Last updated: 2020-12-03
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Knit directory: GeoPKO/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | eafa002 | Nguyen Ha | 2020-12-03 | adapting to new dataset version |
| Rmd | 5502ba6 | Lou van Roozendaal | 2020-11-26 | UpdateUCDP |
| 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 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.
Load packages.
library(dplyr)
library(sp)
library(tidyr)
library(geojsonio)
library(broom)
library(rgdal)
library(ggplot2)
library(leaflet)
library(sf)
library(spdep)
library(maptools)
library(plyr)
library(rjson)
library(RJSONIO)
library(rmapshaper)
library(htmltools)
library(htmlwidgets)First, we import the datasets. We’re using the published Geo-PKO dataset, and conflict forecast data from ViEWS for state-based conflict, non-state conflict, and one-sided violence over the next 36 months in Africa. Both datasets offer insights into the sub-national level, using a unique “PRIO-grid” identifier. The PRIO-grid is 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_v2.0.csv") #replace file laterParsed with column specification:
cols(
.default = col_double(),
source = col_character(),
mission = col_character(),
joined_date = col_character(),
timepoint = col_character(),
location = col_character(),
country = col_character(),
old_xy = col_logical(),
geocomment = col_character(),
comment.on.unit = col_character(),
rpf = col_logical(),
rpf.no = col_logical(),
obs.base = col_logical(),
cantonment = col_logical(),
disarmament = col_logical(),
he.sup.lw = col_logical(),
troop.type = col_character(),
nameoftcc_1 = col_character(),
notroopspertcc_1 = col_character(),
nameoftcc_2 = col_character(),
nameoftcc_3 = col_character()
# ... with 43 more columns
)See spec(...) for full column specifications.Warning: 4400 parsing failures.
row col expected actual file
1032 notroopspertcc_4 a double unknown 'data/Geo_PKO_v2.0.csv'
1035 nameoftcc_6 1/0/T/F/TRUE/FALSE Pakistan 'data/Geo_PKO_v2.0.csv'
1035 notroopspertcc_6 1/0/T/F/TRUE/FALSE 150 'data/Geo_PKO_v2.0.csv'
1035 nameoftcc_7 1/0/T/F/TRUE/FALSE Peru 'data/Geo_PKO_v2.0.csv'
1035 notroopspertcc_7 1/0/T/F/TRUE/FALSE 150 'data/Geo_PKO_v2.0.csv'
.... ................ .................. ........ .......................
See problems(...) for more details.#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.
library(kableExtra)
Attaching package: 'kableExtra'The following object is masked from 'package:dplyr':
group_rowskable(geopko[9546:9550,]) %>% kable_styling() %>%
scroll_box(width = "100%", height = "200px")| source | mission | joined_date | timepoint | 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 | iso3c | Month | MonthName |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Map no. 4249 Rev. 08 | UNMIS | 2007: August | 2007 August | 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 | SDN | 8 | August |
| Map no. 4249 Rev. 08 | UNMIS | 2007: August | 2007 August | 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 | SDN | 8 | August |
| Map no. 4249 Rev. 08 | UNMIS | 2007: August | 2007 August | 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 | SDN | 8 | August |
| Map no. 4249 Rev. 08 | UNMIS | 2007: August | 2007 August | 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 | SDN | 8 | August |
| Map no. 4249 Rev. 08 | UNMIS | 2007: August | 2007 August | 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 | SDN | 8 | August |
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 = "200px")| 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 |
The Geo-PKO dataset we’re working with includes data from previous years, but for this visualisation, we will only use the latest year (July 2019 - June 2020). First, we need to filter the data to include only that period. This way we’ll be looking at deployment status against projected conflict risk from the end of that period until three years from that period. 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 merge the datasets. In addition to filtering for the time period we want, we will also calculate the average number of troops deployed over that time period.
# filtering for troop deployments over Jul 2019 - Jun 2020
geopko2 <- geopko %>%
select(mission, year, month, prioid, no.troops, country, location, latitude, longitude) %>%
mutate_at(vars(longitude, latitude, year, month, no.troops), as.numeric) %>%
filter(year==2019 & month>6 | year==2020 & month<7)
# calculating an average number of troops
geopko3 <- geopko2 %>%
group_by(prioid) %>%
dplyr::mutate(no.troops = mean(no.troops, na.rm=TRUE)) %>%
ungroup() %>%
filter (! duplicated(no.troops)) %>%
mutate(no.troops=round(no.troops))Here, we also calculate the average conflict risk (state-based, non-state, and one-sided) for each location.
predictors2 <- predictors %>%
group_by(pg_id) %>%
dplyr::mutate(average_allwthematic_sb = mean(average_allwthematic_sb, na.rm=TRUE)) %>%
dplyr::mutate(average_allwthematic_ns = mean(average_allwthematic_ns, na.rm=TRUE)) %>%
dplyr::mutate(average_allwthematic_os = mean(average_allwthematic_os, na.rm=TRUE)) %>%
filter (! duplicated(average_allwthematic_sb))Finally, we merge the two datasets.
# merging geopko with conflict forecast data
geopko3$pg_id <- geopko3$prioid
priogriddf <- full_join(
geopko3, predictors2,
by = c("pg_id"),
na.rm = TRUE)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:
shapefile <- rgdal::readOGR("data/ViEWS/priogrid.geojson")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 fieldsThe shapefile contains both geospatial polygon data and numerical data that corresponds to the ViEWS dataset; specifically, a PRIO-grid ID and a country ID. Here’s what the non-spatial data looks like, showing five rows in the dataset.
kable(shapefile@data[101:106,]) %>% kable_styling() %>%
scroll_box(width = "100%", height = "200px")| x |
|---|
| 54217 |
| 54218 |
| 54219 |
| 54220 |
| 54221 |
| 54222 |
To work with the data within this shapefile, we need to fortify the shapefile. 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$id <- rownames(shapefile@data)
shapefile.df <- fortify(shapefile, region = "id")
# merge data of interest
shapefile.df <- merge(shapefile, priogriddf, by.x = "pg_id", by.y = "pg_id", all.x=F, all.y=T, duplicateGeoms=TRUE)So now shapefile.df has the new attributes, including variables from both Geo-PKO and ViEWS. id is a variable that ties the ‘polygon’, or a single square on the grid, to its location on the map
kable(shapefile.df@data[356:360,]) %>% kable_styling() %>%
scroll_box(width = "100%", height = "200px")| pg_id | id | mission | year | month | prioid | no.troops | country | location | latitude | longitude | month_id | average_allwthematic_sb | average_allwthematic_ns | average_allwthematic_os | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 356 | 89685 | 3245 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 486 | 0.0024289 | 0.0015213 | 0.0013923 |
| 357 | 89686 | 3246 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 486 | 0.0029618 | 0.0021277 | 0.0020474 |
| 358 | 89687 | 3247 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 486 | 0.0026179 | 0.0018690 | 0.0020587 |
| 359 | 89688 | 3248 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 486 | 0.0023497 | 0.0017310 | 0.0018626 |
| 360 | 89689 | 3249 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 486 | 0.0023957 | 0.0016043 | 0.0016756 |
To map the data, we’re going to use the leaflet package (and a bunch of others to support it). The first thing we do is set up our colour palette and bins. We’re using the ‘viridis’ colour palette, designed for accessibility and continuous-scale representation. The other thing we include is a small segment of code that fixes spacing between any NA value in the legend, and the remainder of the legend, making it easier to see.
bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal <- colorNumeric("viridis", NULL)
#to fix spacing of NA in legend
css_fix <- "div.info.legend.leaflet-control br {clear: both;}" # CSS to correct spacing
html_fix <- htmltools::tags$style(type = "text/css", css_fix) # Convert CSS to HTMLNext, let’s map. We include three colour layers to shade squares according to their conflict forecast value. These layers cover state-based conflict, non-state conflict, and one-sided violence. Simple markers show where troops are deployed. Troop deployment numbers are included as labels, which you can see for each square on hover.
map <- leaflet(shapefile.df) %>%
addTiles() %>%
addPolygons(color = "#444444", weight = 0.25, smoothFactor = 0.5,
opacity = 0.05, fillOpacity = 0.4,
fillColor = ~pal(shapefile.df$average_allwthematic_sb),
group = "State-Based Conflict",
highlightOptions = highlightOptions(color = "white", weight = 2,
bringToFront = FALSE)) %>%
addPolygons(color = "#444444", weight = 0.25, smoothFactor = 0.5,
opacity = 0.05, fillOpacity = 0.4,
fillColor = ~pal(shapefile.df$average_allwthematic_ns),
group = "Non-State 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$average_allwthematic_os),
group = "One-Sided Violence",
highlightOptions = highlightOptions(color = "white", weight = 2,
bringToFront = FALSE)) %>%
addCircleMarkers((data = shapefile.df@data$no.troops>0), lat = ~latitude, lng = ~longitude,
weight = 1, radius = 2, fillOpacity = 0.6, color = "darkblue") %>%
addPolygons(color = "#444444", weight = 0.1, smoothFactor = 0.5,
opacity = 0.0, fillOpacity = 0.0,
fillColor = ~pal(shapefile.df$no.troops),
label=paste("Troops Deployed: ", shapefile.df$No.troops),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", color="blue"),
textsize = "15px", direction = "auto"),
highlightOptions = highlightOptions(color = "white", weight = 2,
bringToFront = FALSE)) %>%
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"),
options = layersControlOptions(collapsed = FALSE)
)Warning in validateCoords(lng, lat, funcName): Data contains 10630 rows with
either missing or invalid lat/lon values and will be ignoredmap <- map %>% htmlwidgets::prependContent(html_fix) # legend NA fix
# to save as HTML, you can use the following code:
# saveWidget(map, file="pkoviews - priogrid.html")
mapAnd here we have it: an interactive map to view recent peacekeeping deployments (2019-2020) and projected conflict risk over the next 36 months. 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 might be a project for the future.
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. How to merge with this dataset, and a few examples, will be added soon. We start with importing “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")Parsed with column specification:
cols(
year = col_double(),
longitude = col_double(),
latitude = col_double(),
where_coordinates = col_character(),
best = col_double(),
deaths_civilians = col_double()
)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 |
For the GeoPKO data we only used 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”(see UCDP codebook), and “deaths_civilians”, which tells us how many of this “best” variable were civilian deaths.
# Preparing the GeoPKO 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 GeoPKO is mapped with viridis, which exists out of 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$best, bins = c(1,50,100,500,1000,2000,4000,10000,Inf))Just as we used leaflet for the VIEWS map, we do the same here. Note that in this case we did not merge the two datasets into one dataframe. When hovering over the circles more information on either the UN peacekeeping deployment will be provided or on the UCDP conflict-related deaths. It is possible to use the PRIO-grid ID instead, both datasets include these.
UCDP_Overview_Map <- leaflet() %>%
addTiles(options = providerTileOptions(noWrap = TRUE)) %>%
addMeasure(position = "bottomleft")%>% #Adds a widget that can measure distances between two places to the map
clearMarkers()%>%
clearShapes()%>%
addLegend("topright",
pal = pal3,
values = UCDP$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")
#Shows the map
UCDP_Overview_MapIf you do want to combine the two datasets into one dataframe there are different ways to do so. As shown with the VIEWS data, you can merge the datasets based on certain variables using “full_join”, causing the two datasets to be combined in one file behind each other (it creates a dataframe in which the variables of one of the dataset occur next to the other), this can be utilized to the UCDP data as well. However, you can also merge datasets “under” each other, for the UCDP we wanted to show this second way of merging. Variables that are the same between the two datasets might go by different names, that is why some of the variable of the UCDP are renamed so that they are in line with the GeoPKO ones. The newly combined dataset will first show all the UCDP rows, and under these the GeoPKO rows will be “pasted”.
# Rename several variables
#UCDP_dataframeMerge = UCDP_dataframe %>% rename (location=where_coordinates)
# Merge the datasets
#Combined_UCDP_GeoPKO<- bind_rows(UCDP_dataframeMerge, GeoPKO_dataUCDP)
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] kableExtra_1.1.0 readr_1.3.1 htmlwidgets_1.5.1 htmltools_0.5.0
[5] rmapshaper_0.4.4 RJSONIO_1.3-1.4 rjson_0.2.20 plyr_1.8.6
[9] maptools_1.0-2 spdep_1.1-5 spData_0.3.8 sf_0.9-5
[13] leaflet_2.0.3 ggplot2_3.3.2 rgdal_1.5-16 broom_0.7.0
[17] geojsonio_0.9.2 tidyr_1.1.1 sp_1.4-2 dplyr_1.0.2
[21] workflowr_1.6.2
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 rvest_0.3.6 expm_0.999-5
[25] xml2_1.3.2 scales_1.1.1 classInt_0.4-3 stringr_1.4.0
[29] digest_0.6.25 foreign_0.8-80 rmarkdown_2.3 pkgconfig_2.0.3
[33] highr_0.8 jsonvalidate_1.1.0 rlang_0.4.7 rstudioapi_0.11
[37] httpcode_0.3.0 farver_2.0.3 generics_0.0.2 jsonlite_1.7.1
[41] crosstalk_1.1.0.1 gtools_3.8.2 magrittr_1.5 Matrix_1.2-18
[45] Rcpp_1.0.5 munsell_0.5.0 viridis_0.5.1 lifecycle_0.2.0
[49] stringi_1.4.6 whisker_0.4 yaml_2.2.1 MASS_7.3-52
[53] jqr_1.1.0 grid_4.0.2 gdata_2.18.0 promises_1.1.1
[57] crayon_1.3.4 deldir_0.2-3 lattice_0.20-41 splines_4.0.2
[61] geojson_0.3.4 hms_0.5.3 knitr_1.29 pillar_1.4.6
[65] boot_1.3-25 geojsonlint_0.4.0 codetools_0.2-16 LearnBayes_2.15.1
[69] crul_1.0.0 glue_1.4.1 evaluate_0.14 V8_3.2.0
[73] vctrs_0.3.2 httpuv_1.5.4 gtable_0.3.0 purrr_0.3.4
[77] xfun_0.16 e1071_1.7-3 coda_0.19-4 later_1.1.0.1
[81] viridisLite_0.3.0 class_7.3-17 tibble_3.0.3 units_0.6-7
[85] ellipsis_0.3.1