Last updated: 2020-11-25

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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, be it for a specific mission or a region. Below are some examples of visualization.

To start with, we can take a snapshot of the deployment of all the missions that were active in 2018 in Africa. We start by subsetting the main dataset to include only entries for the year of 2018 in the continent Africa, as well as our variables of interests. Geo-PKO 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. We should end up with something similar to the table below.

library(dplyr)
library(tidyr)
library(readr)
library(knitr)
library(kableExtra) 

GeoPKO <- readr::read_csv("data/Geo_PKO_v2_ISO3.csv", col_types = cols(.default="c"),
                          locale=readr::locale(encoding="latin1")) #importing the dataset

GeoPKO2018 <- GeoPKO %>% filter(year==2018) %>%  
  select(Mission, year, location, latitude, longitude, No.troops, HQ, country, iso3c) %>%
  mutate_at(vars(latitude, longitude, No.troops), as.numeric) %>% 
  group_by(location, Mission, latitude, longitude) %>% 
  mutate(YearlyAverage = round(mean(No.troops, na.rm=TRUE))) %>%
  arrange(desc(HQ)) %>% slice(1)

kable(GeoPKO2018, caption = "An extract of the 2018 dataframe") %>% kable_styling() %>% 
  scroll_box(width = "100%", height = "200px")
An extract of the 2018 dataframe
Mission year location latitude longitude No.troops HQ country iso3c YearlyAverage
UNISFA 2018 Abyei Camp 9.6270950 28.434498 1050 3 Sudan SDN 1170
MONUSCO 2018 Adikivu -2.3257290 28.812325 1250 2 DRC COD 796
UNISFA 2018 Agany Toak 9.5274830 28.434313 35 0 Sudan SDN 35
UNISFA 2018 Agok 9.3574860 28.582579 70 0 Sudan SDN 91
MINUSMA 2018 Aguelhok 19.4613900 0.858890 335 0 Mali MLI 335
MINURSO 2018 Agwanit 22.1934000 -13.140930 0 0 Western Sahara MAR 0
UNAMID 2018 Al Da’ein 11.4618600 26.125830 370 2 Sudan SDN 269
UNISFA 2018 Alal 9.7936640 28.339436 150 0 Sudan SDN 150
MINUSMA 2018 Ansongo 15.6597000 0.502200 950 0 Mali MLI 950
UNFICYP 2018 Athienou 35.0618300 33.541660 35 0 Cyprus CYP 35
UNISFA 2018 Athony 9.4705590 28.464043 335 2 Sudan SDN 335
UNMISS 2018 Aweil 8.7681570 27.400190 150 0 South Sudan SSD 150
MINURSO 2018 Awsard 22.5516390 -14.331643 0 0 Western Sahara MAR 0
MINUSMA 2018 Bamako 12.6500000 -8.000000 705 3 Mali MLI 705
MINUSCA 2018 Bambari 5.7638800 20.670733 800 0 Central African Republic CAF 1017
MINUSCA 2018 Bangassou 4.7379210 22.816399 650 0 Central African Republic CAF 650
MINUSCA 2018 Bangui 4.3937970 18.559861 4085 3 Central African Republic CAF 4308
UNISFA 2018 Banton 9.5063890 28.470323 335 0 Sudan SDN 335
MONUSCO 2018 Baraka -4.1043250 29.089285 150 0 DRC COD 150
MINUSCA 2018 Batangafo 7.3009280 18.289260 0 0 Central African Republic CAF 0
MONUSCO 2018 Bendera -5.0655560 28.915556 150 0 DRC COD 150
MONUSCO 2018 Beni 0.5000000 29.466667 800 1 DRC COD 633
MONUSCO 2018 Beni Boikene 0.5402770 29.489585 0 1 DRC COD 0
UNMISS 2018 Bentiu 9.2599050 29.800110 1450 2 South Sudan SSD 1450
MINUSMA 2018 Ber 16.8349270 -2.531944 150 0 Mali MLI 150
MINUSCA 2018 Berberati 4.2573560 15.787821 650 0 Central African Republic CAF 650
MINURSO 2018 Bir Lahlou 26.3268627 -9.549474 0 0 Western Sahara MAR 0
MINUSCA 2018 Birao 10.2932620 22.782273 650 0 Central African Republic CAF 650
MONUSCO 2018 Bogoro 1.4102150 30.279670 150 0 DRC COD 150
UNMISS 2018 Bor 6.2059310 31.556326 1750 2 South Sudan SSD 1750
MINUSCA 2018 Bossangoa 6.4975080 17.450452 650 0 Central African Republic CAF 650
MINUSCA 2018 Bouar 5.9430660 15.598526 1100 2 Central African Republic CAF 1050
MINUSCA 2018 Bria 6.5390230 21.991110 150 2 Central African Republic CAF 417
MONUSCO 2018 Bukavu -2.5000000 28.866667 600 2 DRC COD 600
MONUSCO 2018 Bunia 1.5625000 30.248417 1700 2 DRC COD 1108
UNMISS 2018 Bunji 9.9466650 33.810268 150 0 South Sudan SSD 150
UNAMID 2018 Buram 10.8566980 25.159272 150 0 Sudan SDN 150
MONUSCO 2018 Butembo 0.1142830 29.301407 150 0 DRC COD 150
UNDOF 2018 Camp Faouar 33.2388940 35.910609 520 3 Syria SYR 432
UNDOF 2018 Camp Ziouani 33.1089360 35.816438 1485 0 Syria SYR 1423
MINUJUSTH 2018 Cap-Haïtien 19.7593800 -72.198150 NA 0 Haiti HTI NaN
MINUSCA 2018 Carnot 4.9397900 15.877884 0 0 Central African Republic CAF 0
MINUSCA 2018 Dékoa 6.3188650 19.077846 0 0 Central African Republic CAF 0
UNDOF 2018 Damascus 33.5102000 36.291280 0 3 Syria SYR 0
UNFICYP 2018 Dhenia 35.1667500 33.145990 0 0 Cyprus CYP 0
UNFICYP 2018 Dherinia 35.0648400 33.960830 35 0 Cyprus CYP 35
UNISFA 2018 Diffra 10.0392480 28.406966 335 2 Sudan SDN 335
UNISFA 2018 Dokura 9.6789860 28.458475 300 2 Sudan SDN 300
MINUSMA 2018 Douentza 15.0015500 -2.949780 1100 0 Mali MLI 1160
UNISFA 2018 Dungoup 9.6291360 28.495775 150 0 Sudan SDN 150
MONUSCO 2018 Dungu 3.6166670 28.566667 950 2 DRC COD 733
MINUSMA 2018 Dyabali 14.6937000 -6.019700 1100 0 Mali MLI 1100
UNAMID 2018 El Fasher 13.6333333 25.350000 935 3 Sudan SDN 935
UNAMID 2018 El Geneina 13.4500000 22.450000 635 2 Sudan SDN 721
UNAMID 2018 El Sireaf 13.8873030 23.517703 150 0 Sudan SDN 150
UNFICYP 2018 Famagusta 35.1248900 33.941350 150 2 Cyprus CYP 150
UNISFA 2018 Farouk 10.1109360 28.420397 150 0 Sudan SDN 150
MINUJUSTH 2018 Fort-Liberté 19.6627300 -71.837980 0 0 Haiti HTI 0
MINUSMA 2018 Gao 16.2716700 -0.044720 5635 2 Mali MLI 5605
UNISFA 2018 Gok-machar 9.2154120 26.859909 0 2 Sudan SDN 0
UNISFA 2018 Goli 9.8485970 28.477926 220 0 Sudan SDN 220
UNAMID 2018 Golo 13.1322800 24.280820 150 0 Sudan SDN 188
MONUSCO 2018 Goma -1.6833330 29.233333 1550 2 DRC COD 1008
MINUJUSTH 2018 Gonaïves 19.4475500 -72.689280 NA 0 Haiti HTI NaN
MINUSMA 2018 Gossi 15.8229940 -1.300646 150 0 Mali MLI 150
MINUSMA 2018 Goundam 16.4145300 -3.670750 300 0 Mali MLI 300
UNAMID 2018 Graida 11.3394850 25.139697 300 1 Sudan SDN 225
UNISFA 2018 Highway 9.5526470 28.467694 150 0 Sudan SDN 270
MONUSCO 2018 Himbe -1.4326730 28.888605 300 0 DRC COD 300
MONUSCO 2018 Himbi -1.6367610 29.247930 300 0 DRC COD 300
MINUJUSTH 2018 Hinche 19.1500000 -72.016670 0 0 Haiti HTI 0
MINUJUSTH 2018 Jérémie 18.6500000 -74.116670 NA 0 Haiti HTI NaN
MINUJUSTH 2018 Jacmel 18.2342700 -72.535390 0 0 Haiti HTI 0
UNMISS 2018 Juba 4.8459720 31.601203 3350 3 South Sudan SSD 2830
UNAMID 2018 Kabkabiya 13.6474600 24.086729 185 1 Sudan SDN 214
UNISFA 2018 Kadugli 11.0105960 29.713688 0 2 Sudan SDN 0
MINUSCA 2018 Kaga Bandoro 6.9959470 19.184232 1100 2 Central African Republic CAF 1100
MONUSCO 2018 Kalemie -5.9033440 29.192303 650 0 DRC COD 700
UNAMID 2018 Kalma 12.0155960 24.998158 150 0 Sudan SDN 150
MONUSCO 2018 Kamango 0.6177730 29.901965 150 0 DRC COD 150
MONUSCO 2018 Kamanyola -2.7404250 29.004775 150 0 DRC COD 150
MONUSCO 2018 Kampala 0.3155556 32.565556 0 0 Uganda UGA 0
MONUSCO 2018 Kananga -5.9000000 22.416667 300 0 DRC COD 175
MONUSCO 2018 Kanyabayonga -0.7111110 29.173056 150 0 DRC COD 150
UNAMID 2018 Kas 12.5000000 24.283333 150 0 Sudan SDN 150
UNFICYP 2018 Kato Pyrgos 35.1782400 32.684920 0 0 Cyprus CYP 0
MONUSCO 2018 Kavumu -2.3027780 28.816111 625 0 DRC COD 569
UNAMID 2018 Khor Abeche 12.6602080 25.337603 300 1 Sudan SDN 262
MINUSMA 2018 Kidal 18.4411100 1.407780 3135 2 Mali MLI 3163
MONUSCO 2018 Kigali -1.9546000 30.061000 0 0 Rwanda RWA 0
MONUSCO 2018 Kinshasa -4.3297220 15.315000 1400 3 DRC COD 1200
MONUSCO 2018 Kisangani 0.5000000 25.200000 150 2 DRC COD 150
MONUSCO 2018 Kiwanja -1.1400000 29.439445 650 0 DRC COD 483
MONUSCO 2018 Komanda 1.3623420 29.776738 150 0 DRC COD 150
MONUSCO 2018 Kongolo -5.3833330 27.000000 35 0 DRC COD 35
UNAMID 2018 Korma 13.8333333 24.733333 185 0 Sudan SDN 185
UNMIK 2018 Kosovska Mitrovica 42.8833300 20.866670 0 2 Kosovo XKO 0
UNMISS 2018 Kuacjok 8.3040400 27.993269 150 0 South Sudan SSD 150
UNAMID 2018 Kutum 14.2000000 24.666667 300 0 Sudan SDN 300
MINURSO 2018 Laayoune 27.1350000 -13.162500 35 3 Western Sahara MAR 35
UNAMID 2018 Labado 12.0908920 25.410895 300 0 Sudan SDN 300
UNMISS 2018 Leer 8.3017900 30.141790 150 0 South Sudan SSD 150
UNFICYP 2018 Leonarisso 35.4689200 34.138860 0 0 Cyprus CYP 0
MINUJUSTH 2018 Les Cayes 18.1933100 -73.746010 0 0 Haiti HTI 0
UNFICYP 2018 Linou 35.0785600 32.904700 0 0 Cyprus CYP 0
MONUSCO 2018 Lubumbashi -11.6666670 27.466667 150 2 DRC COD 150
MINUSMA 2018 Ménaka 15.9182000 2.402200 450 0 Mali MLI 450
UNISFA 2018 Madingthon 9.6013440 28.431379 70 0 Sudan SDN 70
MINURSO 2018 Mahbas 27.4242670 -9.065980 0 0 Western Sahara MAR 0
UNMISS 2018 Malakal 9.5334240 31.660485 2050 2 South Sudan SSD 1930
MONUSCO 2018 Manono -7.3000000 27.416667 150 0 DRC COD 150
UNISFA 2018 Marial Achak 9.4793280 28.624917 150 0 Sudan SDN 150
UNIFIL 2018 Maritime Task Force 33.1216998 35.134000 NA 0 Lebanon LBN NaN
UNAMID 2018 Masteri 13.1166667 22.150000 150 0 Sudan SDN 150
MONUSCO 2018 Mavivi 0.5861170 29.484516 335 1 DRC COD 902
MONUSCO 2018 Mayimoya 0.7651220 29.569722 150 0 DRC COD 150
MONUSCO 2018 Mbuji-Mayi -6.1500000 23.600000 150 0 DRC COD 150
MINURSO 2018 Mehaires 26.1472000 -11.069200 0 0 Western Sahara MAR 0
UNMISS 2018 Melut 10.4417950 32.200806 150 0 South Sudan SSD 150
UNAMID 2018 Menawashi 12.6666700 24.983330 150 0 Sudan SDN 150
MINURSO 2018 Mijek 23.4735871 -12.759754 0 0 Western Sahara MAR 0
MINUJUSTH 2018 Miragoâne 18.4459900 -73.089570 NA 0 Haiti HTI NaN
MONUSCO 2018 Moba -7.0666670 29.766667 35 0 DRC COD 35
MONUSCO 2018 Monigi Camp -1.6349200 29.248316 300 0 DRC COD 300
UNMIL 2018 Monrovia 6.3105560 -10.804722 150 0 Liberia LBR 150
MINUSMA 2018 Mopti/Sevare 14.5274200 -4.093440 950 0 Mali MLI 950
UNAMID 2018 Mournei 12.9500000 22.866667 150 0 Sudan SDN 150
UNDOF 2018 Mt Hermon Base 33.3597330 35.820466 150 0 Syria SYR 173
UNAMID 2018 Mukhjar 11.9571480 23.269138 300 0 Sudan SDN 300
MONUSCO 2018 Mushake -1.5281580 28.979520 150 0 DRC COD 150
UNIFIL 2018 Naqoura 33.1117970 35.125602 2200 3 Lebanon LBN 2200
UNMISS 2018 Nasser 8.6000000 33.066667 150 0 South Sudan SSD 150
MINUSCA 2018 Ndélé 8.4110470 20.647584 0 0 Central African Republic CAF 0
MONUSCO 2018 Ndromo -1.3595120 28.733120 650 0 DRC COD 650
UNAMID 2018 Nertiti 12.9632500 24.047643 950 0 Sudan SDN 950
UNFICYP 2018 Nicosia 35.1694400 33.360810 150 2 Cyprus CYP 150
UNISFA 2018 Noong 9.7033280 28.452392 300 0 Sudan SDN 300
UNAMID 2018 Nyala 12.0500000 24.883333 970 2 Sudan SDN 869
MINUSCA 2018 Obo 5.3956700 26.491755 0 0 Central African Republic CAF 0
MINURSO 2018 Oum Dreyga 24.1666670 -13.250000 0 0 Western Sahara MAR 0
MINUSCA 2018 Paoua 7.2473780 16.434391 0 0 Central African Republic CAF 0
UNMISS 2018 Pariang 9.9154200 29.981090 150 0 South Sudan SSD 150
UNMISS 2018 Pibor 6.7985290 33.130445 150 0 South Sudan SSD 150
MINUJUSTH 2018 Port-au-Prince 18.5434900 -72.338810 NA 0 Haiti HTI NaN
MINUJUSTH 2018 Port-de-Paix 19.9398400 -72.830370 0 0 Haiti HTI 0
UNIFIL 2018 Position 1-0A 33.1083330 35.209167 300 0 Lebanon LBN 300
UNIFIL 2018 Position 1-26 33.1695210 35.185870 150 1 Lebanon LBN 150
UNIFIL 2018 Position 2-1 33.2576090 35.312757 450 1 Lebanon LBN 450
UNIFIL 2018 Position 2-3 33.1521074 35.197265 1250 2 Lebanon LBN 1250
UNIFIL 2018 Position 2-31 33.1559650 35.196764 150 0 Lebanon LBN 150
UNIFIL 2018 Position 2-45 33.1291670 35.391389 600 1 Lebanon LBN 600
UNIFIL 2018 Position 2-5 33.2776120 35.249281 600 1 Lebanon LBN 600
UNIFIL 2018 Position 4-2 33.3688890 35.623056 150 1 Lebanon LBN 150
UNIFIL 2018 Position 4-28 33.2783330 35.636111 150 0 Lebanon LBN 150
UNIFIL 2018 Position 4-3 33.3770720 35.640359 300 0 Lebanon LBN 300
UNIFIL 2018 Position 4-30 33.3265710 35.641810 150 0 Lebanon LBN 150
UNIFIL 2018 Position 4-31 33.3115033 35.705589 150 0 Lebanon LBN 150
UNIFIL 2018 Position 4-7C 33.3294440 35.736667 150 0 Lebanon LBN 150
UNIFIL 2018 Position 5-10 33.1838310 35.231681 150 0 Lebanon LBN 150
UNIFIL 2018 Position 5-20 33.0898060 35.370954 150 0 Lebanon LBN 150
UNIFIL 2018 Position 5-42 33.1052786 35.317299 150 0 Lebanon LBN 150
UNIFIL 2018 Position 5-66 33.1282430 35.350073 150 1 Lebanon LBN 150
UNIFIL 2018 Position 6-43 33.1910620 35.392703 150 0 Lebanon LBN 150
UNIFIL 2018 Position 7-1 33.2713890 35.491667 300 1 Lebanon LBN 300
UNIFIL 2018 Position 7-2 33.3760410 35.616939 900 2 Lebanon LBN 900
UNIFIL 2018 Position 7-3 33.3810280 35.613174 450 0 Lebanon LBN 450
UNIFIL 2018 Position 8-30 33.1836190 35.505105 150 1 Lebanon LBN 150
UNIFIL 2018 Position 8-31 33.2045920 35.485374 150 0 Lebanon LBN 150
UNIFIL 2018 Position 8-32 33.1966805 35.540474 150 0 Lebanon LBN 150
UNIFIL 2018 Position 8-33 33.2091250 35.534695 150 0 Lebanon LBN 150
UNIFIL 2018 Position 8-34 33.1628270 35.524991 150 0 Lebanon LBN 150
UNIFIL 2018 Position 8-36 33.1299700 35.492847 150 0 Lebanon LBN 150
UNDOF 2018 Position 80 32.9621450 35.883407 35 0 Syria SYR 112
UNIFIL 2018 Position 9-1 33.2638580 35.410404 450 1 Lebanon LBN 450
UNIFIL 2018 Position 9-10 33.2696800 35.416412 150 0 Lebanon LBN 150
UNIFIL 2018 Position 9-2 33.3029797 35.449903 150 0 Lebanon LBN 150
UNIFIL 2018 Position 9-63 33.2456370 35.539750 150 0 Lebanon LBN 150
UNIFIL 2018 Position 9-66 33.3021770 35.564996 150 0 Lebanon LBN 150
UNIFIL 2018 Position HIN 33.1049040 35.277992 150 0 Lebanon LBN 150
UNMIK 2018 Pristina 42.6727200 21.166880 0 3 Kosovo XKO 0
UNFICYP 2018 Pyla 35.0046900 33.693810 35 0 Cyprus CYP 35
UNMISS 2018 Renk 11.7431000 32.804900 150 0 South Sudan SSD 150
UNISFA 2018 Rumajak/Dokura 9.6789860 28.458475 300 2 Sudan SDN 300
UNMISS 2018 Rumbek 6.8019960 29.691227 650 0 South Sudan SSD 650
MONUSCO 2018 Rwindi -0.7847220 29.290556 650 0 DRC COD 483
MONUSCO 2018 Sake -1.5736110 29.045000 2750 0 DRC COD 2487
MONUSCO 2018 Sange -3.0915510 29.114144 650 0 DRC COD 650
UNAMID 2018 Saraf Omra 13.4500000 23.266667 150 0 Sudan SDN 150
MONUSCO 2018 Semuliki Bridge 0.7392390 29.789729 255 0 DRC COD 255
MONUSCO 2018 Shabunda -2.6915400 27.346250 150 0 DRC COD 150
UNAMID 2018 Shaeria 12.4426150 25.586193 NA 0 Sudan SDN NaN
UNAMID 2018 Shangil Tobay 13.0166670 25.250000 150 0 Sudan SDN 150
MINUSCA 2018 Sibut 5.7314540 19.087667 650 0 Central African Republic CAF 650
UNFICYP 2018 Skouriotissa 35.0917400 32.884140 150 2 Cyprus CYP 150
MINURSO 2018 Smara 26.7384100 -11.671940 0 0 Western Sahara MAR 0
UNAMID 2018 Sortony 13.4505400 24.397706 185 0 Sudan SDN 185
UNFICYP 2018 Strovilia 35.0940970 33.899095 0 0 Cyprus CYP 0
UNAMID 2018 Tawila 13.5000000 24.900000 185 1 Sudan SDN 185
UNISFA 2018 Tejalei 9.6736060 28.593325 300 0 Sudan SDN 300
MINUSMA 2018 Tessalit 20.2012600 1.011860 1320 0 Mali MLI 1320
MINURSO 2018 Tifariti 26.0927780 -10.608889 0 0 Western Sahara MAR 0
MINURSO 2018 Tindouf 27.6711100 -8.147430 0 0 Algeria DZA 0
UNISFA 2018 Todach 9.7347390 28.472629 520 0 Sudan SDN 520
MINUSMA 2018 Tombouctou (Timbuktu) 16.7734800 -3.007420 2370 2 Mali MLI 2370
UNMISS 2018 Torit 4.4133330 32.567778 300 0 South Sudan SSD 210
MONUSCO 2018 Tshikapa -6.4232300 20.793987 150 0 DRC COD 150
UNISFA 2018 Um Khariet 9.7781030 28.637597 150 0 Sudan SDN 150
UNAMID 2018 Umm Barru 15.0500000 23.716667 NA 0 Sudan SDN NaN
UNFICYP 2018 UNPA 35.1585509 33.270104 255 3 Cyprus CYP 255
MONUSCO 2018 Uvira -3.3730550 29.144967 650 0 DRC COD 539
MONUSCO 2018 Walungu -2.6283333 28.665833 650 0 DRC COD 483
UNMISS 2018 Wau 7.7028610 27.995300 950 2 South Sudan SSD 980
UNFICYP 2018 Xeros 35.1395200 32.834310 150 0 Cyprus CYP 150
MINUSCA 2018 Yaloke 5.3150660 17.098075 0 0 Central African Republic CAF 0
UNMISS 2018 Yambio 4.5721310 28.395488 150 0 South Sudan SSD 270
UNMISS 2018 Yei 4.0950350 30.677920 150 0 South Sudan SSD 150
UNAMID 2018 Zalingei 12.9095990 23.474061 335 2 Sudan SDN 309

Hold up – the data subset also includes other missions elsewhere in the world. We want to somehow leave out these observations. In the next step, we’re pulling the shapefiles for country outlines from the package rnaturalearth, and filter for countries in Africa. We can also use this data to extract a list of ISO3 codes for African countries and use it to filter out our main dataframe.

library(rnaturalearth)
library(rnaturalearthdata)
library(sf)

world <- ne_countries(scale = "medium", returnclass = "sf")
AFR_sf <- world %>% filter(region_un == "Africa")
AFR_list <- AFR_sf %>% pull(iso_a3)
GeoPKO2018 <- GeoPKO2018 %>% filter(iso3c %in% AFR_list)

With that set, we can start plotting the deployment locations and their respective sizes for UN peacekeeping missions in Africa in 2018.

library(ggrepel) #to nudge labels nicely away from geom_point
library(viridis) #for pretty colors
library(ggplot2) #to make plots
library(ggthemes)

  ggplot(data=AFR_sf) + geom_sf() + 
  geom_point(data = GeoPKO2018, aes(x=longitude, y=latitude, 
                                    size=YearlyAverage, color= YearlyAverage), 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 = GeoPKO2018 %>% filter(HQ==3), aes (x=longitude, y=latitude, shape="HQ"),
             fill = "red", size=2, color="red", alpha=.8)+
  scale_shape_manual(values=c(23), labels=c("HQ"="Mission HQ"), name="")+
  geom_label_repel(data = GeoPKO2018 %>% filter(HQ==3), aes(x=longitude, y=latitude, label=Mission),
                   min.segment.length = 0, 
                   direction="both",
                   label.size = 0.5,
                   box.padding = 2,
                   size = 3, 
                   fill = alpha(c("white"),0.5),
                   shape=16, 
                   size=2) +
  labs(title ="UN Peacekeeping in Africa - 2018", 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()
  )

Here is a similar visualization, but this time the color aesthetic for geom_point is mapped to shown country instead.

p3 <- ggplot(data=AFR_sf) + geom_sf() +
  geom_point(data=GeoPKO2018, 
             aes(x=longitude, y=latitude, size=YearlyAverage, color=country), alpha=.4, shape=20)+
  geom_point(data=GeoPKO2018 %>% filter(HQ==3), 
             aes(x=longitude, y=latitude), 
             color="black", shape=16, size=2) +
  geom_label_repel(data=GeoPKO2018 %>% filter(HQ==3),
    min.segment.length = 0.2,
    label.size = 0.5,
    box.padding = 2,
    size = 3,
    fill = alpha(c("white"),0.7),
    aes(x=longitude, y=latitude, label=Mission)) +
  labs(title="UN Peacekeeping Deployment and Mission HQs in Africa, 2018")+
  scale_size(range = c(2, 16))+
  labs(size="Average number of troops\n(continuous scale)",color="Country",shape="HQ")+
  theme(
    plot.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(),
    panel.background=element_blank(),
    legend.key = element_rect(fill = "#f5f5f2", color = NA),
    legend.key.size = unit(1, 'lines')
  )+
  guides(colour=guide_legend(ncol=2,override.aes = list(size=5)),
         size=guide_legend(ncol=2))
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 missions taking place in Africa in 2018. First we would calculate the average number of troops that is deployed to a location per mission per year, for every year between 1994 and 2018.

gif_df <- GeoPKO %>%   
  filter(iso3c %in% AFR_list) %>% 
  select(Mission, year, location, latitude, longitude, No.troops, HQ) %>%
  mutate_at(vars(latitude, longitude), as.numeric) %>% 
  group_by_at(vars(-No.troops)) %>%
  summarise(ave.no.troops = round(mean(as.numeric(No.troops), na.rm=TRUE)))
`summarise()` regrouping output by 'Mission', 'year', 'location', 'latitude', 'longitude' (override with `.groups` argument)

The animated graph is built on the above code for static graphics, using the cool package gganimate.

library(gganimate)

# Transforming the "year" variable into a discrete variable.
gif_df$year <- as.factor(gif_df$year)

ggplot(AFR_sf) + 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=1, face="italic"))+
  transition_states(states=year, transition_length = 3, state_length=3)+
  labs(title="UN Peacekeeping in Armed Conflicts in Africa: {closest_state}",
       caption="Source: The Geo-PKO dataset v2.0")+
  ease_aes()

#run the following command to save the plot
#anim_save("animatedUNPKO.gif", p4)

With the release of version 2.0, the Geo-PKO dataset has extended its coverage to missions taking place globally between 1994 and 2019. The above examples are but a few ways through which users can explore and exploit this rich dataset.


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

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         ggthemes_4.2.0          viridis_0.5.1          
 [4] viridisLite_0.3.0       ggrepel_0.8.2           ggplot2_3.3.2          
 [7] sf_0.9-5                rnaturalearthdata_0.1.0 rnaturalearth_0.1.0    
[10] kableExtra_1.1.0        knitr_1.29              readr_1.3.1            
[13] tidyr_1.1.1             dplyr_1.0.2             workflowr_1.6.2        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         lattice_0.20-41    prettyunits_1.1.1  class_7.3-17      
 [5] rprojroot_1.3-2    digest_0.6.25      plyr_1.8.6         R6_2.4.1          
 [9] backports_1.1.7    evaluate_0.14      e1071_1.7-3        httr_1.4.2        
[13] highr_0.8          pillar_1.4.6       progress_1.2.2     rlang_0.4.7       
[17] gifski_0.8.6       rstudioapi_0.11    whisker_0.4        rmarkdown_2.3     
[21] labeling_0.3       webshot_0.5.2      stringr_1.4.0      munsell_0.5.0     
[25] compiler_4.0.2     httpuv_1.5.4       xfun_0.16          pkgconfig_2.0.3   
[29] rgeos_0.5-3        htmltools_0.5.0    tidyselect_1.1.0   tibble_3.0.3      
[33] gridExtra_2.3      crayon_1.3.4       withr_2.2.0        later_1.1.0.1     
[37] grid_4.0.2         gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0         
[41] git2r_0.27.1       magrittr_1.5       units_0.6-7        scales_1.1.1      
[45] KernSmooth_2.23-17 stringi_1.4.6      farver_2.0.3       fs_1.5.0          
[49] promises_1.1.1     sp_1.4-2           xml2_1.3.2         ellipsis_0.3.1    
[53] generics_0.0.2     vctrs_0.3.2        tools_4.0.2        glue_1.4.1        
[57] tweenr_1.0.1       purrr_0.3.4        hms_0.5.3          yaml_2.2.1        
[61] colorspace_1.4-1   classInt_0.4-3     rvest_0.3.6