Last updated: 2020-12-14

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Knit directory: GeoPKO/

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File Version Author Date Message
Rmd 9652a69 Tanushree Rao 2020-12-14 merge page update v2 including labels only for deployments and updating text to clarify
Rmd 74c5349 Tanushree Rao 2020-12-14 merge page update v1 with troops averaged over prioid
Rmd d436446 Tanushree Rao 2020-12-09 fixing issues with old and new merge page
Rmd 6a20fd9 Lou van Roozendaal 2020-12-04 update global animated map
html c0b0ce3 Nguyen Ha 2020-12-03 Build site.
Rmd ee18491 Nguyen Ha 2020-12-03 minor edit
html a966566 Nguyen Ha 2020-12-03 Build site.
Rmd eafa002 Nguyen Ha 2020-12-03 adapting to new dataset version
Rmd 5502ba6 Lou van Roozendaal 2020-11-26 UpdateUCDP
Rmd c69de3a Tanushree Rao 2020-11-25 minor text updates + removing warning messages
Rmd 7cd68d8 Tanushree Rao 2020-11-25 minor edits, removing warning messages
html acee4ed Nguyen Ha 2020-11-25 Build site.
Rmd b6fea7a Nguyen Ha 2020-11-25 Revised viz, index, incorporated merge attempt 2.rmd
html 2011bda Nguyen Ha 2020-11-25 Build site.
Rmd 448b94c Nguyen Ha 2020-11-25 Revised viz, index, incorporated merge.rmd
Rmd ad652a3 Tanushree Rao 2020-11-24 changed ref to geopko so it opens with v1 data
Rmd 6d15379 Tanushree Rao 2020-11-24 updating merge page with new data
Rmd 2608fc0 Tanushree Rao 2020-09-10 merge file
Rmd 9174a7c Tanushree Rao 2020-09-09 adding kables
Rmd 113774e Tanushree Rao 2020-09-04 edits to data
Rmd 5afdf38 Tanushree Rao 2020-09-03 adding merge page

This page shows how to merge Geo-PKO data with conflict data and visualise the results. The examples used here are Uppsala University’s Violence Early Warning System (ViEWS) project, which forecasts conflict risk, and the Uppsala Conflict Data Programme (UCDP), one of the world’s leading sources of data on armed conflict. Merging these datasets can provide insights into the links between conflict risk and peacekeeping deployments, and help policymakers make effective peacekeeping decisions where the risk of conflict is high.

Setting up

Load packages.

library(dplyr)
library(tidyr)
library(geojsonio)
library(broom)
library(rgdal)
library(leaflet)
library(sp)
library(sf)
library(ggplot2)
library(spdep)
library(rjson)
library(RJSONIO)
library(rmapshaper)
library(htmltools)
library(htmlwidgets)
library(leaflet.providers)
library(kableExtra)

Geo-PKO x ViEWS

Our goal is to show peacekeeping deployments in 2019 and predicted conflict risks for July 2020 – six months later. It helps us visualise conflict forecasts for locations where peacekeepers are deployed and better understand the relationship between peacekeeping deployments and conflict risk.

First, we import the datasets. We’re using the published Geo-PKO dataset version 2.0, and conflict forecast data from ViEWS. The latter includes the predicted risks for state-based conflict, non-state conflict, and one-sided violence in Africa, starting from July 2020 and spanning over the next 36 months. ViEWS’ data is collected by each PRIO-grid – an innovative geospatial unit from the Peace Research Institute Oslo that divides the world into roughly 100km x 100km squares, allowing geographic analysis beyond the country level to be streamlined.

library(readr)
geopko <- readr::read_csv("data/Geo_PKO_v.2.0.csv") 
#unzip("data/ViEWS.zip", exdir="data/ViEWS")
predictors <- read.csv("data/ViEWS/ensemble_pgm.csv")

The Geo-PKO dataset includes detail on troop deployment numbers, types of troops, non-troop deployments, and contributing countries.

kable(geopko[9546:9550,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
source mission year month location geosplit country latitude longitude old_xy geocomment zone.de.confidence battalion company platoon other.size comment.on.unit no.troops rpf rpf.no inf inf.no fpu fpu.no res res.no fp fp.no eng sig trans riv he.sup sf med maint recon avia mp demining uav obs.base cantonment disarmament other.type armor he.sup.lw troop.type no.tcc nameoftcc_1 notroopspertcc_1 nameoftcc_2 notroopspertcc_2 nameoftcc_3 notroopspertcc_3 nameoftcc_4 notroopspertcc_4 nameoftcc_5 notroopspertcc_5 nameoftcc_6 notroopspertcc_6 nameoftcc_7 notroopspertcc_7 nameoftcc_8 notroopspertcc_8 nameoftcc_9 notroopspertcc_9 nameoftcc_10 notroopspertcc_10 nameoftcc_11 notroopspertcc_11 nameoftcc_12 notroopspertcc_12 nameoftcc_13 notroopspertcc_13 nameoftcc_14 notroopspertcc_14 nameoftcc_15 notroopspertcc_15 nameoftcc_16 notroopspertcc_16 nameoftcc_17 notroopspertcc_17 unpol.dummy unmo.dummy coding quality for UNMO (1=unsure; 0=perfectly fine) hq lo jmco security.group.dummy comments cow_code gnwo tcc1 tcc2 tcc3 tcc4 tcc5 tcc6 tcc7 tcc8 tcc9 tcc10 tcc11 tcc12 tcc13 tcc14 tcc15 tcc16 tcc17 adm1.id adm1.name prioid
Map no. 4249 Rev. 08 UNMIS 2007 8 Bentiu 0 Sudan 9.233333 29.83333 NA GNS has two locations pointing to basically the same place. I took the coords for “seat of first-order admin division”, rahter than “populated place”, both are in line with GE. Also, today’s south sudan NA 0 1 0 0 NA 150 NA NA 1 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA 0 0 NA 1 1 India 150 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 NA NA NA NA 0 1 0 0 0 FALSE 0 NA 625 625 750 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 622 Unity 142980
Map no. 4249 Rev. 08 UNMIS 2007 8 Bor 0 Sudan 6.205931 31.55633 NA GNS has two locations, the “seat of first-order admin division” pointing to location of Google Earth by same name, as well as corresponding to source map, hence that one coded. Also, today’s south sudan NA 0 0 0 0 NA 0 NA NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA 0 0 NA 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 NA NA NA NA NA NA 0 1 0 0 0 FALSE 0 NA 625 625 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 621 Jungoli 138664
Map no. 4249 Rev. 08 UNMIS 2007 8 Dilling 0 Sudan 12.050000 29.65000 NA NA NA 0 0 0 0 NA 0 NA NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA 0 0 NA 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 NA NA NA NA NA NA 0 1 0 0 0 FALSE 0 NA 625 625 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 720 South Kordufan 147300
Map no. 4249 Rev. 08 UNMIS 2007 8 Ed Damazin 0 Sudan 11.789100 34.35920 NA NA NA 1 4 0 0 NA 1250 NA NA 1 650 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 NA NA NA 0 0 NA 1, 2, 4, 11, 14 1 Pakistan 1250 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 NA NA NA NA 0 1 0 2 0 FALSE 0 NA 625 625 770 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 287 Blue Nile 146589
Map no. 4249 Rev. 08 UNMIS 2007 8 El Obeid 0 Sudan 13.183333 30.21667 NA NA NA 0 1 0 0 NA 150 NA NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA 1 0 NA 99 0 unknown 150 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 NA NA NA NA 0 0 NA 0 0 FALSE 0 “other” troop type refers to LOG company, unknown TCC 625 625 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 721 North Kordufan 148741

Preparing the data

We first need to filter the Geo-PKO data to include only observations for the year 2019, the latest year in version 2.0. We also remove countries outside Africa, as the ViEWS dataset only covers this continent.

Note: 2020 data is not currently available, but will be in early 2021. The dataset used for this visualisation will then be updated.

# filtering for troop deployments for the year 2019 in Africa

geopko2 <- geopko %>%
    filter(year=="2019") %>% 
  # subsetting columns so we have a smaller data frame to work with
  select(mission, prioid, no.troops, country, location, latitude, longitude) %>%
  dplyr::mutate_at(vars(longitude, latitude, no.troops), as.numeric) %>%
  # filtering out missions that did not take place in Africa
  filter(!(country %in% c("Kosovo", "Lebanon", "Haiti", "Syria", "Cyprus")))

# for a list of host countries in the Geo-PKO dataset, you can run the following:
# geopko %>% distinct(country)

We now need to calculate the average number of troops deployed over that time period. In the visualisation, we’ll use coordinates to map every single deployment locations, so the average is calculated by location. In the next step, we use this number to calculate the average number of deployed troops for each PRIO-grid square. As a PRIO-grid is quite large, one grid square may have been home to multiple locations. We won’t visualise this bit of data here, but this is useful for understanding how to synthesize datasets with different units of analysis.

# calculating an average number of troops per location

geopko2 <- geopko2 %>%
  group_by_at(vars(-no.troops)) %>%
  dplyr::summarise(no.troops.yearly.avr = round(mean(no.troops, na.rm=TRUE))) %>%
  ungroup() %>% 
# summing the troop count for each prio grid
  group_by(prioid) %>%
  dplyr::mutate(no.troops.prio = sum(no.troops.yearly.avr, na.rm=TRUE)) %>%
  ungroup()

The predictor database begins with July 2020 and forecasts the risk of conflict over the next 36 months ahead. Here’s a preview of the data within it, showing state-based (sb), non-state (ns) and one-sided violence (os) forecasts. The variable month_id codes months differently to the Geo-PKO dataset, with every month assigned a different numeric value.

kable(predictors[90545:90550,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
month_id pg_id average_allwthematic_sb average_allwthematic_ns average_allwthematic_os
90545 494 141560 0.0056672 0.0040548 0.0049985
90546 494 141561 0.0054137 0.0032145 0.0038274
90547 494 141562 0.0115811 0.0073864 0.0045633
90548 494 141563 0.0093885 0.0043294 0.0027875
90549 494 141564 0.0085510 0.0184678 0.0043437
90550 494 141565 0.0098269 0.0046215 0.0037409

You’ll see both the Geo-PKO and ViEWS datasets include the PRIO-grid identification variable (pg_id or prioid), which corresponds to a specific grid square on the map. This is what we’ll use to eventually merge the datasets. For now, we subset the ViEWS predictions to include only the month of July 2020, which gives us a lagged time period of six months between the Geo-PKO observations and ViEWS predictions.

predictors2 <- predictors %>% 
  filter(month_id==486) 

Finally, we merge the two datasets.

priogriddf <- left_join(
  predictors2, geopko2, 
  by = c("pg_id"="prioid"), 
  name="pg_id")

Merging the dataframe with the shapefile

Like we mentioned before, the PRIO-grid unit involves dividing the entire world into roughly 100km x 100km squares. That means that if we want to map it, we’ll be working with large files, so keep that in mind when you’re reading in the shapefile.

OGR data source with driver: GeoJSON 
Source: "C:\Users\Nguyen Ha\Documents\DPCR\GeoPKO\Pages\GeoPKO\data\ViEWS\priogrid.geojson", layer: "priogrid"
with 64818 features
It has 1 fields

The shapefile contains both geospatial polygon data and numerical data that corresponds to the ViEWS dataset; specifically, a PRIO-grid ID and a country ID. To work with the data within this shapefile, we need to fortify it. This converts it into a dataframe. We also convert the IDs to rownames to make it easier to work with. And, finally, we merge it with pgnewdf2, which we created earlier.

# fortify
shapefile@data$id <- rownames(shapefile@data)
shapefile.df <- fortify(shapefile, region = "id")

# merge data
shapefile.df <- merge(shapefile, priogriddf, by.x = "pg_id", by.y = "pg_id", all.x=F, all.y=T, duplicateGeoms=TRUE)

Now shapefile.df has the new attributes, including variables from both Geo-PKO and ViEWS. id is a variable that ties the ‘polygon’, or a single square on the grid, to its location on the map. As many PRIO-grid squares will have a conflict forecast index but no troop deployments, many data points under Geo-PKO variables will show NA.

kable(shapefile.df@data[5386:5500,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
pg_id id month_id average_allwthematic_sb average_allwthematic_ns average_allwthematic_os mission country location latitude longitude no.troops.yearly.avr no.troops.prio
5386 142960 16815 486 0.0038824 0.0022568 0.0037296 NA NA NA NA NA NA NA
5387 142961 16816 486 0.0041070 0.0017938 0.0021648 NA NA NA NA NA NA NA
5388 142962 16817 486 0.0026617 0.0022553 0.0032941 NA NA NA NA NA NA NA
5389 142963 16818 486 0.0022654 0.0019824 0.0028631 NA NA NA NA NA NA NA
5390 142964 16819 486 0.0027963 0.0022319 0.0036279 NA NA NA NA NA NA NA
5391 142965 16820 486 0.0026532 0.0020810 0.0031885 NA NA NA NA NA NA NA
5392 142966 16821 486 0.0026570 0.0019803 0.0040816 NA NA NA NA NA NA NA
5393 142967 16822 486 0.0030261 0.0032091 0.0036799 NA NA NA NA NA NA NA
5394 142968 16823 486 0.0048318 0.0052461 0.0050416 NA NA NA NA NA NA NA
5395 142969 16824 486 0.0244170 0.0055964 0.0072135 NA NA NA NA NA NA NA
5396 142970 16825 486 0.0055342 0.0021692 0.0030499 NA NA NA NA NA NA NA
5397 142971 16826 486 0.0032251 0.0020708 0.0021431 NA NA NA NA NA NA NA
5398 142972 16827 486 0.0034121 0.0021737 0.0023336 NA NA NA NA NA NA NA
5399 142973 16828 486 0.0043921 0.0030074 0.0027574 NA NA NA NA NA NA NA
5400 142974 16829 486 0.0054323 0.0054343 0.0032919 UNISFA Sudan Gok-machar 9.215412 26.85991 0 0
5401 142975 16830 486 0.0065361 0.0037252 0.0032160 NA NA NA NA NA NA NA
5402 142976 16831 486 0.0169208 0.0062564 0.0049979 NA NA NA NA NA NA NA
5403 142977 16832 486 0.0062144 0.0076972 0.0046881 UNISFA Sudan Athony 9.470559 28.46404 335 335
5404 142978 16833 486 0.0055708 0.0509225 0.0055099 UNISFA Sudan Agok 9.357486 28.58258 128 278
5405 142978 16833 486 0.0055708 0.0509225 0.0055099 UNISFA Sudan Marial Achak 9.479328 28.62492 150 278
5406 142979 16834 486 0.0102152 0.0405263 0.0077651 NA NA NA NA NA NA NA
5407 142980 16835 486 0.0142708 0.0064349 0.0100021 UNMISS South Sudan Bentiu 9.259905 29.80011 1493 1493
5408 142981 16836 486 0.0053903 0.0024632 0.0031937 NA NA NA NA NA NA NA
5409 142982 16837 486 0.0061301 0.0039779 0.0042585 NA NA NA NA NA NA NA
5410 142983 16838 486 0.0064783 0.0031868 0.0044326 NA NA NA NA NA NA NA
5411 142984 16839 486 0.0098993 0.0049793 0.0050087 NA NA NA NA NA NA NA
5412 142985 16840 486 0.0060069 0.0032141 0.0038324 NA NA NA NA NA NA NA
5413 142986 16841 486 0.0052141 0.0032155 0.0027096 NA NA NA NA NA NA NA
5414 142987 16842 486 0.0081636 0.0034759 0.0050795 NA NA NA NA NA NA NA
5415 142988 16843 486 0.0043634 0.0028049 0.0024548 NA NA NA NA NA NA NA
5416 142989 16844 486 0.0102931 0.0040764 0.0054326 NA NA NA NA NA NA NA
5417 142990 16845 486 0.0091394 0.0132143 0.0060761 NA NA NA NA NA NA NA
5418 142991 16846 486 0.0091114 0.0047432 0.0070673 NA NA NA NA NA NA NA
5419 142992 16847 486 0.0155308 0.0070456 0.0343647 NA NA NA NA NA NA NA
5420 142993 16848 486 0.0069197 0.0079125 0.0045316 NA NA NA NA NA NA NA
5421 142994 16849 486 0.0329898 0.0055623 0.0133976 NA NA NA NA NA NA NA
5422 142995 16850 486 0.0061751 0.0037489 0.0051516 NA NA NA NA NA NA NA
5423 142996 16851 486 0.0056568 0.0037440 0.0078758 NA NA NA NA NA NA NA
5424 142997 16852 486 0.0089381 0.0047676 0.0163093 NA NA NA NA NA NA NA
5425 142998 16853 486 0.0314606 0.0154105 0.0400417 NA NA NA NA NA NA NA
5426 142999 16854 486 0.0065077 0.0037026 0.0063653 NA NA NA NA NA NA NA
5427 143000 16855 486 0.0058690 0.0040956 0.0050626 NA NA NA NA NA NA NA
5428 143001 16856 486 0.0091558 0.0058094 0.0226096 NA NA NA NA NA NA NA
5429 143002 16857 486 0.0113627 0.0075678 0.0108579 NA NA NA NA NA NA NA
5430 143003 16858 486 0.0124609 0.0063454 0.0105985 NA NA NA NA NA NA NA
5431 143004 16859 486 0.0123421 0.0058527 0.0121990 NA NA NA NA NA NA NA
5432 143005 16860 486 0.0152787 0.0292900 0.0283692 NA NA NA NA NA NA NA
5433 143006 16861 486 0.0128581 0.0127575 0.0108854 NA NA NA NA NA NA NA
5434 143007 16862 486 0.0089875 0.0050298 0.0085515 NA NA NA NA NA NA NA
5435 143008 16863 486 0.0089865 0.0062909 0.0060385 NA NA NA NA NA NA NA
5436 143009 16864 486 0.0087704 0.0048946 0.0044014 NA NA NA NA NA NA NA
5437 143010 16865 486 0.0084145 0.0037783 0.0044391 NA NA NA NA NA NA NA
5438 143011 16866 486 0.0079217 0.0031273 0.0032630 NA NA NA NA NA NA NA
5439 143012 16867 486 0.0074002 0.0031132 0.0027221 NA NA NA NA NA NA NA
5440 143013 16868 486 0.0075829 0.0031822 0.0029905 NA NA NA NA NA NA NA
5441 143014 16869 486 0.0081350 0.0048502 0.0027781 NA NA NA NA NA NA NA
5442 143015 16870 486 0.0137394 0.0055197 0.0037260 NA NA NA NA NA NA NA
5443 143016 16871 486 0.0104873 0.0053263 0.0080376 NA NA NA NA NA NA NA
5444 143017 16872 486 0.0071454 0.0035923 0.0034135 NA NA NA NA NA NA NA
5445 143018 16873 486 0.0074592 0.0099381 0.0036531 NA NA NA NA NA NA NA
5446 143019 16874 486 0.0071459 0.0037201 0.0024416 NA NA NA NA NA NA NA
5447 143020 16875 486 0.0072631 0.0036979 0.0025004 NA NA NA NA NA NA NA
5448 143021 16876 486 0.0070133 0.0035395 0.0020214 NA NA NA NA NA NA NA
5449 143022 16877 486 0.0067475 0.0024173 0.0021152 NA NA NA NA NA NA NA
5450 143612 16951 486 0.0029173 0.0015203 0.0024311 NA NA NA NA NA NA NA
5451 143613 16952 486 0.0084545 0.0041365 0.0188877 NA NA NA NA NA NA NA
5452 143614 16953 486 0.0055442 0.0035424 0.0071654 NA NA NA NA NA NA NA
5453 143615 16954 486 0.0032308 0.0017484 0.0031580 NA NA NA NA NA NA NA
5454 143616 16955 486 0.0042212 0.0017180 0.0040975 NA NA NA NA NA NA NA
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5456 143618 16957 486 0.0042203 0.0018184 0.0044225 NA NA NA NA NA NA NA
5457 143619 16958 486 0.0040969 0.0018089 0.0033085 NA NA NA NA NA NA NA
5458 143620 16959 486 0.0031173 0.0020361 0.0032273 NA NA NA NA NA NA NA
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5460 143622 16961 486 0.0027427 0.0018215 0.0028786 NA NA NA NA NA NA NA
5461 143623 16962 486 0.0032921 0.0015830 0.0027836 NA NA NA NA NA NA NA
5462 143624 16963 486 0.0033064 0.0018881 0.0032171 NA NA NA NA NA NA NA
5463 143625 16964 486 0.0041152 0.0025124 0.0039843 NA NA NA NA NA NA NA
5464 143626 16965 486 0.0026959 0.0016769 0.0022480 NA NA NA NA NA NA NA
5465 143627 16966 486 0.0029141 0.0018863 0.0025911 NA NA NA NA NA NA NA
5466 143628 16967 486 0.0032218 0.0022249 0.0027566 NA NA NA NA NA NA NA
5467 143629 16968 486 0.0035042 0.0022265 0.0032172 NA NA NA NA NA NA NA
5468 143630 16969 486 0.0075799 0.0033944 0.0050007 NA NA NA NA NA NA NA
5469 143631 16970 486 0.0063663 0.0020426 0.0031098 NA NA NA NA NA NA NA
5470 143632 16971 486 0.0113675 0.0027509 0.0073838 NA NA NA NA NA NA NA
5471 143633 16972 486 0.0032053 0.0019689 0.0023615 NA NA NA NA NA NA NA
5472 143634 16973 486 0.0032129 0.0024418 0.0030780 NA NA NA NA NA NA NA
5473 143635 16974 486 0.0052846 0.0031546 0.0046095 NA NA NA NA NA NA NA
5474 143636 16975 486 0.0035425 0.0019640 0.0025275 NA NA NA NA NA NA NA
5475 143637 16976 486 0.0025714 0.0020597 0.0021644 NA NA NA NA NA NA NA
5476 143638 16977 486 0.0030249 0.0024913 0.0026558 NA NA NA NA NA NA NA
5477 143639 16978 486 0.0034638 0.0027427 0.0032370 NA NA NA NA NA NA NA
5478 143640 16979 486 0.0032344 0.0029057 0.0029564 NA NA NA NA NA NA NA
5479 143641 16980 486 0.0050388 0.0036084 0.0048726 NA NA NA NA NA NA NA
5480 143642 16981 486 0.0034228 0.0021162 0.0030131 NA NA NA NA NA NA NA
5481 143643 16982 486 0.0061890 0.0038615 0.0074817 NA NA NA NA NA NA NA
5482 143644 16983 486 0.0039599 0.0024352 0.0046044 NA NA NA NA NA NA NA
5483 143645 16984 486 0.0027797 0.0022436 0.0025579 NA NA NA NA NA NA NA
5484 143646 16985 486 0.0032871 0.0017466 0.0024051 NA NA NA NA NA NA NA
5485 143647 16986 486 0.0035130 0.0021294 0.0060667 NA NA NA NA NA NA NA
5486 143648 16987 486 0.0059859 0.0069254 0.0062558 NA NA NA NA NA NA NA
5487 143649 16988 486 0.0064681 0.0092315 0.0069150 NA NA NA NA NA NA NA
5488 143650 16989 486 0.0076255 0.0098509 0.0039985 NA NA NA NA NA NA NA
5489 143651 16990 486 0.0282848 0.0117731 0.0094136 NA NA NA NA NA NA NA
5490 143652 16991 486 0.0141636 0.0116765 0.0230129 NA NA NA NA NA NA NA
5491 143653 16992 486 0.0221102 0.0422745 0.0419147 NA NA NA NA NA NA NA
5492 143654 16993 486 0.0532576 0.0469015 0.0259562 NA NA NA NA NA NA NA
5493 143655 16994 486 0.0184486 0.0157384 0.0222044 NA NA NA NA NA NA NA
5494 143656 16995 486 0.0076151 0.0164903 0.0078290 NA NA NA NA NA NA NA
5495 143657 16996 486 0.0261824 0.1440699 0.0173727 NA NA NA NA NA NA NA
5496 143658 16997 486 0.0323618 0.2729190 0.0609969 NA NA NA NA NA NA NA
5497 143659 16998 486 0.0069876 0.0290492 0.0077272 NA NA NA NA NA NA NA
5498 143660 16999 486 0.0080614 0.0224487 0.0078844 NA NA NA NA NA NA NA
5499 143661 17000 486 0.0059992 0.0168699 0.0052389 NA NA NA NA NA NA NA
5500 143662 17001 486 0.0080721 0.0177159 0.0137558 NA NA NA NA NA NA NA

Mapping ViEWS data with Geo-PKO

To map the data, we’re going to use the leaflet package (and a bunch of others to support it). The first thing we do is set up our colour palette and bins. We’re using the ‘viridis’ colour palette, designed for accessibility and continuous-scale representation. The other thing we include is a small segment of code that fixes spacing between any NA value in the legend, and the remainder of the legend, making it easier to see.

bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal <- colorNumeric("viridis", NULL)

#to fix spacing of NA in legend
css_fix <- "div.info.legend.leaflet-control br {clear: both;}" # CSS to correct spacing
html_fix <- htmltools::tags$style(type = "text/css", css_fix)  # Convert CSS to HTML

Next, let’s map. We include three colour layers to shade squares according to their conflict forecast value. These layers can be toggled between state-based conflict, non-state conflict, and one-sided violence. Troop deployment numbers are included as labels, which you can see for each square on hover. Labels for specific deployment locations also show the name of the location and country, on hover over location markers.

Zoom in for more detail.

map <- leaflet(shapefile.df) %>%
  addTiles() %>%
  addPolygons(
    color = "#444444", weight = 0.25, smoothFactor = 0.5,
    opacity = 0.05, fillOpacity = 0.4,
    fillColor = ~ pal(shapefile.df@data$average_allwthematic_sb),
    group = "State-Based Conflict",
    highlightOptions = highlightOptions(
      color = "white", weight = 2,
      bringToFront = FALSE
    )
  )  %>%
  addPolygons(
    color = "#444444", weight = 0.25, smoothFactor = 0.5,
    opacity = 0.05, fillOpacity = 0.4,
    fillColor = ~ pal(shapefile.df@data$average_allwthematic_ns),
    group = "Non-State Conflict",
    highlightOptions = highlightOptions(
      color = "white", weight = 2,
      bringToFront = TRUE
    )
  ) %>%
  addPolygons(
    color = "#444444", weight = 0.25, smoothFactor = 0.5,
    opacity = 0.05, fillOpacity = 0.4,
    fillColor = ~ pal(shapefile.df@data$average_allwthematic_os),
    group = "One-Sided Violence",
    highlightOptions = highlightOptions(
      color = "white", weight = 2,
      bringToFront = TRUE
    )
  ) %>%
  # these circle markers use geopko3 data to cover multiple locations per prioid
  addCircleMarkers(data = geopko2,
    lat = ~latitude, lng = ~longitude,
    weight = 1, radius = 2, fillOpacity = 0.6, color = "darkblue",
    group = "Deployment Locations",
    label = paste0(
      "<strong>Deployment Location: </strong>", geopko2$mission,
      "<br/><strong>Number of Troops: </strong>", geopko2$no.troops.yearly.avr,
      "<br/><strong>Location: </strong>", geopko2$location,", ", geopko2$country
    )
  %>%
  lapply(htmltools::HTML) 
  ) %>%
  addLegend("bottomright",
    pal = pal,
    values = shapefile.df$average_allwthematic_sb,
    title = "Conflict Forecast",
    opacity = 1
  ) %>%
  addLayersControl(
    baseGroups = c("State-Based Conflict", "Non-State Conflict", "One-Sided Violence"),
    overlayGroups = c("Deployment Locations"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>% 
  htmlwidgets::prependContent(html_fix) # legend NA fix

map

And here we have it: an interactive map to view the latest peacekeeping data and projected conflict risk over the following 36 months. You can zoom in to areas of higher conflict risk to see more detail, including deployment locations. Note that some marked locations still say 0 for troop count; this indicates a mission site where no troops are deployed, but support personnel such as UNPOL or UNMO are present.

We can also do the same but with a world terrain basemap to identify, at a basic level, potential geographic impacts such as the greater concentration of conflict risk in mountainous areas, and the general deployment of peacekeepers in non-mountainous areas.

# adding provider tiles - replaces addTiles()
map <- map %>% addProviderTiles("Esri.WorldTerrain")

map

Extensions of this visualisation can be even more useful, particularly with a time-slider that can help us identify how the risk of conflict changes given peacekeeping deployments (and vice versa). That would be a valuable be a project for the future.

Geo-PKO x UCDP

Another useful dataset is from the Uppsala Conflict Data Programme, which offers insights into deaths from armed conflict. Looking at this data in conjunction with peacekeeping data can be useful to draw conclusions into peacekeeping given the severity of armed conflict, or lack thereof. We start by importing the “UCDP Georeferenced Event Dataset (GED) Global version 20.1”, of which a small excerpt of 2011 is shown in the table below.

UCDP <- read_csv("data/UCDP/ged201.csv")
kable(UCDP[9546:9550,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "200px")
year longitude latitude where_coordinates best deaths_civilians
2019 68.23228 33.60806 Dara Qayaq river 2 0
2019 63.11753 31.91940 Rakin village 1 0
2019 65.87257 36.30936 Khanaqa village 7 0
2019 69.42800 37.41980 Qara Tapa village 17 0
2019 70.21723 33.46187 Babrak Tana ruin 6 6

Preparing the data

For the Geo-PKO data we only use certain variables, and calculate the average troop number by year. The UCDP data has different variables you could use. In this example we took best, which indicates “the best (most likely) estimate of total fatalities resulting from integer an event” and deaths_civilians, which tells us how many of this best variable were civilian deaths.

# Preparing the Geo-PKO dataset
GeoPKO_dataUCDP <- geopko %>%
  select(mission, year, location, latitude, longitude, no.troops) %>% # Select only the variables you need
  mutate_at(vars(latitude, longitude, no.troops), as.numeric) %>%
  group_by(mission, year, location) %>%
  mutate(ave.no.troops = as.integer(mean(no.troops, na.rm = TRUE))) %>% # Sum the troop numbers by year through using a combination of group_by & mutate
  select(-no.troops) %>% # Deselect the previous troop number variable
  distinct() %>% # Delete any duplicate rows
  drop_na(ave.no.troops) %>% # Remove NAs from the average troop count
  filter(ave.no.troops > 0) # Exclude any troop numbers under the value of 0

# Preparing the UCDP dataset
UCDP_dataframe <- UCDP %>%
  select(year, longitude, latitude, where_coordinates, best, deaths_civilians) %>%
  drop_na(latitude, longitude) %>%
  group_by(year, where_coordinates) %>%
  mutate(best = as.integer(mean(best, na.rm = TRUE))) %>% # Take the mean of the "best" variable
  mutate(deaths_civilians = as.integer(mean(deaths_civilians, na.rm = TRUE))) %>% # Take the mean of the "deaths_civilians" variable
  filter(best > 0 & year >= 1995) %>% # Filter that data so that it only keeps rows for the years after 1995.
  distinct()

Setting the aesthetics

You can either set all the colours by hand, as seen in pal3, or use the “viridis” package to create a colour scale for you, as shown in pal2. For the UCDP data we used shades of red. The Geo-PKO data is mapped with viridis, which includes blue, green, and yellow.

pal2 <- colorBin((viridis::viridis(10)), GeoPKO_dataUCDP$ave.no.troops, bins = c(1, 50, 100, 500, 1000, 2000, 4000, 8000))
pal3 <- colorBin(c("#700524", "#8d072e", "#981f42", "#ed4d3a", "#af516c", "#d19bab", "#dcb4c0"),
  UCDP_dataframe$best,
  bins = c(1, 50, 100, 500, 1000, 2000, 4000, 10000, Inf)
)

Mapping UCDP data with Geo-PKO

Just as we used leaflet for the ViEWS map, we do the same here. Note that in this case we did not merge the two datasets into one dataframe. When hovering over the circles, more information will be provided on either the UN peacekeeping deployment or on the UCDP conflict-related deaths. It is possible to use the PRIO-grid ID instead, and both datasets include this variable.

leaflet() %>%
  addTiles(options = providerTileOptions(noWrap = TRUE)) %>%
  addMeasure(position = "bottomleft", primaryLengthUnit = "kilometers") %>% # Adds a widget that can measure distances between two places to the map
  clearMarkers() %>%
  clearShapes() %>%
  addLegend("topright",
    pal = pal3,
    values = UCDP$best,
    title = "Fatalities",
    opacity = 1
  ) %>%
  addLegend("topright",
    pal = pal2,
    values = GeoPKO_dataUCDP$ave.no.troops,
    title = "Peacekeepers",
    opacity = 1
  ) %>%
  addLayersControl(
    baseGroups = c("2019", "2018", "2015", "2010", "2005", "2000", "1995"),
    options = layersControlOptions(collapsed = FALSE), position = "topleft"
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2019 <- GeoPKO_dataUCDP %>% filter(year == 2019)),
    color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2019",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2019$mission,
      "<br/><strong>Location:</strong>", GeoPKO2019$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2019$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2019 <- UCDP_dataframe %>% filter(year == 2019)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2019",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2019$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2019$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2019$deaths_civilians
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2018 <- GeoPKO_dataUCDP %>% filter(year == 2018)),
    color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2018",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2018$mission,
      "<br/><strong>Location:</strong>", GeoPKO2018$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2018$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2018 <- UCDP_dataframe %>% filter(year == 2018)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2018",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2018$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2018$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2018$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2015 <- GeoPKO_dataUCDP %>% filter(year == 2015)),
    color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2015",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2015$mission,
      "<br/><strong>Location:</strong>", GeoPKO2015$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2015$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2015 <- UCDP_dataframe %>% filter(year == 2015)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2015",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2015$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2015$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2015$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2010 <- GeoPKO_dataUCDP %>% filter(year == 2010)),
    color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2010",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2010$mission,
      "<br/><strong>Location:</strong>", GeoPKO2010$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2010$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2010 <- UCDP_dataframe %>% filter(year == 2010)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2010",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2010$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2010$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2010$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2005 <- GeoPKO_dataUCDP %>% filter(year == 2005)),
    color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2005",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2005$mission,
      "<br/><strong>Location:</strong>", GeoPKO2005$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2005$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2005 <- UCDP_dataframe %>% filter(year == 2005)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2005",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2005$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2005$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2005$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO2000 <- GeoPKO_dataUCDP %>% filter(year == 2000)), color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    opacity = 0.15, fillOpacity = 0.5,
    lng = ~longitude, lat = ~latitude, group = "2000",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO2000$mission,
      "<br/><strong>Location:</strong>", GeoPKO2000$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO2000$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP2000 <- UCDP_dataframe %>% filter(year == 2000)),
    lng = ~longitude, lat = ~latitude,
    color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "2000",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP2000$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP2000$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP2000$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (GeoPKO1995 <- GeoPKO_dataUCDP %>% filter(year == 1995)), color = ~ pal2(ave.no.troops), radius = ~ (ave.no.troops)^(1 / 3),
    lng = ~longitude, lat = ~latitude,
    opacity = 0.15, fillOpacity = 0.5,
    group = "1995",
    label = paste(
      "<strong>UN Peacekeeping Site<br/>Mission:</strong>", GeoPKO1995$mission,
      "<br/><strong>Location:</strong>", GeoPKO1995$location,
      "<br/><strong>Troops Deployed:</strong>", GeoPKO1995$ave.no.troops
    ) %>%
      lapply(htmltools::HTML)
  ) %>%
  addCircleMarkers(
    data = (UCDP1995 <- UCDP_dataframe %>% filter(year == 1995)),
    lng = ~longitude, lat = ~latitude, color = ~ pal3(best), radius = ~ (best)^(1 / 3),
    opacity = 0.05, fillOpacity = 0.4,
    group = "1995",
    label = paste(
      "<strong>UCDP Reported Fatalities<br/>Location:</strong>", UCDP1995$where_coordinates,
      "<br/><strong>Total deaths:</strong>", UCDP1995$best,
      "<br/><strong>Civilian deaths:</strong>", UCDP1995$deaths_civilians
    ) %>% lapply(htmltools::HTML)
  )
# to save as HTML, you can use the following code:
# saveWidget(UCDP_Overview_Map, file="geopko&ucdp - geopko.html")

And here we have it again: an interactive map to view peacekeeping deployments, in a few selected years, and conflict-related deaths within that same year. Select different years to see how both the numbers and locations of both datasets have changed over time.


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] readr_1.3.1             kableExtra_1.1.0        leaflet.providers_1.9.0
 [4] htmlwidgets_1.5.1       htmltools_0.5.0         rmapshaper_0.4.4       
 [7] RJSONIO_1.3-1.4         rjson_0.2.20            spdep_1.1-5            
[10] spData_0.3.8            ggplot2_3.3.2           sf_0.9-5               
[13] leaflet_2.0.3           rgdal_1.5-16            sp_1.4-2               
[16] broom_0.7.0             geojsonio_0.9.2         tidyr_1.1.1            
[19] dplyr_1.0.2            

loaded via a namespace (and not attached):
 [1] nlme_3.1-148       fs_1.5.0           RColorBrewer_1.1-2 webshot_0.5.2     
 [5] httr_1.4.2         gmodels_2.18.1     rprojroot_1.3-2    tools_4.0.2       
 [9] backports_1.1.7    R6_2.4.1           KernSmooth_2.23-17 rgeos_0.5-3       
[13] DBI_1.1.0          lazyeval_0.2.2     colorspace_1.4-1   raster_3.3-13     
[17] withr_2.2.0        gridExtra_2.3      tidyselect_1.1.0   curl_4.3          
[21] compiler_4.0.2     git2r_0.27.1       cli_2.0.2          rvest_0.3.6       
[25] expm_0.999-5       xml2_1.3.2         scales_1.1.1       classInt_0.4-3    
[29] stringr_1.4.0      digest_0.6.25      foreign_0.8-80     rmarkdown_2.3     
[33] pkgconfig_2.0.3    highr_0.8          jsonvalidate_1.1.0 rlang_0.4.7       
[37] rstudioapi_0.11    httpcode_0.3.0     farver_2.0.3       generics_0.0.2    
[41] jsonlite_1.7.1     crosstalk_1.1.0.1  gtools_3.8.2       magrittr_1.5      
[45] Matrix_1.2-18      fansi_0.4.1        Rcpp_1.0.5         munsell_0.5.0     
[49] viridis_0.5.1      lifecycle_0.2.0    stringi_1.4.6      whisker_0.4       
[53] yaml_2.2.1         MASS_7.3-52        jqr_1.1.0          grid_4.0.2        
[57] maptools_1.0-2     gdata_2.18.0       promises_1.1.1     crayon_1.3.4      
[61] deldir_0.2-3       lattice_0.20-41    splines_4.0.2      geojson_0.3.4     
[65] hms_0.5.3          knitr_1.29         pillar_1.4.6       boot_1.3-25       
[69] geojsonlint_0.4.0  codetools_0.2-16   LearnBayes_2.15.1  crul_1.0.0        
[73] glue_1.4.1         evaluate_0.14      V8_3.2.0           vctrs_0.3.2       
[77] httpuv_1.5.4       gtable_0.3.0       purrr_0.3.4        assertthat_0.2.1  
[81] xfun_0.16          e1071_1.7-3        coda_0.19-4        later_1.1.0.1     
[85] viridisLite_0.3.0  class_7.3-17       tibble_3.0.3       workflowr_1.6.2   
[89] units_0.6-7        ellipsis_0.3.1