London Migration
George Akomfrah
2026-02-17
Results
Internal Migration
Flow of internal migration events in and out of London, 2019
to 2024 combined
This figure visualises internal migration flows between the regions of England and Wales over years 2019 to 2024, combined. All flow within regions (from one local authority to another) have been removed.
Line width represents the volume of population flow which is also marked with ticks on the circumference of the chart. Lines raised higher from the edge represent outflow of population towards the region marked in the respective colour.
Despite being the second*most populous region, London see the highest volume of population flow (around 3.9 million) compared to the most populous region of South East at around 3.5 million.
Overall, London shows a net inflow of internal migrants.
The destination regions for the most population outflow from London were South East followed East region.
The source regions for the most population inflow into London were also South East followed by East region.
In-flow, out-flow and net internal migration flow, London, 2019 to 2024 combined
The Figure shows migration events by year, in and out of London with the net events shown in the grey line.
London typically see’s a larger volume of migration out of the region, resulting in a yearly net outflow of internal migration events.
The largest net outflow was observed in 2021, likely influence by the COVID-19 pandemic.
International Migration - UK
Immigration, emigration and net migration, 2014 to 2024
This chart shows internation immigration, emigration and net migration shown in the grey line.
The UK typically see’s net inflow of international migration.
From 2022 to 2024 there has been a sharp increase in the immigration, whilst emigration remained mostly similar. This has resulted in an increase in net migration into the UK following the COVID-19 pandemic.
International Migration - London foreign births
Cumulative arriving population of foreign born resident
living in London in 2021, arriving before 1951 to 2021
This figure shows the cumulative makeup of London’s foreign born London residents based on year of arrival to the UK.
It is important to note that as this taken from a ‘snap shot’ from the 2021 census, the contribution of more recent years is influenced by attrition of foreign born resident due to both migration out of London as well as mortality.
Additionally, due to Census 2021 reporting, 2011 to 2021 contains an extra year compared to the 10-year range of prior years starting in 1961.
In 2021, roughly 60% of foreign born london resident had arrived to the UK since 2011.
Country, subregion and continent of origin of foreign born
London resident, 2021
This figure gives a breakdown of the top 10 places of birth contributing
to London’s foreign born resident population.
India is the country contributing the largest population of foreign born residents in London.
Romania and Poland are second and third, respectively.
When assessed by both sub-region and continent of birth, Europe contributes the largest population of foreign born residents in London.
R Code
# Summary -----------------------------------------------------------------
# To build a visual overview of migration for London.
# To produce 5 key visuals from ONS internal migration movement and census 2021 foreign born usual residents.
# (1) Census 2021 - Foreign born place of birth (sankey: region -> country -> England -> London
# (2) Census 2021 - Foreign born by year of arrival (Cumulative Bare)
# (3) International migration - Net international migration over time (divergent bar + net overlay)
# (4) Internal migration - Movement in/out London by external local authority & region (Chord chart)
# (5) Internal migration - Net internal migration over time (divergent bar + net overlay)
# Setup -------------------------------------------------------------------
library(openxlsx)
library(formattable)
library(dplyr)
library(reshape2)
library(tidyr)
library(stringr)
library(tinytex)
library(ggtext)
library(ggplot2)
library(ggsankey)
library(circlize)
# Files -------------------------------------------------------------------
# Data
# data.internal.migration.2024 <- read.xlsx("detailedestimates2024on2023las.xlsx", sheet = "IM2024 on 2023 LAs")
#
# data.internal.migration.2023 <- read.xlsx("detailedestimates2023on2023las.xlsx", sheet = "IM2023 on 2023 LAs")
#
# data.internal.migration.2022 <- read.xlsx("detailedestimates2022on2023las.xlsx", sheet = "IM2022 on 2023 LAs")
#
# data.internal.migration.2021 <- read.xlsx("detailedestimates2021on2021and2023las.xlsx", sheet = "2021 on 2023 LAs")
#
# data.internal.migration.2020 <- read.xlsx("detailedestimates2020on2021and2023las.xlsx", sheet = "2020 on 2023 LAs")
#
# data.internal.migration.2019 <- read.xlsx("detailedestimates2019on2021and2023las.xlsx", sheet = "2019 on 2023 LAs")
data.international.migration.2024 <- read.xlsx("ltimnov25.xlsx",
sheet = "1",
startRow = 6)
data.census <- read.csv("census_2021_london_birthplace_arrivalyear.csv")
table.internal.migration <- read.csv("table.internal.migration.csv")
# Lookups
lookup.la2023toregion <- read.xlsx("lasregionew2023lookup.xlsx", startRow = 4)
# Clean -------------------------------------------------------------------
colnames(data.international.migration.2024) <- c("flow", "year", "all.nationalities","british","eu", "non.eu")
colnames(data.census) <- c("region.code","region","birth.code","birth","arrival.year.code","arrival.year","population")
# data.internal.migration.2021 <-
# data.internal.migration.2021 %>%
# rename(year = Year)
#
# data.internal.migration.2020 <-
# data.internal.migration.2020 %>%
# rename(year = Year)
#
# data.internal.migration.2019 <-
# data.internal.migration.2019 %>%
# rename(year = Year)
# tests --------------------------------------------------------------------
# Internal Migration (Chord Chart) ------------------------------------------------------
# Group by sex, calculate totals across all ages, join regions
# table.internal.migration <-
# bind_rows( # combine internal migration files
# data.internal.migration.2019,
# data.internal.migration.2020,
# data.internal.migration.2021,
# data.internal.migration.2022,
# data.internal.migration.2023,
# data.internal.migration.2024
# ) %>%
# mutate( # add total column based on all age rows
# age.total = rowSums(pick(contains("age")), na.rm = TRUE)
# ) %>%
# select(
# outla,
# inla,
# sex,
# year,
# age.total
# ) %>%
# group_by(outla, inla, year) %>% # group columns to aggregate sex
# summarise(age.total = sum(age.total), .groups = "drop") %>%
# left_join( # join region lookup to inward LA and to outward LA
# lookup.la2023toregion,
# by = c("outla" = "LA.code")
# ) %>%
# left_join(
# lookup.la2023toregion,
# by = c("inla" = "LA.code")
# ) %>%
# rename(
# outla.name = LA.name.x,
# outregion.name = Region.name.x,
# outregion.code = Region.code.x,
# inla.name = LA.name.y,
# inregion.name = Region.name.y,
# inregion.code = Region.code.y
# )
# Export for reduced file size on import
# write.csv(table.internal.migration, "table.internal.migration.csv")
# Chord Diagram
table.internal.migration2 <-
table.internal.migration %>%
select(inregion.name, outregion.name, age.total)%>%
group_by(inregion.name, outregion.name)%>%
summarise(age.total = sum(age.total), .groups = "drop")%>%
filter(
!is.na(outregion.name),
!is.na(inregion.name),
outregion.name != inregion.name
)%>%
rename(
from = outregion.name,
to = inregion.name,
value = age.total)
# ) %>%
# mutate(
# value = round(value/1000,0)
# )
chordDiagramFromDataFrame(table.internal.migration2,
direction.type = "diffHeight"
)
# Identify which links involve London
is_london <- table.internal.migration2$from == "London" |
table.internal.migration2$to == "London"
# Set link colors: grey for normal, red for London links
link_colors <- ifelse(is_london, "sienna2", "grey80")
# Set link widths: thicker for London
link_widths <- ifelse(is_london, 2, 0.5)
# Draw chord diagram with highlighted links
chordDiagramFromDataFrame(
table.internal.migration2,
directional = 1,
direction.type = "diffHeight",
col = link_colors,
link.lwd = 1.75
)
par(cex = 2.5)
# Internal Migration (Divergent Bar) --------------------------------------
# remove rows where migration is moving within London
table.internal.migration.divergentbar <-
table.internal.migration %>%
filter(
outregion.name != inregion.name
)
# Create two separate dataframes, filtered to London for inregion and outregion, respectively. Join dataframes by year and calculate 3rd net migration column.
table.internal.migration.divergentbar.in <-
table.internal.migration.divergentbar%>%
filter(
inregion.name == "London" #arrivals into London only
)%>%
select(
year,
age.total
)%>%
group_by(year)%>%
summarise(age.total = sum(age.total))
table.internal.migration.divergentbar.out <-
table.internal.migration.divergentbar%>%
filter(
outregion.name == "London" #departures from London only
)%>%
select(
year,
age.total
)%>%
group_by(year)%>%
summarise(age.total = sum(age.total))
table.internal.migration.divergentbar.net <-
left_join(
table.internal.migration.divergentbar.in,
table.internal.migration.divergentbar.out,
by = "year"
) %>%
mutate(
net = age.total.x - age.total.y,
age.total.y = age.total.y * -1
)%>%
rename(
"Arriving" = "age.total.x",
"Leaving" = "age.total.y",
"Net migration" = "net",
"Year" = "year"
) %>%
melt(
value.name = "Value",
id.vars = "Year",
variable.name = "Migration"
) %>%
mutate(
Value = round(Value / 1000,0)
)
# Chart
chart.internal.migration.bar <-
ggplot(
table.internal.migration.divergentbar.net,
aes(x = Year, y = Value)
) +
geom_bar(
data = dplyr::filter(
table.internal.migration.divergentbar.net,
Migration %in% c("Arriving", "Leaving")
),
alpha = 0.6,
aes(fill = Migration),
stat = "identity",
position = "stack"
) +
geom_line(
data = dplyr::filter(
table.internal.migration.divergentbar.net,
Migration == "Net migration"
),
aes(group = 1),
colour = "grey40",
linewidth = 1.2
) +
geom_point(
data = dplyr::filter(
table.internal.migration.divergentbar.net,
Migration == "Net migration"
),
colour = "grey40",
size = 4
) +
# Labels on points
geom_text(
data = dplyr::filter(
table.internal.migration.divergentbar.net,
Migration == "Net migration"
),
aes(label = Value),
vjust = -2,
colour = "black",
size = (12/3.5) #scaled to match axis labels
)+
scale_fill_manual(
values = c(
"Arriving" = "steelblue",
"Leaving" = "sienna2"
)
) +
theme_minimal() +
labs(
y = "Migrating population (thousands)"
) +
theme(
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.text = element_text(size = 12)
) +
scale_x_continuous(breaks = seq(2019,2024, by = 1)) # force all numbers to show for x axis labels
chart.internal.migration.bar
# International Migrants (Sankey) ----------------------------------------
# flow to show: country -> sub region -> region -> London
# group all arrivals from year of arrival
# separate out regions/sub region/country based on ":"
# calculate top countries within each region to aggregate those outside of top X by conditional column based on % of total. e.g IF country contrinutes < 2% of total migrants = Other
test <-
data.census %>%
group_by(region, birth) %>%
summarise(population = sum(population), .groups = "drop") %>%
mutate(
country = sub(".*: ", "", birth) # country or most granular place of birth available
) %>%
separate(
birth,
into = c("continent", "subregion", "subsubregion"),
sep = ": "
) %>%
filter(
subregion != "United Kingdom" #remove births from UK
) %>%
mutate(
total.population = sum(population),
percent = ((population/total.population) * 100) # to find contribution of country to foreign births
) %>%
arrange(desc(population)) %>%
mutate(cumulative.percent = cumsum(percent))
table.foreign.births <-
data.census %>%
group_by(region, birth) %>%
summarise(population = sum(population), .groups = "drop") %>%
mutate(
country = sub(".*: ", "", birth) # country or most granular place of birth available
) %>%
separate(
birth,
into = c("continent", "subregion", "subsubregion"),
sep = ": "
) %>%
filter(
subregion != "United Kingdom" #remove births from UK
) %>%
mutate(
total.population = sum(population),
percent = ((population/total.population) * 100) # to find contribution of country to foreign births
) %>%
filter(
percent >= 2.8
) %>%
mutate(
country = # if already summarised as an "other" country standardise all "other"
if_else(
str_detect(country,
regex("other", ignore_case = TRUE)),
paste0("Other - ", subregion),
country
),
country.2 = if_else(percent < 1, "Other", country),
subregion = if_else(subregion == "Other Europe", subsubregion, subregion) #fixes europe subregion to eu/restofeurope
) %>%
group_by( # group to collapse all "other" countries within respective continent and subregion
region,
continent,
subregion,
country
) %>%
summarise(population = sum(population), .groups = "drop")
sankey_data <-
table.foreign.births %>%
make_long(
country,
subregion,
continent,
region,
value = population
) %>%
arrange(desc(value))
chart.international.migration.sankey <-
ggplot(sankey_data,
aes(x = x,
node = node,
next_x = next_x,
next_node = next_node,
fill = factor(node),
label = node,
value = value
)
) +
theme_minimal(
) +
geom_sankey(
flow.alpha = 0.6,
show.legend = F
) +
theme(
axis.text.x = element_text( size = 12),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
legend.position = "none"
) +
scale_x_discrete(
labels = c(
"country" = "Country",
"subregion" = "Subregion",
"continent" = "Continent",
"region" = "London"
)
)+
geom_sankey_label(
)
# International Migrants (Cumulative Bar) ----------------------------------------
table.foreign.births.trend <-
data.census %>%
filter(
arrival.year != "Born in the UK",
arrival.year != "Does not apply"
) %>%
group_by(arrival.year, arrival.year.code) %>%
summarise(population = sum(population), .groups = "drop") %>%
mutate(
year = sub(".*Arrived ", "", arrival.year) # country or most granular place of birth available
) %>%
mutate(
collapse.years = if_else(year %in% c("2020 to 2021",
"2017 to 2019",
"2014 to 2016",
"2011 to 2013"),
"2011 to 2021",
year
)
) %>%
group_by(collapse.years) %>%
summarise(population = sum(population),
arrival.year.code = sum(arrival.year.code),
.groups = "drop") %>%
arrange(arrival.year.code) %>%
mutate(
collapse.years = factor(
collapse.years,
levels = collapse.years),
cum.population = cumsum(population),
arrival.year.code = if_else(arrival.year.code == 42, 9, arrival.year.code) # quick fix for sort column numbering
)
# Chart
chart.international.migration.stackedarea <-
ggplot(
data = table.foreign.births.trend,
aes(
x = arrival.year.code,
y = cum.population,
group = 1
)
) +
geom_col(
aes(
fill = collapse.years
)
) +
theme_minimal() +
theme(
axis.ticks.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text.y = element_text(size = 12),
legend.text = element_text(size = 12)
)+
scale_y_continuous(labels = scales::label_comma())+
labs(
y = "Cumulative Population",
fill = "Arrival Year"
) +
scale_fill_viridis_d()
# International Migrants (Divergent Bar) ----------------------------------------
#filter to YE June, unpivot flow
table.international.migration <-
data.international.migration.2024 %>%
filter(
str_detect(year, "Jun")
)%>%
mutate(
year = case_when(
str_detect(year, "24") ~2024,
str_detect(year, "23") ~2023,
str_detect(year, "22") ~2022,
str_detect(year, "21") ~2021,
str_detect(year, "20") ~2020,
str_detect(year, "19") ~2019,
str_detect(year, "18") ~2018,
str_detect(year, "17") ~2017,
str_detect(year, "16") ~2016,
str_detect(year, "15") ~2015,
str_detect(year, "14") ~2014,
TRUE ~ 0
)
)%>%
filter(
year != 0
)%>%
select(
year,
flow,
all.nationalities
)%>%
mutate(
all.nationalities = round(all.nationalities / 1000),
all.nationalities =
case_when(
str_detect(flow, "Emigration") ~ all.nationalities * -1, # where migration type is emigration, make negative, otherwise keep the same value
TRUE ~ all.nationalities
)
)
# chart
chart.international.migration.bar <-
ggplot(
table.international.migration,
aes(x = year, y = all.nationalities)
) +
geom_bar(
data = dplyr::filter(
table.international.migration,
flow %in% c("Immigration", "Emigration")
),
alpha = 0.6,
aes(fill = flow),
stat = "identity",
position = "stack"
) +
geom_line(
data = dplyr::filter(
table.international.migration,
flow == "Net migration"
),
aes(group = 1),
colour = "grey40",
linewidth = 1.2
) +
geom_point(
data = dplyr::filter(
table.international.migration,
flow == "Net migration"
),
colour = "grey40",
size = 4
) +
# Labels on points
geom_text(
data = dplyr::filter(
table.international.migration,
flow == "Net migration"
),
aes(label = all.nationalities),
vjust = -2,
colour = "black",
size = (12/3.5) # scaled to match axis labels
)+
scale_fill_manual(
values = c(
"Immigration" = "steelblue",
"Emigration" = "sienna2"
)
) +
theme_minimal() +
labs(
y = "Migrating population (thousands)",
x = "Year (ending June)",
fill = "Migration"
)+
theme(
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.text = element_text(size = 12)
) +
scale_x_continuous(breaks = seq(2014,2024, by = 1)) # force all numbers to show for x axis labels