Last updated: 2020-08-13

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Knit directory: GeoPKO/

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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. Below are some examples of visualization.

First, we can produce a quick snapshot of the deployment of all the missions that were active in 2018. 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.

library(tidyverse)
library(readr)
library(ggthemes)
library(knitr)
library(kableExtra)

GeoPKO <- read_csv("data/geopko.csv",  
                   col_types = cols(.default="c")) #importing the dataset

GeoPKO$No.troops <- as.numeric(GeoPKO$No.troops) #changing the variable class for the number of troops
GeoPKO$latitude <- as.numeric(GeoPKO$latitude)
GeoPKO$longitude <- as.numeric(GeoPKO$longitude)

map2018df <- GeoPKO %>% filter(year==2018) %>%  
  select(Mission, year, location, latitude, longitude, No.troops, HQ, country) %>% #generating dataframe for 2018
  group_by(location, Mission, latitude, longitude) %>% 
  mutate(ave = mean(No.troops, na.rm=TRUE)) %>% select(-No.troops) %>% 
  group_by(location, Mission) %>%
  arrange(desc(HQ)) %>% slice(1)

kable(map2018df[90:95,], caption = "A preview of this dataframe") %>% kable_styling()
A preview of this dataframe
Mission year location latitude longitude HQ country ave
UNISFA 2018 Madingthon 9.601344 28.43138 0 Sudan 70
MINURSO 2018 Mahbas 27.424267 -9.06598 0 Western Sahara 0
UNMISS 2018 Malakal 9.533424 31.66049 2 South Sudan 1930
MONUSCO 2018 Manono -7.300000 27.41667 0 DRC 150
UNISFA 2018 Marial Achak 9.479328 28.62492 0 Sudan 150
UNAMID 2018 Masteri 13.116667 22.15000 0 Sudan 150

Next, we obtain the geometric shapes from the package 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")

Creating a prototype map showing size of deployment in 2018.

library(ggrepel)
library(viridis)

p2 <-  ggplot(data=Africa) + geom_sf() + 
  geom_point(data = map2018df, 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 = map2018df %>% 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 = map2018df %>% filter(HQ==3), aes(x=longitude, y=latitude, label=Mission),
                   min.segment.length = 0.2, label.size = 0.5,
                  box.padding = 2,
                  size = 3, 
                  fill = alpha(c("white"),0.7),
                  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()
  )

p2

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

p3 <- ggplot(data=Africa) + geom_sf() +
    geom_point(data=map2018df, 
               aes(x=longitude, y=latitude, size=ave, color=country), alpha=.4, shape=20)+
    geom_point(data=map2018df %>% 
                   filter(HQ==3), 
               aes(x=longitude, y=latitude), color="black", shape=16, size=2
    ) +
    geom_label_repel(
        data=map2018df %>% 
            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 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 %>% 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)

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(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)

Single-mission visualization

Placeholder.


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_3.3.2          
[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_0.5.0   
[33] dbplyr_1.4.2       highr_0.8          rlang_0.4.7        readxl_1.3.1      
[37] rstudioapi_0.11    generics_0.0.2     farver_2.0.3       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_0.3.1