/
process_data.R
1672 lines (1414 loc) · 88.4 KB
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process_data.R
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#### Load or Save Workspace ####
# save.image("/Users/peakcm/Documents/SLE_Mobility/sle_ericson/process_data.RData")
# load("/Users/peakcm/Documents/SLE_Mobility/sle_ericson/process_data.RData")
#### Libraries ####
library(ggplot2)
library(spatial)
library(rgeos)
library(rgdal)
library(gpclib)
library(sp)
library(maptools)
require(chron)
library(spdep)
library(maps)
library(geosphere)
library(RColorBrewer)
library(reshape2)
library(RCurl)
library(TSA)
# library(plotly)
#### Tasks ####
# Look at number of trips during quarantine days compared to non
# Before and after for Tonkolili (July 24)
# Three saturdays (April 4, 11, 18) nationally. Result: Strong signal
# "three days stay at home" March 27-29 nationally. Result: signal is not very strong except on first day
# before and after schools reopen (April 14). Result: no national signal.
#### Read Data ####
# Create a dataset for the Admin 2 Trips
setwd("/Users/peakcm/Documents/SLE_Mobility/sle_ericson")
data_admin2 <- read.csv(file = "IndivMvtBtwnAdmin2DaySep1Agg.csv")
date.start <- as.numeric(as.Date(c("03/20/2015"), format = "%m/%d/%Y"))
dates <- seq(from = date.start, to = date.start + ncol(data_admin2) - 3)
names(data_admin2) <- c("Dis_From", "Dis_To", dates)
data_admin2$cum_trips <- apply(data_admin2[,3:ncol(data_admin2)], 1, sum)
data_admin2$cum_trips_16518to16520 <- apply(data_admin2[,7:9], 1, sum)
data_admin2$cum_trips_16521to16523 <- apply(data_admin2[,10:12], 1, sum)
data_admin2$cum_trips_16524to16526 <- apply(data_admin2[,13:15], 1, sum)
# Create a dataset for the Admin 3 Trips
setwd("/Users/peakcm/Documents/SLE_Mobility/sle_ericson")
data_admin3 <- read.csv(file = "IndivMvtBtwnAdmin3DaySep1Agg.csv")
date.start <- as.numeric(as.Date(c("03/20/2015"), format = "%m/%d/%Y"))
dates <- seq(from = date.start, to = date.start + ncol(data_admin3) - 3)
names(data_admin3) <- c("Chief_From", "Chief_To", dates)
data_admin3$cum_trips <- apply(data_admin3[,3:ncol(data_admin3)], 1, sum)
data_admin3$cum_trips_16518to16520 <- apply(data_admin3[,7:9], 1, sum)
data_admin3$cum_trips_16521to16523 <- apply(data_admin3[,10:12], 1, sum)
data_admin3$cum_trips_16524to16526 <- apply(data_admin3[,13:15], 1, sum)
# Create a dataset for the tower locations provided to us by Ericsson
setwd("/Users/peakcm/Documents/SLE_Mobility/data")
towers <- read.csv(file = "Tower_Locations.csv")
towers$CHCODE <- towers$admin3
towers.fort <- fortify(towers, region = admin3)
# Add Buffer to tower locations
work.dir <- "/Users/peakcm/Documents/SLE_Mobility/Arc GIS"
towers_buffer <- readOGR(work.dir, layer = 'towers_buffer')
# plot(towers_buffer)
towers_buffer.fort <- fortify(towers_buffer)
# Add city CHCODES to the encompassing polygon
data_admin3[data_admin3$Chief_From == 1291, "Chief_From"] <- 1212
data_admin3[data_admin3$Chief_To == 1291, "Chief_To"] <- 1212
towers[towers$CHCODE == 1291,"CHCODE"] <- 1212
data_admin3[data_admin3$Chief_From == 1391, "Chief_From"] <- 1313
data_admin3[data_admin3$Chief_To == 1391, "Chief_To"] <- 1313
towers[towers$CHCODE == 1391,"CHCODE"] <- 1313
data_admin3[data_admin3$Chief_To == 2191, "Chief_To"] <- 2102
data_admin3[data_admin3$Chief_From == 2191, "Chief_From"] <- 2102
towers[towers$CHCODE == 2191,"CHCODE"] <- 2102
data_admin3[data_admin3$Chief_From == 3191, "Chief_From"] <- 3108
data_admin3[data_admin3$Chief_To == 3191, "Chief_To"] <- 3108
towers[towers$CHCODE == 3191,"CHCODE"] <- 3108
data_admin3[data_admin3$Chief_From == 3291, "Chief_From"] <- 3209
data_admin3[data_admin3$Chief_To == 3291, "Chief_To"] <- 3209
towers[towers$CHCODE == 3291,"CHCODE"] <- 3209
# Combine some Freetown towers into the encompassing polygon
data_admin3[data_admin3$Chief_From == 4201, "Chief_From"] <- 4208
data_admin3[data_admin3$Chief_To == 4201, "Chief_To"] <- 4208
towers[towers$CHCODE == 4201,"CHCODE"] <- 4208
data_admin3[data_admin3$Chief_From == 4202, "Chief_From"] <- 4208
data_admin3[data_admin3$Chief_To == 4202, "Chief_To"] <- 4208
towers[towers$CHCODE == 4202,"CHCODE"] <- 4208
data_admin3[data_admin3$Chief_From == 4206, "Chief_From"] <- 4208
data_admin3[data_admin3$Chief_To == 4206, "Chief_To"] <- 4208
data_admin3[data_admin3$Chief_From == 4207, "Chief_From"] <- 4208
data_admin3[data_admin3$Chief_To == 4207, "Chief_To"] <- 4208
towers[towers$CHCODE == 4207,"CHCODE"] <- 4208
# Create a shapefile for Sierra Leone Admin 3
work.dir <- "/Users/peakcm/Documents/SLE_Mobility/Arc GIS/Open Humanitarian Data Repository/Sierra Leone Chiefdoms SLGov Admin_3 2012"
admin3.sp <- readOGR(work.dir, layer = 'Sierra_Leone_Chiefdoms_SLGov_Admin_3_2012')
admin3.sp <- admin3.sp[admin3.sp$CHCODE > 0,]
centroids <- data.frame(getSpPPolygonsLabptSlots(admin3.sp))
names(centroids) <- c("Long", "Lat")
admin3.sp <- spCbind(admin3.sp, centroids$Long)
admin3.sp <- spCbind(admin3.sp, centroids$Lat)
admin3.sp.fort <- fortify(admin3.sp, region = "CHCODE", )
admin3.sp.fort$CHCODE <- as.numeric(admin3.sp.fort$id)
# Create a shapefile for Sierra Leone Admin 2
work.dir <- "/Users/peakcm/Documents/SLE_Mobility/Arc GIS/Open Humanitarian Data Repository/Sierra_Leone_Districts_Admin_2_2012"
admin2.sp <- readOGR(work.dir, layer = 'Sierra_Leone_Districts_Admin_2_2012')
centroids <- data.frame(getSpPPolygonsLabptSlots(admin2.sp))
names(centroids) <- c("Long", "Lat")
admin2.sp <- spCbind(admin2.sp, centroids$Long)
admin2.sp <- spCbind(admin2.sp, centroids$Lat)
admin2.sp.fort <- fortify(admin2.sp, region = "DISCODE", )
admin2.sp.fort$DISCODE <- as.numeric(admin2.sp.fort$id)
# Create a shapefile for Sierra Leone Admin 1
work.dir <- "/Users/peakcm/Documents/SLE_Mobility/Arc GIS/Open Humanitarian Data Repository/Sierra_Leone_Provinces_-_Admin_1_2012"
admin1.sp <- readOGR(work.dir, layer = 'Sierra_Leone_Provinces_-_Admin_1_2012')
centroids <- data.frame(getSpPPolygonsLabptSlots(admin1.sp))
names(centroids) <- c("Long", "Lat")
admin1.sp <- spCbind(admin1.sp, centroids$Long)
admin1.sp <- spCbind(admin1.sp, centroids$Lat)
admin1.sp.fort <- fortify(admin1.sp, region = "DISCODE", )
admin1.sp.fort$RCODE <- as.numeric(admin1.sp.fort$id)
# Create a dataset with estimated travel times between districts from WHO Mr. Yarabah Conteh
names <- c("Bo", "Kaliahun", "Freetown", "Bombali", "Port Loko", "Bonthe", "Kambia", "Kenema", "Koindagu", "Kono", "Moyamba", "Pujehun", "Tonkolili", "Freetown_Rural")
DISCODE <- c(31, 11, 42, 21, 24, 32, 22, 12, 23, 13, 33, 34, 25, 41)
travel.times <- data.frame(rep(DISCODE, each=length(DISCODE)))
names(travel.times) <- c("Dis_From")
travel.times$Dis_To <- rep(DISCODE, times = length(DISCODE))
travel.times$Hours <- NA
travel.times[travel.times$Dis_From==31,"Hours"] <- c(0, 3, 3.5, 3, 2.5, 2, 3, 1, 5, 6, 2, 3, 3.5, 3.5)
travel.times[travel.times$Dis_From==11,"Hours"] <- c(NA, 0, 7, 5.5, 6, 4, 6.5, 2, 7, 4, 5, 5.5, 7, 7)
travel.times[travel.times$Dis_From==42,"Hours"] <- c(NA, NA, 0, 3, 3, 5, 4, 4.5, 5, 6, 4, 5, 4, 0)
travel.times[travel.times$Dis_From==21,"Hours"] <- c(NA, NA, NA, 0, 2, 4, 3, 4, 2, 4, 4, 5, .75, NA)
travel.times[travel.times$Dis_From==24,"Hours"] <- c(NA, NA, NA, NA, 0, 4, .75, 5, 4, 5, 3, 5, 3, NA)
travel.times[travel.times$Dis_From==32,"Hours"] <- c(NA, NA, NA, NA, NA, 0, 4, 2, 6, 7, 3, 3, 5, NA)
travel.times[travel.times$Dis_From==22,"Hours"] <- c(NA, NA, NA, NA, NA, NA, 0, 5, 5, 4, 4, 5, 3, NA)
travel.times[travel.times$Dis_From==12,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, 0, 7, 4, 3, 2.5, 5, NA)
travel.times[travel.times$Dis_From==23,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, NA, 0, 5, 6, 6, 2.5, NA)
travel.times[travel.times$Dis_From==13,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 7, 8, 3.5, NA)
travel.times[travel.times$Dis_From==33,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 3.5, 5, NA)
travel.times[travel.times$Dis_From==34,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 5, NA)
travel.times[travel.times$Dis_From==25,"Hours"] <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA)
travel.times[travel.times$Dis_From==41,"Hours"] <- travel.times[travel.times$Dis_From==42,"Hours"]
for (i in 1:nrow(travel.times)){
if (is.na(travel.times[i, "Hours"]) == 1){
Dis_From <- travel.times[i, "Dis_From"]
Dis_To <- travel.times[i, "Dis_To"]
travel.times[i,"Hours"] <- travel.times[travel.times$Dis_From == Dis_To & travel.times$Dis_To == Dis_From, "Hours"]
}
}
# Import population data
setwd("/Users/peakcm/Documents/2014 Cholera OCV/Data - Raw/Population")
data_chief_pop <- read.csv(file = "SL_Chiefdom_Codes_Pop.csv")
# Open 2014 dataset
setwd("/Users/peakcm/Documents/SLE_Mobility/data")
data_2014 <- read.csv(file = "AllMvtBtwnMUC2014WDate.csv")
date.start.2014 <- as.numeric(as.Date(c("05/29/2014"), format = "%m/%d/%Y"))
date.start.2014.shift <- date.start.2014 - data_2014$dayofyear[1]
data_2014$dayofyear <- data_2014$dayofyear + date.start.2014.shift
data_2014$day <- as.Date(data_2014$dayofyear, origin = "1970-01-01")
#### Troubleshoot towers ####
tower_table <- data.frame(table(towers$CellID))
tower_table[tower_table$Freq>1,]
repeats <- as.character(tower_table[tower_table$Freq>1,"Var1"])
# Towers where the repeat numbers are in roughly the same chiefdom: 11, 12, 13, 31, 32, 33, 151, 152, 153, 397, 3661, 3662, 3663, 3678
# Towers where the repeat numbers are in different places
# 181. WA Urban, WA Rural
# 221, 222, 223 Eastern Koidu Town, WA Urban
# 281, 282, 283 Eastern Kenema, WA Rural
# 311, 312, 313 Southern Bo Town, WA Urban
# 391, 392, 393 Southern Moyamba, WA Urban
# 481, 482, 483 Southern Moyamba, WA Urban
# 791, 792, 793 Southern Bo, WA Urban
# 1401, 1402, 1403 Northern Koinadugu, WA Rural
# 1661, 1662, 1663 Northern Port Loko, WA Urban
# 1941, 1942, 1943 WA Urban, WA Rural
# Other problematic towers from analysis:
# 1, 2, 3, 399 are mising in the geocoded table
# 399 is probably supposed to be with 397 and 398 as per the pattern.
# 3651, 3652, 3653 are currently located in WA, but int he data they seem to be used by people in Southern Bo Town. So might want to move them there.
#### Tonkolili Plots ####
# Focus on Kholifa Rowala Chiefdom (2505) in Tonkolili District
hist(log(data_admin3[data_admin3$Chief_From == 2505, "cum_trips"]), xlab = "log(cumulative trips)", main = "Trips from Kholifa Rowala, Tonkolili")
data_admin3 <- data_admin3[order(-data_admin3$cum_trips),]
plot(data_admin3[data_admin3$Chief_From == 2505, "cum_trips"],
main = "Trips from Kholifa Rowala, Tonkolili", xlab = "Chiefdom Rank", ylab = "Cumulative trips")
# Pie chart (Chiefdoms)
lbls <- paste(data_admin3[data_admin3$Chief_From == 2505,"Chief_To"], "\n", round(data_admin3[data_admin3$Chief_From == 2505,"cum_trips"]/sum(data_admin3[data_admin3$Chief_From == 2505,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_From == 2505,"cum_trips"], labels = lbls, main="Chiefdom Destinations from\nKholifa Rowala, Tonkolili (2505)", )
lbls <- paste(data_admin3[data_admin3$Chief_To == 2505,"Chief_From"], "\n", round(data_admin3[data_admin3$Chief_To == 2505,"cum_trips"]/sum(data_admin3[data_admin3$Chief_To == 2505,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_To == 2505,"cum_trips"], labels = lbls, main="Chiefdom Departures to\nKholifa Rowala, Tonkolili (2505)", )
# List of chiefdom destinations
data_admin3.From2505 <- data_admin3[data_admin3$Chief_From == 2505,c("Chief_From","Chief_To", "cum_trips")]
# View(data_admin3.From2505[order(-data_admin3.From2505$cum_trips),])
sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2000 & data_admin3.From2505$Chief_To < 3000,"cum_trips" ]) / sum(data_admin3.From2505[, "cum_trips"]) # 83.6% of all travel is to the North region
sum(data_admin3.From2505[data_admin3.From2505$Chief_To > 4000 ,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To < 2000 | data_admin3.From2505$Chief_To >= 3000, "cum_trips"]) # 73.8% of all travel outside of the North region is to Western Area
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2102,"cum_trips" ]) / sum(data_admin3.From2505[, "cum_trips"]) # 33.6% of all trips are to Makeni Town
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2102,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2000 & data_admin3.From2505$Chief_To < 3000, "cum_trips"]) # 40.2% of travel to the North region is to Makeni Town
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2102,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2100 & data_admin3.From2505$Chief_To < 2200, "cum_trips"]) # 94.3% of travel to Bombali District is to Makeni Town
sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2100 & data_admin3.From2505$Chief_To < 2200,"cum_trips" ]) / sum(data_admin3.From2505[, "cum_trips"]) # 35.7% of all travel is to Bombali District
sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2500 & data_admin3.From2505$Chief_To < 2600,"cum_trips" ]) / sum(data_admin3.From2505[, "cum_trips"]) # 44.0% of all travel is to Tonkolili District
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2510 ,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2500 & data_admin3.From2505$Chief_To < 2600, "cum_trips"]) # 47.1% of all travel to Tonkolili District is to Tane chiefdom
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2510 ,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To >= 2500 & data_admin3.From2505$Chief_To < 2600, "cum_trips"]) # 47.1% of all travel to Tonkolili District is to Tane chiefdom
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 2102 | data_admin3.From2505$Chief_To == 2102,"cum_trips" ]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To < 2000, "cum_trips"])
sum(data_admin3.From2505[data_admin3.From2505$Chief_To == 3108, "cum_trips"]) / sum(data_admin3.From2505[data_admin3.From2505$Chief_To > 3000 & data_admin3.From2505$Chief_To < 4000, "cum_trips"])
# Pie chart (Districts)
data_Tonk_districts <- data.frame(sort(unique(data_admin2[,1])))
names(data_Tonk_districts) <- c("district")
data_Tonk_districts$from <- NA
data_Tonk_districts$to <- NA
for (dist in data_Tonk_districts$district){
cat(dist, "\n")
data_Tonk_districts[data_Tonk_districts$district == dist, "from"] <-
sum(data_admin3[data_admin3$Chief_From == 2505 & floor(data_admin3$Chief_To/100) == dist, "cum_trips"])
data_Tonk_districts[data_Tonk_districts$district == dist, "to"] <-
sum(data_admin3[data_admin3$Chief_To == 2505 & floor(data_admin3$Chief_From/100) == dist, "cum_trips"])
}
data_Tonk_districts$region <- floor(data_Tonk_districts$district/10)
data_Tonk_districts$region <- factor(data_Tonk_districts$region,
levels = c(2, 4, 1, 3),
labels = c("North", "West", "East", "South"))
data_Tonk_districts$district_name <- c("Kailahun", "Kenema", "Kono", "Bombali", "Kambia", "Koinadugu", "Port Loko", "Tonkolili", "Bo", "Bonthe", "Moyamba", "Pujehun", "Western Area Rural", "Western Area Urban")
data_Tonk_districts <- data_Tonk_districts[order(data_Tonk_districts$to),] # Sort data so for horizontal bar chart
data_Tonk_districts$district <- factor(data_Tonk_districts$district, levels = data_Tonk_districts$district,
labels = data_Tonk_districts$district_name)
ggplot(data=data_Tonk_districts) +
geom_bar(aes(x=district, y=from, fill = region), stat="identity") +
# xlab("Destination District Number") +
ylab("Number of trips") +
ggtitle("Trips from Kholifa Rowala, Tonkolili") +
scale_fill_discrete(name = "Region") +
theme_bw() + coord_flip() +
theme(text = element_text(size=18)) +
theme(axis.title.y = element_blank())
# ggplot(data=data_Tonk_districts) +
# geom_bar(aes(x=factor(district), y=to, fill = factor(region)), stat="identity") +
# xlab("Destination District Number") +
# ylab("Number of trips") +
# ggtitle("Trips to Kholifa Rowala, Tonkolili") +
# scale_fill_discrete(name = "Region", labels=c("East","North","South", "West")) +
# theme_bw()
#
# ggplot(data=data_Tonk_districts) +
# geom_bar(aes(x=factor(district), y=from, fill = factor(region)), stat="identity") +
# xlab("Source District Number") +
# ylab("Number of trips") +
# ggtitle("Trips from Kholifa Rowala, Tonkolili") +
# scale_fill_discrete(name = "Region",labels=c("East","North","South", "West")) +
# theme_bw()
#### Spatial for Tonkolili ####
tonk <- data.frame(matrix(rep(NA, nrow(admin3.sp)*3), ncol=3))
names(tonk) <- c("CHCODE", "from_tonk", "to_tonk")
for (row in 1:nrow(admin3.sp)){
chief <- admin3.sp$CHCODE[row]
tonk[row, "CHCODE"] <- chief
if (is.element(chief, data_admin3$Chief_From) & is.element(chief, data_admin3$Chief_To) & chief != 2505){
tonk[row, "from_tonk"] <- max(0, data_admin3[data_admin3$Chief_From == 2505 & data_admin3$Chief_To == chief, "cum_trips"])
tonk[row, "to_tonk"] <- max(0, data_admin3[data_admin3$Chief_To == 2505 & data_admin3$Chief_From == chief, "cum_trips"])
} else {
tonk[row, "from_tonk"] <- 0
tonk[row, "to_tonk"] <- 0
}
}
admin3.sp.fort$from_tonk <- NA
admin3.sp.fort$to_tonk <- NA
for (row in 1:nrow(admin3.sp.fort)){
if (is.element(admin3.sp.fort[row,"CHCODE"], tonk$CHCODE)){
admin3.sp.fort[row, "from_tonk"] <- tonk[tonk$CHCODE == admin3.sp.fort[row,"CHCODE"],"from_tonk"]
admin3.sp.fort[row, "to_tonk"] <- tonk[tonk$CHCODE == admin3.sp.fort[row,"CHCODE"],"to_tonk"]
} else {
admin3.sp.fort[row, "from_tonk"] <- 0
admin3.sp.fort[row, "to_tonk"] <- 0
}
}
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(from_tonk+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2505,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Kholifa Rowala, Tonkolili") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = from_tonk/sum(tonk$from_tonk), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2505,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Kholifa Rowala, Tonkolili") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(to_tonk+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2505,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Kholifa Rowala, Tonkolili") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = to_tonk/sum(tonk$to_tonk), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2505,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Kholifa Rowala, Tonkolili") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
#### Kambia Plots ####
# Focus on Tonko Limba Chiefdom (2207) in Kambia District
# http://www.reuters.com/article/2015/09/02/us-health-ebola-leone-idUSKCN0R22CV20150902
# http://www.cidrap.umn.edu/news-perspective/2015/09/more-ebola-sierra-leone-dallas-probe-notes-missteps
hist(log(data_admin3[data_admin3$Chief_From == 2207, "cum_trips"]), xlab = "log(cumulative trips)", main = "Trips from Tonko Limba, Kambia")
data_admin3 <- data_admin3[order(-data_admin3$cum_trips),]
plot(data_admin3[data_admin3$Chief_From == 2207, "cum_trips"],
main = "Trips from Tonko Limba, Kambia", xlab = "Chiefdom Rank", ylab = "Cumulative Trips")
# Pie chart (Chiefdoms)
lbls <- paste(data_admin3[data_admin3$Chief_From == 2207,"Chief_To"], "\n", round(data_admin3[data_admin3$Chief_From == 2207,"cum_trips"]/sum(data_admin3[data_admin3$Chief_From == 2207,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_From == 2207,"cum_trips"], labels = lbls, main="Chiefdom Destinations\n from Tonko Limba, Kambia (2207)", )
lbls <- paste(data_admin3[data_admin3$Chief_To == 2207,"Chief_From"], "\n", round(data_admin3[data_admin3$Chief_To == 2207,"cum_trips"]/sum(data_admin3[data_admin3$Chief_To == 2207,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_To == 2207,"cum_trips"], labels = lbls, main="Chiefdom Departures\n to Tonko Limba, Kambia (2207)", )
# List of chiefdom destinations
data_admin3.From2207 <- data_admin3[data_admin3$Chief_From == 2207,c("Chief_From","Chief_To", "cum_trips")]
# View(data_admin3.From2207[order(-data_admin3.From2207$cum_trips),])
sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2000 & data_admin3.From2207$Chief_To < 3000,"cum_trips" ]) / sum(data_admin3.From2207[, "cum_trips"]) # 86.4% of all travel is to the North region
sum(data_admin3.From2207[data_admin3.From2207$Chief_To > 4000 ,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To < 2000 | data_admin3.From2207$Chief_To >= 3000, "cum_trips"]) # 95.0% of all travel outside of the North region is to Western Area
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2102,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2100 & data_admin3.From2207$Chief_To < 2200, "cum_trips"]) # Only 9.9% of all trips to Bombali District are to Makeni Town
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2112,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2100 & data_admin3.From2207$Chief_To < 2200, "cum_trips"]) # 52.1% of all trips to Bombali District are to Sella Limba
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2111,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2100 & data_admin3.From2207$Chief_To < 2200, "cum_trips"]) # 35.5% of all trips to Bombali District are to Sandra Tenda
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2111 | data_admin3.From2207$Chief_To == 2112,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2100 & data_admin3.From2207$Chief_To < 2200, "cum_trips"]) # 87.7% of all trips to Bombali District are to Sella Limba or Sandra Tenda
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2201,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2200 & data_admin3.From2207$Chief_To < 2300, "cum_trips"]) # 71.2% of travel to the Kambia District is to Bramala Chiefdom
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2203,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2200 & data_admin3.From2207$Chief_To < 2300, "cum_trips"]) # 25.8% of travel to the Kambia District is to Magbema Chiefdom
sum(data_admin3.From2207[data_admin3.From2207$Chief_To == 2201 | data_admin3.From2207$Chief_To == 2203,"cum_trips" ]) / sum(data_admin3.From2207[data_admin3.From2207$Chief_To >= 2200 & data_admin3.From2207$Chief_To < 2300, "cum_trips"]) # 97.1% of travel to the Kambia District is to Bramala or Magbema Chiefdoms
# Pie chart (Districts)
data_Kambia_districts <- data.frame(sort(unique(data_admin2[,1])))
names(data_Kambia_districts) <- c("district")
data_Kambia_districts$from <- NA
data_Kambia_districts$to <- NA
for (dist in data_Kambia_districts$district){
cat(dist, "\n")
data_Kambia_districts[data_Kambia_districts$district == dist, "from"] <-
sum(data_admin3[data_admin3$Chief_From == 2207 & floor(data_admin3$Chief_To/100) == dist, "cum_trips"])
data_Kambia_districts[data_Kambia_districts$district == dist, "to"] <-
sum(data_admin3[data_admin3$Chief_To == 2207 & floor(data_admin3$Chief_From/100) == dist, "cum_trips"])
}
data_Kambia_districts$region <- floor(data_Kambia_districts$district/10)
data_Kambia_districts$region <- factor(data_Kambia_districts$region,
levels = c(2, 4, 1, 3),
labels = c("North", "West", "East", "South"))
data_Kambia_districts$district_name <- c("Kailahun", "Kenema", "Kono", "Bombali", "Kambia", "Koinadugu", "Port Loko", "Tonkolili", "Bo", "Bonthe", "Moyamba", "Pujehun", "Western Area Rural", "Western Area Urban")
data_Kambia_districts <- data_Kambia_districts[order(data_Kambia_districts$to),] # Sort data so for horizontal bar chart
data_Kambia_districts$district <- factor(data_Kambia_districts$district, levels = data_Kambia_districts$district,
labels = data_Kambia_districts$district_name)
ggplot(data=data_Kambia_districts) +
geom_bar(aes(x=district, y=from, fill = region), stat="identity") +
# xlab("Destination District Number") +
ylab("Number of trips") +
ggtitle("Trips from Tonko Limba, Kambia") +
scale_fill_discrete(name = "Region") +
theme_bw() + coord_flip() +
theme(text = element_text(size=18)) +
theme(axis.title.y = element_blank())
# Pie chart (Districts)
data_kambia_districts <- data.frame(sort(unique(data_admin2[,1])))
names(data_kambia_districts) <- c("district")
data_kambia_districts$from <- NA
data_kambia_districts$to <- NA
for (dist in data_kambia_districts$district){
cat(dist, "\n")
data_kambia_districts[data_kambia_districts$district == dist, "from"] <-
sum(data_admin3[data_admin3$Chief_From == 2207 & floor(data_admin3$Chief_To/100) == dist, "cum_trips"])
data_kambia_districts[data_kambia_districts$district == dist, "to"] <-
sum(data_admin3[data_admin3$Chief_To == 2207 & floor(data_admin3$Chief_From/100) == dist, "cum_trips"])
}
data_kambia_districts$region <- floor(data_kambia_districts$district/10)
# lbls <- paste(data_kambia_districts$district, "\n", round(data_kambia_districts$from/sum(data_kambia_districts$from)*100, 0),"%", sep="")
# pie(x = data_kambia_districts$from, labels = lbls, main="District Destinations\n from Tonko Limba, Kambia (2207)")
#
# lbls <- paste(data_kambia_districts$district, "\n", round(data_kambia_districts$to/sum(data_kambia_districts$to)*100, 0),"%", sep="")
# pie(x = data_kambia_districts$to, labels = lbls, main="District Destinations\n to Tonko Limba, Kambia (2207)")
ggplot(data=data_kambia_districts) +
geom_bar(aes(x=factor(district), y=from, fill = factor(region)), stat="identity") +
xlab("Source District Number") +
ylab("Number of trips") +
ggtitle("Trips from Tonko Limba, Kambia") +
scale_fill_discrete(name = "Region",labels=c("East","North","South", "West")) +
theme_bw()
sum(data_kambia_districts[data_kambia_districts$district < 30 & data_kambia_districts$district >= 20, "from"]) / sum(data_kambia_districts[, "from"])
sum(data_kambia_districts[data_kambia_districts$district >= 40, "from"]) / sum(data_kambia_districts[, "from"])
ggplot(data=data_kambia_districts) +
geom_bar(aes(x=factor(district), y=to, fill = factor(region)), stat="identity") +
xlab("Destination District Number") +
ylab("Number of trips") +
ggtitle("Trips to Tonko Limba, Kambia") +
scale_fill_discrete(name = "Region", labels=c("East","North","South", "West")) +
theme_bw()
#### Spatial for Kambia ####
sella_coords <- c(9.252489, -12.773020)
kambia <- data.frame(matrix(rep(NA, nrow(admin3.sp)*3), ncol=3))
names(kambia) <- c("CHCODE", "from_kambia", "to_kambia")
for (row in 1:nrow(admin3.sp)){
chief <- admin3.sp$CHCODE[row]
kambia[row, "CHCODE"] <- chief
if (is.element(chief, data_admin3$Chief_From) & is.element(chief, data_admin3$Chief_To) & chief != 2207){
kambia[row, "from_kambia"] <- max(0, data_admin3[data_admin3$Chief_From == 2207 & data_admin3$Chief_To == chief, "cum_trips"])
kambia[row, "to_kambia"] <- max(0, data_admin3[data_admin3$Chief_To == 2207 & data_admin3$Chief_From == chief, "cum_trips"])
} else {
kambia[row, "from_kambia"] <- 0
kambia[row, "to_kambia"] <- 0
}
}
admin3.sp.fort$from_kambia <- NA
admin3.sp.fort$to_kambia <- NA
for (row in 1:nrow(admin3.sp.fort)){
if (is.element(admin3.sp.fort[row,"CHCODE"], kambia$CHCODE)){
admin3.sp.fort[row, "from_kambia"] <- kambia[kambia$CHCODE == admin3.sp.fort[row,"CHCODE"],"from_kambia"]
admin3.sp.fort[row, "to_kambia"] <- kambia[kambia$CHCODE == admin3.sp.fort[row,"CHCODE"],"to_kambia"]
} else {
admin3.sp.fort[row, "from_kambia"] <- 0
admin3.sp.fort[row, "to_kambia"] <- 0
}
}
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(from_kambia+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2207,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Tonko Limba, Kambia") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=sella_coords[2], y=sella_coords[1]), shape = "X", size=3, color="red")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = from_kambia/sum(kambia$from_kambia), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2207,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Tonko Limba, Kambia") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=sella_coords[2], y=sella_coords[1]), shape = "X", size=3, color="red")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(to_kambia+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2207,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Tonko Limba, Kambia") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white")+
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=sella_coords[2], y=sella_coords[1]), shape = "X", size=3, color="red")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = to_kambia/sum(kambia$to_kambia), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2207,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Tonko Limba, Kambia") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=sella_coords[2], y=sella_coords[1]), shape = "X", size=3, color="red")
#### Bombali Plots ####
# Focus on Makeni Town Chiefdom (2102 by aggregation) in Bombali District
# Note that the quarantined village of Robuya, where she lived, is within a mile of Makeni Town, where she died.
# There may be a closer tower to consider
hist(log(data_admin3[data_admin3$Chief_From == 2102, "cum_trips"]), xlab = "log(cumulative trips)", main = "Trips from Makeni Town, Bombali")
data_admin3 <- data_admin3[order(-data_admin3$cum_trips),]
plot(data_admin3[data_admin3$Chief_From == 2102, "cum_trips"],
main = "Trips from Makeni Town, Bombali", xlab = "Chiefdom Rank", ylab = "Cumulative Trips")
# Pie chart (Chiefdoms)
lbls <- paste(data_admin3[data_admin3$Chief_From == 2102,"Chief_To"], "\n", round(data_admin3[data_admin3$Chief_From == 2102,"cum_trips"]/sum(data_admin3[data_admin3$Chief_From == 2102,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_From == 2102,"cum_trips"], labels = lbls, main="Chiefdom Destinations\n from Makeni Town, Bombali (2102)", )
lbls <- paste(data_admin3[data_admin3$Chief_To == 2102,"Chief_From"], "\n", round(data_admin3[data_admin3$Chief_To == 2102,"cum_trips"]/sum(data_admin3[data_admin3$Chief_To == 2102,"cum_trips"])*100, 0),"%", sep="")
pie(x = data_admin3[data_admin3$Chief_To == 2102,"cum_trips"], labels = lbls, main="Chiefdom Departures\n to Makeni Town, Bombali (2102)", )
# List of chiefdom destinations
data_admin3.From2102 <- data_admin3[data_admin3$Chief_From == 2102,c("Chief_From","Chief_To", "cum_trips")]
# View(data_admin3.From2102[order(-data_admin3.From2102$cum_trips),])
sum(data_admin3.From2102[data_admin3.From2102$Chief_To == 1212 | data_admin3.From2102$Chief_To == 1313,"cum_trips" ]) / sum(data_admin3.From2102[data_admin3.From2102$Chief_To < 2000, "cum_trips"])
sum(data_admin3.From2102[data_admin3.From2102$Chief_To == 3108, "cum_trips"]) / sum(data_admin3.From2102[data_admin3.From2102$Chief_To > 3000 & data_admin3.From2102$Chief_To < 4000, "cum_trips"])
data_admin3.From2102[data_admin3.From2102$Chief_To == 1212 | data_admin3.From2102$Chief_To == 1313, ] / data_admin3.From2102[data_admin3.From2102$Chief_To < 2000, ])
# Pie chart (Districts)
data_Bombali_districts <- data.frame(sort(unique(data_admin2[,1])))
names(data_Bombali_districts) <- c("district")
data_Bombali_districts$from <- NA
data_Bombali_districts$to <- NA
for (dist in data_Bombali_districts$district){
cat(dist, "\n")
data_Bombali_districts[data_Bombali_districts$district == dist, "from"] <-
sum(data_admin3[data_admin3$Chief_From == 2102 & floor(data_admin3$Chief_To/100) == dist, "cum_trips"])
data_Bombali_districts[data_Bombali_districts$district == dist, "to"] <-
sum(data_admin3[data_admin3$Chief_To == 2102 & floor(data_admin3$Chief_From/100) == dist, "cum_trips"])
}
data_Bombali_districts$region <- floor(data_Bombali_districts$district/10)
data_Bombali_districts$region <- factor(data_Bombali_districts$region,
levels = c(2, 4, 1, 3),
labels = c("North", "West", "East", "South"))
data_Bombali_districts$district_name <- c("Kailahun", "Kenema", "Kono", "Bombali", "Kambia", "Koinadugu", "Port Loko", "Tonkolili", "Bo", "Bonthe", "Moyamba", "Pujehun", "Western Area Rural", "Western Area Urban")
data_Bombali_districts <- data_Bombali_districts[order(data_Bombali_districts$to),] # Sort data so for horizontal bar chart
data_Bombali_districts$district <- factor(data_Bombali_districts$district, levels = data_Bombali_districts$district,
labels = data_Bombali_districts$district_name)
plot <-
ggplot(data=data_Bombali_districts) +
geom_bar(aes(x=district, y=from, fill = region), stat="identity") +
# xlab("Destination District Number") +
ylab("Number of trips") +
ggtitle("Trips from Makeni Town, Bombali") +
scale_fill_discrete(name = "Region") +
theme_bw() + coord_flip() +
theme(text = element_text(size=18)) +
theme(axis.title.y = element_blank())
setwd("/Users/peakcm/Desktop")
ggsave(plot, file="MakeniBars.png", dpi = 1000)
ggplot(data=data_Bombali_districts) +
geom_bar(aes(x=district, y=to, fill = factor(region)), stat="identity") +
xlab("Source District Number") +
ylab("Number of trips") +
ggtitle("Trips to Makeni Town, Bombali") +
scale_fill_discrete(name = "Region", labels=c("East","North","South", "West")) +
theme_bw() + coord_flip()
# Report proportion of trips instead
sum_from <- sum(data_Bombali_districts$from)
plot <-
ggplot(data=data_Bombali_districts) +
geom_bar(aes(x=district, y=from/sum_from, fill = region), stat="identity") +
# xlab("Destination District Number") +
ylab("Fraction of trips") +
ggtitle("Top Destinations of Travel from Makeni Town") +
scale_fill_discrete(name = "Region") +
theme_bw() + coord_flip() +
theme(text = element_text(size=18)) +
theme(axis.title.y = element_blank()) +
theme(panel.border = element_blank()) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor = element_blank())
setwd("/Users/peakcm/Desktop")
ggsave(plot, file="MakeniBars.png", dpi = 1000)
#### Spatial for Bombali ####
robuya_coords <- c(8.874, -12)
bombali <- data.frame(matrix(rep(NA, nrow(admin3.sp)*3), ncol=3))
names(bombali) <- c("CHCODE", "from_bombali", "to_bombali")
for (row in 1:nrow(admin3.sp)){
chief <- admin3.sp$CHCODE[row]
bombali[row, "CHCODE"] <- chief
if (is.element(chief, data_admin3$Chief_From) & is.element(chief, data_admin3$Chief_To) & chief != 2102){
bombali[row, "from_bombali"] <- max(0, data_admin3[data_admin3$Chief_From == 2102 & data_admin3$Chief_To == chief, "cum_trips"])
bombali[row, "to_bombali"] <- max(0, data_admin3[data_admin3$Chief_To == 2102 & data_admin3$Chief_From == chief, "cum_trips"])
} else {
bombali[row, "from_bombali"] <- 0
bombali[row, "to_bombali"] <- 0
}
}
admin3.sp.fort$from_bombali <- NA
admin3.sp.fort$to_bombali <- NA
for (row in 1:nrow(admin3.sp.fort)){
if (is.element(admin3.sp.fort[row,"CHCODE"], bombali$CHCODE)){
admin3.sp.fort[row, "from_bombali"] <- bombali[bombali$CHCODE == admin3.sp.fort[row,"CHCODE"],"from_bombali"]
admin3.sp.fort[row, "to_bombali"] <- bombali[bombali$CHCODE == admin3.sp.fort[row,"CHCODE"],"to_bombali"]
} else {
admin3.sp.fort[row, "from_bombali"] <- 0
admin3.sp.fort[row, "to_bombali"] <- 0
}
}
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = from_bombali, group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2102,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Makeni Town, Bombali") +
scale_fill_continuous(name = "Number of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_polygon(data = admin2.sp.fort, aes(x=long, y=lat, group=group), colour = "black", fill=NA, linetype = 1, lwd = 0.3) +
geom_polygon(data = admin1.sp.fort, aes(x=long, y=lat, group=group), colour = "black", fill=NA, linetype = 1, lwd = 0.3) +
geom_point(aes(x=robuya_coords[2], y=robuya_coords[1]), shape = "X", size=3, color="red") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.border = element_blank())
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = from_bombali/sum(bombali$from_bombali), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2102,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Makeni Town, Bombali") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=robuya_coords[2], y=robuya_coords[1]), shape = "X", size=3, color="red")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = to_bombali, group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2102,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Makeni Town, Bombali") +
scale_fill_continuous(name = "Number of Trips", low = "white")+
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=robuya_coords[2], y=robuya_coords[1]), shape = "X", size=3, color="red")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = to_bombali/sum(bombali$to_bombali), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2102,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Sources to Makeni Town, Bombali") +
scale_fill_continuous(name = "Proportion of Trips", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=robuya_coords[2], y=robuya_coords[1]), shape = "X", size=3, color="red")
#### Find Chiefdoms without towers ####
length(unique(data_admin3$Chief_From)) / length(unique(towers$CHCODE)) #97% of chiefdoms with towers have travel reports
(length(unique(towers$CHCODE))) / (length(admin3.sp$CHCODE)) #58.6% of chiefdoms in Sierra Leone have at least one tower
unique(towers$CHCODE)
sort(admin3.sp$CHCODE)
sort(data_chief_pop$CHCODE) # aggregates Western Area
is.element(unique(towers$CHCODE), admin3.sp$CHCODE)
admin3.sp$CHCODE[is.element(admin3.sp$CHCODE, unique(towers$CHCODE))==1] #Chiefdoms with towers
admin3.sp$CHCODE[is.element(admin3.sp$CHCODE, unique(towers$CHCODE))==0] #Chiefdoms without towers
data_chief_pop[is.element(data_chief_pop$CHCODE, admin3.sp$CHCODE[is.element(admin3.sp$CHCODE, unique(towers$CHCODE))==0]), ]
hist(data_chief_pop[is.element(data_chief_pop$CHCODE, admin3.sp$CHCODE[is.element(admin3.sp$CHCODE, unique(towers$CHCODE))==0]), "Pop_2012_est"])
sum(data_chief_pop[is.element(data_chief_pop$CHCODE, admin3.sp$CHCODE[is.element(admin3.sp$CHCODE, unique(towers$CHCODE))==0]), "Pop_2012_est"]) /
sum(data_chief_pop[, "Pop_2012_est"])
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(from_tonk+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[is.element(admin3.sp.fort$CHCODE, towers$CHCODE)==0,], aes(x = long, y = lat, group = group), size=1.2, fill = "lightgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2505,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Kholifa Rowala, Tonkolili") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
# geom_polygon(data = towers_buffer.fort, aes(x=long, y=lat), color = "grey")
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(from_kambia+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[is.element(admin3.sp.fort$CHCODE, towers$CHCODE)==0,], aes(x = long, y = lat, group = group), size=1.2, fill = "lightgrey") +
geom_polygon(data = admin3.sp.fort[admin3.sp.fort$CHCODE == 2207,], aes(x = long, y = lat, group = group), size=1.2, colour = "yellow", fill = "white") +
coord_equal() +
theme_bw() + ggtitle("Destinations from Tonko Limba, Kambia") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
geom_point(aes(x=sella_coords[2], y=sella_coords[1]), shape = "X", size=3, color="red") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="grey", size=18, pch = 1)
#### Country-wide maps ####
# What proportion of travel is through Freetown?
sum(data_admin3[data_admin3$Chief_From > 4000, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From < 4000, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From > 4000, "cum_trips"]) / sum(data_admin3[, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From > 4000 & data_admin3$Chief_To > 4000, "cum_trips"]) / sum(data_admin3[, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From > 4000 | data_admin3$Chief_To > 4000, "cum_trips"]) / sum(data_admin3[, "cum_trips"])
# What proportion of travel is through Makeni Town?
sum(data_admin3[data_admin3$Chief_From == 2102, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From == 2102 | data_admin3$Chief_To == 2102, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From != 2102, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From == 2102, "cum_trips"]) / sum(data_admin3[, "cum_trips"])
sum(data_admin3[data_admin3$Chief_From == 2102 | data_admin3$Chief_To == 2102, "cum_trips"]) / sum(data_admin3[, "cum_trips"])
# Map of number of trips from each chiefdom
admin3.sp.fort$trips_from <- NA
admin3.sp.fort$trips_to <- NA
for (chief in unique(admin3.sp.fort$CHCODE)){
admin3.sp.fort[admin3.sp.fort$CHCODE == chief, "trips_from"] <- sum(data_admin3[data_admin3$Chief_From == chief,"cum_trips"])
admin3.sp.fort[admin3.sp.fort$CHCODE == chief, "trips_to"] <- sum(data_admin3[data_admin3$Chief_To == chief,"cum_trips"])
}
plot <-
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(trips_from+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[is.element(admin3.sp.fort$CHCODE, towers$CHCODE)==0,], aes(x = long, y = lat, group = group), size=1.2, fill = "lightgrey") +
coord_equal() +
theme_bw() +
# ggtitle("Cumulative Trips From Each Chiefdom") +
scale_fill_continuous(name = "Number of Trips",
breaks = c(0, 2, 4, 6),
labels = c("1", "100", "10,000", "1,000,000"),
low="white", high="darkblue") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(panel.border = element_blank()) +
theme(axis.ticks = element_blank(), axis.text.y = element_blank(), axis.text.x = element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank())
setwd("/Users/peakcm/Desktop")
ggsave(plot, file="tripsfromchiefs.png", dpi = 1000)
ggplot() +
geom_polygon(data = admin3.sp.fort, aes(x = long, y = lat, fill = log10(trips_to+1), group = group), colour = "darkgrey") +
geom_polygon(data = admin3.sp.fort[is.element(admin3.sp.fort$CHCODE, towers$CHCODE)==0,], aes(x = long, y = lat, group = group), size=1.2, fill = "lightgrey") +
coord_equal() +
theme_bw() + ggtitle("Cumulative Trips To Each Chiefdom") +
scale_fill_continuous(name = "Log10(Number of Trips)", low = "white") +
geom_point(data=towers.fort, aes(x=Long, y=Lat ), color="black", size=1)
# Map all connections
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
lines(inter)
}
# Shade connections by weight
data_admin3 <- data_admin3[order(data_admin3$cum_trips),] #sort so the heavier connections are drawn later
colors <- colorRampPalette(brewer.pal(9,"Blues"))(100)
range <- max(data_admin3$cum_trips)
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
colindex <- colors[ceiling(data_admin3[row,"cum_trips"] / range * 100)]
lines(inter, col = colindex, lwd = (2*ceiling(data_admin3[row,"cum_trips"] / range))^2)
}
# Shade connections weighted by log of count
data_admin3 <- data_admin3[order(data_admin3$cum_trips),] #sort so the heavier connections are drawn later
range <- max(log(data_admin3$cum_trips))
colors <- colorRampPalette(brewer.pal(9,"Blues"))(100)
# To set transparency to ramp up with color
for (i in 1:length(colors)){
if (i < 20){alpha <- 20
} else if (i > 80){alpha <- 80
} else {alpha <- i}
colors[i] <- paste0(colors[i], as.character(alpha))
}
# dev.off()
# png(file="allroutes.png")
plot(admin3.sp)
# plot(admin1.sp[admin1.sp$DISCODE == 2,], add=TRUE, border = "red", lwd = 3, lty = 2)
# plot(admin1.sp[admin1.sp$DISCODE == 3,], add=TRUE, border = "purple", lwd = 3, lty = 2)
# plot(admin1.sp[admin1.sp$DISCODE == 1,], add=TRUE, border = "turquoise", lwd = 3, lty = 2)
# plot(admin1.sp[admin1.sp$DISCODE == 4,], add=TRUE, border = "green", lwd = 3, lty = 2)
geom_polygon(data = admin1.sp.fort, aes(x=long, y=lat, group=group), colour = "black", fill=NA, linetype = 1, lwd = 0.3) +
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# points(towers.fort$Long, towers.fort$Lat)
text(x = robuya_coords[2]+0.25, y = robuya_coords[1]-0.05, label = "Makeni", col = "black", font = 2)
text(x = robuya_coords[2]-1.65, y = robuya_coords[1]-0.45, label = "Freetown", col = "black", font = 2)
# text(x = robuya_coords[2]+0.5, y = robuya_coords[1]-0.8, label = "Bo", col = "white")
# Travel from Freetown. shade connections weighted by log of count
# dev.off()
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
if (data_admin3[row, "Chief_From"] > 4100){
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
}
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# Travel from Bo Town shade connections weighted by log of count
# dev.off()
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
if (data_admin3[row, "Chief_From"] == 3108){
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
}
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# Travel from Kenema Town. shade connections weighted by log of count
# dev.off()
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
if (data_admin3[row, "Chief_From"] == 1212){
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
}
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# Travel from Tonko Limba shade connections weighted by log of count
# dev.off()
plot(admin3.sp)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
if (data_admin3[row, "Chief_From"] == 2207){
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
}
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# Travel from Makeni shade connections weighted by log of count
# dev.off()
# colors <- colorRampPalette(brewer.pal(9,"Greys"))(100)
# # To set transparency to ramp up with color
# for (i in 1:length(colors)){
# if (i < 20){alpha <- 20
# } else if (i > 80){alpha <- 80
# } else {alpha <- i}
#
# colors[i] <- paste0(colors[i], as.character(alpha))
# }
plot(admin3.sp)
plot(admin1.sp[admin1.sp$DISCODE == 2,], add=TRUE, col = "#F35D5A95")
plot(admin1.sp[admin1.sp$DISCODE == 3,], add=TRUE, col = "#B95EFF95")
plot(admin1.sp[admin1.sp$DISCODE == 1,], add=TRUE, col = "#17B2B795")
plot(admin1.sp[admin1.sp$DISCODE == 4,], add=TRUE, col = "#6BA10395")
plot(admin3.sp, col = NA, add = TRUE)
for (row in 1:nrow(data_admin3)){
from <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_From"])
to <- which(admin3.sp$CHCODE==data_admin3[row, "Chief_To"])
if (data_admin3[row, "Chief_From"] == 2102){
lon_1 <- admin3.sp$centroids.Long[from]
lat_1 <- admin3.sp$centroids.Lat[from]
lon_2 <- admin3.sp$centroids.Long[to]
lat_2 <- admin3.sp$centroids.Lat[to]
inter <- gcIntermediate(c(lon_1, lat_1), c(lon_2, lat_2), n=50, addStartEnd=TRUE)
}
weight <- ceiling(log(data_admin3[row,"cum_trips"]) / range * 100) / 100
lines(inter, col = colors[weight*100], lwd = (2.5*weight)^2)
}
# text(x = robuya_coords[2]+0.25, y = robuya_coords[1], label = "Makeni", col = "black", font = 2)
# text(x = robuya_coords[2]-1.65, y = robuya_coords[1]-0.45, label = "Freetown", col = "black", font = 2)
#### Country-wide temporal series ####
# Melt data_admin3 call data
data_admin3.melt <- melt(data_admin3, id = c("Chief_From", "Chief_To"))
# Create a dataset with the cumulative trips on each day
data_admin3.daily <- data.frame(matrix(rep(NA, 2*(ncol(data_admin3)-3)), ncol=2))
names(data_admin3.daily) <- c("day", "cum_trips")
data_admin3.daily$day <- names(data_admin3)[3:(ncol(data_admin3)-1)]
data_admin3.daily$weekday <- NA
data_admin3.daily <- data_admin3.daily[1:(nrow(data_admin3.daily)-3),]
for (row in 1:nrow(data_admin3.daily)){
data_admin3.daily[row, "cum_trips"] <- sum(data_admin3.melt[data_admin3.melt$variable == data_admin3.daily[row, "day"],"value"])
}