()| Mission | Start | End |
|---|---|---|
| BINUB | 2007 | 2009 |
| MINUCI | 2003 | 2004 |
| MINURCA | 1998 | 2000 |
| MINURCAT | 2008 | 2010 |
| MINURSO | 1995 | 2018 |
| MINUSCA | 2014 | 2018 |
| MINUSMA | 2014 | 2018 |
| MONUA | 1997 | 1999 |
| MONUC | 1999 | 2010 |
| MONUSCO | 2010 | 2018 |
| ONUB | 2004 | 2006 |
| ONUMOZ | 1994 | 1994 |
| UNAMID | 2008 | 2018 |
| UNAMIR | 1994 | 1995 |
| UNAMSIL | 1999 | 2005 |
| UNAVEM II | 1994 | 1994 |
| UNAVEM III | 1995 | 1997 |
| UNIOSIL | 2006 | 2008 |
| UNIPSIL | 2008 | 2008 |
| UNISFA | 2011 | 2018 |
| UNMIL | 2003 | 2018 |
| UNMIS | 2005 | 2011 |
| UNMISS | 2011 | 2018 |
| UNOCI | 2004 | 2017 |
| UNOMIL | 1994 | 1997 |
| UNOMSIL | 1998 | 1999 |
| UNOSOM II | 1994 | 1994 |
Number of troops
Visualizing deployment locations
An advantage to the Geo-PKO dataset is that it records the numbers of troops by their specific deployment locations. Therefore, users can quickly visualize where active troops are in a mission. Below are some examples of visuaization.
First, we can produce a quick snapshot of the deployment size in 2018, as well as the missions that were active in that year. We start by subsetting the main dataset to include entries for the year of 2018 and our variables of interests. GeoPKO reports deployment sizes according to the available maps published by the UN. Therefore, to obtain the numbers of troop deployment at the yearly level, we calculate the average number of troops per location over the months recorded.
GeoPKO$No.troops <- as.numeric(GeoPKO$No.troops)Warning: NAs introduced by coercionmap2018df <- GeoPKO %>% filter(year==2018) %>%
select(Mission, year, location, latitude, longitude, No.troops, HQ, country)
map2018df1 <- map2018df %>% group_by(location, Mission) %>%
mutate(ave = mean(No.troops, na.rm=TRUE)) %>% distinct()
kable(map2018df1[90:95,], caption = "A preview of this dataframe") %>% kable_styling()| Mission | year | location | latitude | longitude | No.troops | HQ | country | ave |
|---|---|---|---|---|---|---|---|---|
| MONUSCO | 2018 | Butembo | 0.114283 | 29.30141 | 150 | 0 | DRC | 150 |
| MONUSCO | 2018 | Kinshasa | -4.329722 | 15.31500 | 1250 | 3 | DRC | 1190 |
| MONUSCO | 2018 | Tshikapa | -6.423230 | 20.79399 | 150 | 0 | DRC | 150 |
| MONUSCO | 2018 | Kalemie | -5.903344 | 29.19230 | 800 | 0 | DRC | 680 |
| MONUSCO | 2018 | Dungu | 3.616667 | 28.56667 | 300 | 0 | DRC | 820 |
| MONUSCO | 2018 | Bunia | 1.562500 | 30.24842 | 900 | 2 | DRC | 1150 |
Next, we obtain the geometric shapes from rnaturalearth, and filter for countries in Africa.
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
world <- ne_countries(scale = "medium", returnclass = "sf")
Africa <- world %>% filter(region_un == "Africa")library(ggrepel)
library(viridis)
p2 <- ggplot(data=Africa) + geom_sf() +
geom_point(data = map2018df1, aes(x=longitude, y=latitude, size= ave, color= ave), alpha=.7)+
scale_size_continuous(name="Average Troop Deployment", range=c(1,12), breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000)) +
scale_color_viridis(option="cividis", breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000), name="Average Troop Deployment" ) +
guides( colour = guide_legend()) +
geom_point(data = map2018df1 %>% filter(HQ==3), aes (x=longitude, y=latitude), color = "red", shape = 4, size=7)+
geom_label_repel(data = map2018df1 %>% filter(HQ==3), aes(x=longitude, y=latitude, label=Mission)) +
labs (title ="UN Peacekeeping Deployment in Africa - 2018 (approx.)", color='Average Troop Deployment') +
theme(
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#f5f5f2", color = NA),
panel.background = element_rect(fill = "#f5f5f2", color = NA),
legend.background = element_rect(fill = "#f5f5f2", color = NA),
plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
panel.grid=element_blank(),
axis.title=element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
legend.key=element_blank()
)
p2
Here is the same map, but this time the points show both troop size and country.
p3 <- ggplot(data=Africa) + geom_sf() +
geom_point(data=map2018df1,
aes(x=longitude, y=latitude, size=ave, color=country), alpha=.4)+
geom_point(data=map2018df1 %>% filter(HQ==3),
aes(x=longitude, y=latitude), color="black", shape=16, size=2)+
geom_label_repel(data=map2018df1 %>% filter(HQ==3),
aes(x=longitude, y=latitude, label=Mission))+
labs(title="UN Peacekeeping Deployment in Africa - 2018")+
scale_size(range = c(1, 12))+
labs(size="Average number of troops \n(continuous scale)",col="Country")+
theme(text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#f5f5f2", color = NA),
panel.background = element_rect(fill = "#f5f5f2", color = NA),
legend.background = element_rect(fill = "#f5f5f2", color = NA),
plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
panel.grid=element_blank(),
axis.title=element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
legend.key=element_blank(),
legend.position="right",
legend.box="horizontal"
)
p3
How has this changed over the period covered by the dataset? An animated graph is great for this purpose. The first step is to prepare a dataframe, much similar to what has been done above for 2018. First we would calculate the average number of troops that is deployed to a location per mission per year.
gif_df <- GeoPKO %>% select(Mission, year, location, latitude, longitude, No.troops, HQ) %>%
group_by(Mission, year, location) %>%
mutate(ave.no.troops = as.integer(mean(No.troops, na.rm=TRUE))) %>% select(-No.troops) %>% distinct() %>% drop_na(ave.no.troops)Next, we add animation to the above map using the package gganimate.
library(gganimate)
# Transforming the "year" variable into a discrete variable.
gif_df$year <- as.factor(gif_df$year)
ggplot(data=Africa) + geom_sf() +
geom_point(data = gif_df, aes(x=longitude, y=latitude, size= ave.no.troops, color= ave.no.troops, group=year), alpha=.7)+
scale_size_continuous(name="Average Troop Deployment", range=c(1,12), breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000)) +
scale_color_viridis(option="cividis", breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000), name="Average Troop Deployment" ) +
guides(colour = guide_legend()) +
theme(
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#f5f5f2", color = NA),
panel.background = element_rect(fill = "#f5f5f2", color = NA),
legend.background = element_rect(fill = "#f5f5f2", color = NA),
plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
panel.grid=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank(),
legend.key=element_blank(),
plot.caption=element_text(hjust=0, face="italic"))+
transition_states(states=year, transition_length = 3, state_length=3)+
labs(title="UN Peacekeeping in intrastate armed conflicts in Africa: {closest_state}",
color="Average Deployment Size",
caption="Source: The GeoPKO dataset 1.2")+
enter_fade()
#run the following command to save the plot
#anim_save("animatedUNPKO.gif", p4)
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_Sweden.1252 LC_CTYPE=English_Sweden.1252
[3] LC_MONETARY=English_Sweden.1252 LC_NUMERIC=C
[5] LC_TIME=English_Sweden.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gganimate_1.0.6 viridis_0.5.1 viridisLite_0.3.0
[4] ggrepel_0.8.2 sf_0.9-4 rnaturalearthdata_0.1.0
[7] rnaturalearth_0.1.0 kableExtra_1.1.0 knitr_1.29.3
[10] ggthemes_4.2.0 forcats_0.5.0 stringr_1.4.0
[13] dplyr_0.8.3 purrr_0.3.4 readr_1.3.1
[16] tidyr_1.0.0 tibble_3.0.1 ggplot2_3.3.1
[19] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-137 fs_1.4.1 lubridate_1.7.8 webshot_0.5.2
[5] progress_1.2.2 httr_1.4.1 rprojroot_1.3-2 tools_3.5.2
[9] backports_1.1.7 rgdal_1.4-8 R6_2.4.1 KernSmooth_2.23-15
[13] rgeos_0.5-2 DBI_1.1.0 colorspace_1.4-1 withr_2.2.0
[17] sp_1.4-2 tidyselect_0.2.5 gridExtra_2.3 prettyunits_1.1.1
[21] compiler_3.5.2 git2r_0.27.1 cli_2.0.2 rvest_0.3.5
[25] xml2_1.3.2 labeling_0.3 scales_1.1.1 classInt_0.4-3
[29] digest_0.6.25 rmarkdown_1.18 pkgconfig_2.0.3 htmltools_0.4.0
[33] dbplyr_1.4.2 highr_0.8 rlang_0.4.6 readxl_1.3.1
[37] rstudioapi_0.11 farver_2.0.3 generics_0.0.2 jsonlite_1.6.1
[41] magrittr_1.5 Rcpp_1.0.4.6 munsell_0.5.0 fansi_0.4.1
[45] lifecycle_0.2.0 stringi_1.4.6 whisker_0.4 yaml_2.2.1
[49] plyr_1.8.6 grid_3.5.2 promises_1.1.0 crayon_1.3.4
[53] lattice_0.20-38 haven_2.2.0 hms_0.5.3 pillar_1.4.4
[57] reprex_0.3.0 glue_1.4.1 evaluate_0.14 gifski_0.8.6
[61] modelr_0.1.5 vctrs_0.3.1 tweenr_1.0.1 httpuv_1.5.2
[65] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
[69] broom_0.5.6 e1071_1.7-3 later_1.0.0 class_7.3-14
[73] units_0.6-6 ellipsis_0.3.0