war_visulization_moran_I

#import the moran's I package:
library(ape)

Data source

The conflict events are sourced from the ‘Armed Conflict Location & Event Data Project’ (ACLED) which records the latitude and longitude, time, actors, and casualty numbers of violent events [4]. We selected the ‘battle’ type of conflict event data from the ACLED database. From January 1, 2023, to October 1, 2024, the ACLED database recorded a total of 7,719 relevant war events in East Asia and Southeast Asia.

import the data

getwd()#you can use it to find the folder 
setwd("D:/R-4.3.1/R.SCRIPT/THE COURSE/VISUALIZATION/assignment-10.24/R-code and data")#you can change the file directory.
war_in_asia<-read.csv("2023-01-01-2024-10-01-East_Asia-Southeast_Asia.csv")

wardata<-data.frame(war_in_asia$fatalities,war_in_asia$latitude,war_in_asia$longitude)

length(war_in_asia$region[war_in_asia$region=='Southeast Asia'])

mean(war_in_asia$fatalities)

median(war_in_asia$fatalities)

wardata <- na.omit(wardata)

visualization of the wars

longitude <- wardata$war_in_asia.longitude
latitude <- wardata$war_in_asia.latitude

library(ggplot2)
library(ggmap)
library(sp)
library(rnaturalearth)

# load the world data
world <- ne_countries(scale = "medium", returnclass = "sf")

# set the world map
mp <- ggplot() +
  geom_sf(data = world, fill = "gray96", color = "gray50") +
  coord_sf(crs = "+proj=longlat +datum=WGS84") +
  theme_void()

# put the point where wars happened
size_warpoint <- wardata$war_in_asia.fatalities**(1/2)
color_warpoint <- (wardata$war_in_asia.fatalities + 1)**(1/2)

mp2 <- mp +
  geom_point(aes(x = longitude , y = latitude, size = size_warpoint,
                 color = color_warpoint), alpha = 0.5) +
  scale_color_gradient(low = "pink2", high = "darkred") +
  scale_size(range = c(0.1, 3))

# focus on the research district
mp3 <- mp2 +
  geom_sf() +
  coord_sf(xlim = c(80, 130), ylim = c(10, 46), expand = FALSE) +
  labs(title = "Wars in East and Southeast Asia") +
  theme(
    axis.title.x = element_text(margin = margin(t = 10)),
    axis.title.y = element_text(angle = 90,margin = margin(r = 10)),
    axis.text.x = element_text(margin = margin(t = 5)),
    axis.text.y = element_text(margin = margin(r = 5)),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.5),
    panel.grid.minor = element_line(color = "gray95", linewidth = 0.25)
  )

# show the map
mp3

moran’s I index

This study aims to investigate whether the intensity of wars in the East Asia-Southeast Asia region is related to spatial factors:

warplace.dists <- as.matrix(dist(cbind(wardata$war_in_asia.latitude, wardata$war_in_asia.longitude)))

warplace.dists.inv <- 1/warplace.dists

diag(warplace.dists.inv) <- 0
warplace.dists.inv[is.infinite(warplace.dists.inv)] <- 0
MoranItest_result<-Moran.I(wardata$war_in_asia.fatalities,warplace.dists.inv)

MoranItest_result

write.csv(MoranItest_result, "moranIoutput.csv", row.names = TRUE)