Human Trafficking Data Analysis - DATA 606
Julia Ferris
12/6/2023
Disclaimer
This presentation contains content about human trafficking. If at any
time it becomes too much, feel free to mute your computer and turn the
volume on at the end.
Abstract
Human trafficking is a crime that makes victims of people of all
ages, genders, and nationalities. Victims experience forced labor,
commercial sex, and other forms of modern slavery. Currently, over 27
million people are estimated to be subjected to trafficking. Not all
perpetrators of human trafficking are people in organized crime.
Sometimes, governments recruit children for forced labor or other uses
by their people. Since this is such a massive issue occurring worldwide,
it is an issue that needs to be addressed and discussed so that it can
be reduced.
In this presentation, all the data came from the website for the
United Nations Office on Drugs and Crime. To start, the data was
modified into a tidy format to make analysis easier. Several graphs were
created to display descriptive statistics about the information. Then,
the research focused on trends over time for certain countries by gender
and by age, specifically focusing on the U.S. and countries with the
highest numbers. To further my research, I also performed t-tests for
genders and ages to determine if one gender or one age group experiences
trafficking more than the other. The graphs made those differences
clear, but the t-tests showed those differences numerically. Lastly, I
used linear regression by gender, age, and year to predict future
numbers.
I found that significantly more females were victims of human
trafficking compared to males, and I found that significantly more
people ages 18 or older were victims of human trafficking compared to
people less than 18.
Introduction
Data Collection
The data table was downloaded from the United Nations Office on Drugs
and Crime (UNDOC). The data were collected from national authorities,
such as law enforcement and the criminal justice system in each region.
UNDOC collected this data by using the Questionnaire for the Global
Report on Trafficking in Persons (GLOTIP). The information reported in
the questionnaire is verified with other sources. Although most data
were collected with the questionnaire, some information was submitted
from other sources, like official websites of national authorities or
reports from governments.
Dependent Variable
The dependent variable is the number of victims of trafficking. This
is quantitative.
Independent
Variable(s)
The independent variables are gender (males, females) and age group
(17 or younger, 18 or older). For the linear regression, year is another
independent variable. Those are all quantitative.
Research Questions
What are some interesting trends over time for different countries by
gender and/or age?
What are the results of t-tests that compare males to females and
ages 17 and less to ages 18 and older?
How can you use a linear regression line to determine an average
number of people in human trafficking (across all countries and in the
United States) for each of the following:
Males 18 years or more in the year 2023
Females 17 years or less in the year 2025
Males 17 years or less in the year 2025
Females 18 years or more in the year 2023
Data Preparation
library(readr)
traffickingVictims <- read_csv("https://raw.githubusercontent.com/juliaDataScience-22/cuny-fall-23/stats-and-probability/data_glotip.csv")
library(dplyr)
library(plyr)
library(ggplot2)
# Less than 5 can include 0, 1, 2, 3, or 4.
# On average, we can expect 2 to be the value,
# so I changed every value that was <5 to 2.
traffickingVictims$txtVALUE <- gsub("<5",2,traffickingVictims$txtVALUE)
traffickingVictims$txtVALUE <- as.numeric(traffickingVictims$txtVALUE)
traffickingVictims <- traffickingVictims |>
filter(Indicator == "Detected trafficking victims")
traffickingVictims <- traffickingVictims[,c(-1, -11, -13)]
totals <- traffickingVictims |>
filter(Dimension == "Total")
others <- traffickingVictims |>
filter(Dimension != "Total")
countryUnique <- unique(na.omit(traffickingVictims$Country))
newOthersDF <- data.frame(Country = c(NA),
Year = c(NA),
Dimension = c(NA),
Sex = c(NA),
Age = c(NA),
Num_Of_People = c(NA))
for (country in countryUnique)
{
if(!is.na(country))
{
yearsForData <- as.list(others |>
filter(Country %in% country) |>
select(Year) |>
as.list(distinct()))
yearsForData <- unique(yearsForData$Year)
for (year in yearsForData)
{
if(!is.na(year))
{
tempDF <- others |>
filter(Country %in% country) |>
filter(Year %in% year)
dimensions <- unique(tempDF$Dimension)
for (dim in dimensions)
{
if(!is.na(dim))
{
temp2DF <- tempDF |>
filter(Dimension %in% dim)
sexes <- unique(temp2DF$Sex)
for (sex in sexes)
{
if(!is.na(sex))
{
temp3DF <- temp2DF |>
filter(Sex %in% sex)
ages <- unique(temp3DF$Age)
for (age in ages)
{
if(!is.na(age))
{
temp4DF <- temp3DF |>
filter(Age %in% age)
sumForThis <- sum(temp4DF$txtVALUE, na.rm = TRUE)
newlist <- data.frame(Country = country,
Year = year,
Dimension = dim,
Sex = sex,
Age = age,
Num_Of_People = sumForThis)
newOthersDF <- bind_rows(newOthersDF, newlist)
} #End age if statement
} #End age for loop
} #End sex if statement
} #End sex for loop
} #End dim if statement
} #End dim for loop
} #End year if statement
} #End year for loop
} #End country if statement
} #End country for loop
#This will get rid of the NA row
newOthersDF <- newOthersDF[-1,]
finalDFTrafficking <- data.frame(Country = c(newOthersDF$Country, totals$Country),
Year = c(newOthersDF$Year, totals$Year),
Dimension = c(newOthersDF$Dimension,
totals$Dimension),
Sex = c(newOthersDF$Sex, totals$Sex),
Age = c(newOthersDF$Age, totals$Age),
Num_Of_People = c(newOthersDF$Num_Of_People,
totals$txtVALUE))
Summary Statistics
The variables were age and gender. The categories for age were 0 to
17, and 18 or older. The categories for gender were Males, and Females.
Combinations of these showed counts, indicating the number of people who
fit in those categories.
Statistics for gender
Females are much more often victims of trafficking than males.
options(scipen=999)
new <- finalDFTrafficking |> filter(Sex == "Female")
females <- sum(new$Num_Of_People, na.rm = TRUE)
new2 <- finalDFTrafficking |> filter(Sex == "Male")
males <- sum(new2$Num_Of_People, na.rm = TRUE)
df <- data.frame(sex = c("females", "males"),
count = c(females, males))
ggplot(df, aes(x=sex, y=count)) +
geom_bar(stat = "identity") +
labs(title = "Comparison of Victims by Gender")
Statistics for age
People over 18 years old are much more often victims of trafficking
than people younger than 18.
new <- finalDFTrafficking |> filter(Age == "0 to 17 years")
less_than_18 <- sum(new$Num_Of_People, na.rm = TRUE)
new2 <- finalDFTrafficking |> filter(Age == "18 years or over")
eighteen_plus <- sum(new2$Num_Of_People, na.rm = TRUE)
df <- data.frame(age = c("0 to 17 years", "18 years or more"),
count = c(less_than_18, eighteen_plus))
ggplot(df, aes(x=age, y=count)) +
geom_bar(stat = "identity") +
labs(title = "Comparison of Victims by Age")
Statistics for Gender Plus
Age
Age seems to be a more important factor. Females are more often
victims than males, but males 18 or older are more often victims than
females 17 or younger.
new <- finalDFTrafficking |> filter(Age == "0 to 17 years") |> filter(Sex == "Male")
less_than_18_male <- sum(new$Num_Of_People, na.rm = TRUE)
new1 <- finalDFTrafficking |> filter(Age == "0 to 17 years") |> filter(Sex == "Female")
less_than_18_female <- sum(new1$Num_Of_People, na.rm = TRUE)
new2 <- finalDFTrafficking |> filter(Age == "18 years or over") |> filter(Sex == "Male")
eighteen_plus_male <- sum(new2$Num_Of_People, na.rm = TRUE)
new3 <- finalDFTrafficking |> filter(Age == "18 years or over") |> filter(Sex == "Female")
eighteen_plus_female <- sum(new3$Num_Of_People, na.rm = TRUE)
df <- data.frame(category = c("17 or less + Males", "17 or less + Females",
"More than 18 + Males", "More than 18 + Females"),
count = c(less_than_18_male, less_than_18_female,
eighteen_plus_male, eighteen_plus_female))
ggplot(df, aes(x=category, y=count)) +
geom_bar(stat = "identity") +
labs(title = "Comparison of Victims by Gender and Age")
Victims By Country
The following maps show the number of people in each country in human
trafficking. The different graphs show different categories of people,
and the totals are all people over all the years.
The last graph shows the highest numbers across the globe, and that
graph shows females 18 or older.
countries <- unique(na.omit(finalDFTrafficking$Country))
otherDF <- c()
for (country in countries)
{
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "0 to 17 years") |> filter(Sex == "Male")
males_17_minus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "0 to 17 years") |> filter(Sex == "Female")
females_17_minus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "18 years or over") |> filter(Sex == "Male")
males_18_plus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "18 years or over") |> filter(Sex == "Female")
females_18_plus <- sum(temp$Num_Of_People, na.rm = TRUE)
newlist <- data.frame(Country = c(country),
males_17_or_less = c(males_17_minus),
females_17_or_less = c(females_17_minus),
males_18_plus = c(males_18_plus),
females_18_plus = c(females_18_plus))
otherDF <- bind_rows(otherDF, newlist)
}
otherDF$Country <- gsub("United States of America", 'USA', otherDF$Country, useBytes = TRUE)
library(maps)
##
## Attaching package: 'maps'
## The following object is masked from 'package:plyr':
##
## ozone
world_map <- map_data(map = "world")
world_map$region <- gsub("USA", "USA", world_map$region)
ggplot(otherDF) +
geom_map(aes(map_id = countries, fill = males_17_or_less), map = world_map) +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), colour = 'black', fill = NA) +
expand_limits(x = world_map$long, y = world_map$lat) +
scale_fill_viridis_c(option = "magma") +
theme_void() +
coord_fixed() +
labs(title = "Number of Male Victims 17 or Younger by Country")
ggplot(otherDF) +
geom_map(aes(map_id = countries, fill = females_17_or_less), map = world_map) +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), colour = 'black', fill = NA) +
expand_limits(x = world_map$long, y = world_map$lat) +
scale_fill_viridis_c(option = "magma") +
theme_void() +
coord_fixed() +
labs(title = "Number of Female Victims 17 or Younger by Country")
ggplot(otherDF) +
geom_map(aes(map_id = countries, fill = males_18_plus), map = world_map) +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), colour = 'black', fill = NA) +
expand_limits(x = world_map$long, y = world_map$lat) +
scale_fill_viridis_c(option = "magma") +
theme_void() +
coord_fixed() +
labs(title = "Number of Male Victims 18 or Older by Country")
ggplot(otherDF) +
geom_map(aes(map_id = countries, fill = females_18_plus), map = world_map) +
geom_polygon(data = world_map, aes(x = long, y = lat, group = group), colour = 'black', fill = NA) +
expand_limits(x = world_map$long, y = world_map$lat) +
scale_fill_viridis_c(option = "magma") +
theme_void() +
coord_fixed() +
labs(title = "Number of Female Victims 18 or Older by Country")
Visuals of Trends
In general, the graphs show increases in numbers as the years
pass.
Top countries by continent: United States, Argentina, Nigeria, UK
(Great Britain and Northern Ireland), and the Philippines.
Countries with the highest numbers by group:
Males 17 or younger: United States of America and
Nigeria
Males 18 or older: United States of America and
Argentina
Females 17 or younger: UK and Nigeria (United States
possibly)
Females 18 or older: Nigeria and Argentina
Note: The United States did not show each group broken up, so the
data ends before 2020 for 3 of 4 graphs.
# For all countries, not easy to distinguish
ggplot(finalDFTrafficking, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 9 rows containing missing values (`geom_line()`).
# For countries with the highest numbers
topCountries <-
finalDFTrafficking |>
filter((Country == "Canada") | (Country == "Argentina") | (Country == "Brazil") |
(Country == "Peru") | (Country == "Mexico") | (Country == "United States of America") |
(Country == "Nigeria") | (Country == "Ghana") |
(Country == "Burkina Faso") | (Country == "Egypt") | (Country == "Madagascar") |
(Country == "Thailand") | (Country == "Nepal") | (Country == "India") |
(Country == "Myanmar") | (Country == "Bangladesh") | (Country == "Uzbekistan") |
(Country == "Italy") | (Country == "France") | (Country == "Germany") |
(Country == "Czechia") | (Country == "Spain") | (Country == "Belarus") |
(Country == "Ukraine") | (Country == "Romania") | (Country == "Bulgaria") |
(Country == "United Kingdom of Great Britain and Northern Ireland") | (Country == "Philippines") |
(Country == "Netherlands") | (Country == "Russian Federation") | (Country == "Malaysia"))
northAmerican <-
finalDFTrafficking |>
filter((Country == "Canada") | (Country == "Mexico") | (Country == "United States of America"))
southAmerican <-
finalDFTrafficking |>
filter((Country == "Argentina") | (Country == "Brazil") |
(Country == "Peru"))
african <-
finalDFTrafficking |>
filter((Country == "Nigeria") | (Country == "Ghana") |
(Country == "Burkina Faso") | (Country == "Egypt") | (Country == "Madagascar"))
european <-
finalDFTrafficking |>
filter((Country == "Italy") | (Country == "France") | (Country == "Germany") |
(Country == "Czechia") | (Country == "Spain") | (Country == "Belarus") |
(Country == "Ukraine") | (Country == "Romania") | (Country == "Bulgaria") |
(Country == "United Kingdom of Great Britain and Northern Ireland") |
(Country == "Netherlands") | (Country == "Russian Federation"))
asian <-
finalDFTrafficking |>
filter((Country == "Thailand") | (Country == "Nepal") | (Country == "India") |
(Country == "Myanmar") | (Country == "Bangladesh") | (Country == "Uzbekistan") |
(Country == "Philippines") | (Country == "Malaysia"))
northA <- ggplot(northAmerican, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
southA <- ggplot(southAmerican, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
africa <- ggplot(african, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
europe <- ggplot(european, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
asia <- ggplot(asian, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
# Prep for next section
countriesOverTime <- unique(na.omit(finalDFTrafficking$Country))
otherDF <- c()
for (country in countries)
{
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "0 to 17 years") |> filter(Sex == "Male")
males_17_minus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "0 to 17 years") |> filter(Sex == "Female")
females_17_minus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "18 years or over") |> filter(Sex == "Male")
males_18_plus <- sum(temp$Num_Of_People, na.rm = TRUE)
temp <- finalDFTrafficking |> filter(Country == country) |> filter(Age == "18 years or over") |> filter(Sex == "Female")
females_18_plus <- sum(temp$Num_Of_People, na.rm = TRUE)
newlist <- data.frame(Country = c(country),
males_17_or_less = c(males_17_minus),
females_17_or_less = c(females_17_minus),
males_18_plus = c(males_18_plus),
females_18_plus = c(females_18_plus))
otherDF <- bind_rows(otherDF, newlist)
}
males_17_minus <-
finalDFTrafficking |>
filter((Country == "United Kingdom of Great Britain and Northern Ireland") |
(Country == "United States of America") |
(Country == "Argentina") | (Country == "Nigeria") |
(Country == "Nepal") | (Country == "Australia")) |>
filter(Sex == "Male") |>
filter(Age == "0 to 17 years")
males_18_plus <-
finalDFTrafficking |>
filter((Country == "United Kingdom of Great Britain and Northern Ireland") |
(Country == "United States of America") |
(Country == "Argentina") | (Country == "Nigeria") |
(Country == "Nepal") | (Country == "Australia")) |>
filter(Sex == "Male") |>
filter(Age == "18 years or over")
females_17_minus <-
finalDFTrafficking |>
filter((Country == "United Kingdom of Great Britain and Northern Ireland") |
(Country == "United States of America") |
(Country == "Argentina") | (Country == "Nigeria") | (Country == "Nepal") |
(Country == "Australia")) |>
filter(Sex == "Female") |>
filter(Age == "0 to 17 years")
females_18_plus <-
finalDFTrafficking |>
filter((Country == "United Kingdom of Great Britain and Northern Ireland") |
(Country == "United States of America") |
(Country == "Argentina") | (Country == "Nigeria") | (Country == "Nepal") |
(Country == "Australia")) |>
filter(Sex == "Female") |>
filter(Age == "18 years or over")
# Compare countries
ggplot(males_17_minus, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "Male Victims 17 or Younger Over Time in Top Countries")
## Warning: Removed 1 row containing missing values (`geom_line()`).
ggplot(males_18_plus, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "Male Victims 18 or Older Over Time in Top Countries")
## Warning: Removed 2 rows containing missing values (`geom_line()`).
ggplot(females_17_minus, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "Female Victims 17 or Younger Over Time in Top Countries")
## Warning: Removed 2 rows containing missing values (`geom_line()`).
ggplot(females_18_plus, aes(x = Year, y= Num_Of_People, color = Country)) +
geom_line(size = 0.5) +
ggtitle("Trafficking over time") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "Female Victims 18 or Older Over Time in Top Countries")
## Warning: Removed 2 rows containing missing values (`geom_line()`).
# Compare ages and genders
uk <-
finalDFTrafficking |>
filter(Country == "United Kingdom of Great Britain and Northern Ireland") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
usa <-
finalDFTrafficking |>
filter(Country == "United States of America") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
argentina <-
finalDFTrafficking |>
filter(Country == "Argentina") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
nigeria <-
finalDFTrafficking |>
filter(Country == "Nigeria") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
nepal <-
finalDFTrafficking |>
filter(Country == "Nepal") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
australia <-
finalDFTrafficking |>
filter(Country == "Australia") |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
Some interesting findings:
In most countries, people ages 18 or older were trafficked the most.
However, in Nigeria, males 17 or younger were victims more often than
males 18 or older.
In Nepal, the numbers of victims dramatically increased in 2020.
In Australia, the number of victims ages 18 or older increased about
5 times the amount from 2015 to 2020.
uk1 <- ggplot(uk |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in the UK (Britain and Ireland)") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
uk2 <- ggplot(uk |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in the UK (Britain and Ireland)") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
us1 <- ggplot(usa |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in the U.S.") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
us2 <- ggplot(usa |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in the U.S.") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
arg1 <- ggplot(argentina |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in Argentina") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
arg2 <- ggplot(argentina |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in Argentina") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(nigeria |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in Nigeria") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(nigeria |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in Nigeria") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(nepal |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in Nepal") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(nepal |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in Nepal") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(australia |> filter(Sex == "Male"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Males Over Time by Age in Australia") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(australia |> filter(Sex == "Female"), aes(x = Year, y= Num_Of_People, color = Age)) +
geom_line(size = 0.5) +
ggtitle("Trafficking of Females Over Time by Age in Australia") +
labs( x = "Year", y = "Number of Victims" ) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
Statistical Tests
T-tests
Linear Regression
T-test - Comparing Males
and Females
Null Hypothesis: Males and females are victims of human trafficking
in equal numbers.
Alternate Hypothesis: Males and females are victims of human
trafficking in different amounts.
Results of Gender T-test
P-value: < 0.05
Result: Reject the null and accept the alternate hypothesis. Based on
the means, females are victims significantly more than males.
T-test -
Comparing 17 and Younger to 18 and Older
Null Hypothesis: People ages 17 or less and people ages 18 or more
are victims of human trafficking in equal numbers.
Alternate Hypothesis: People ages 17 or less and people ages 18 or
more are victims of human trafficking in different amounts.
Results of Age T-test
P-value: < 0.05
Result: Reject the null and accept the alternate hypothesis. Based on
the means, people ages 18 or more are victims significantly more than
people ages 17 or less.
Code for T-tests
# Compare Male vs Female
bothGenders <- finalDFTrafficking |> filter((Sex == "Male") | (Sex == "Female"))
t.test(Num_Of_People ~ Sex, data = bothGenders)
##
## Welch Two Sample t-test
##
## data: Num_Of_People by Sex
## t = 13.46, df = 8197, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## 29.79041 39.94651
## sample estimates:
## mean in group Female mean in group Male
## 72.99109 38.12262
# Compare 17 or less vs 18 or more
separateAges <- finalDFTrafficking |> filter((Age == "0 to 17 years") | (Age == "18 years or over"))
t.test(Num_Of_People ~ Age, data = separateAges)
##
## Welch Two Sample t-test
##
## data: Num_Of_People by Age
## t = -15.437, df = 8808.2, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means between group 0 to 17 years and group 18 years or over is not equal to 0
## 95 percent confidence interval:
## -41.07578 -31.81923
## sample estimates:
## mean in group 0 to 17 years mean in group 18 years or over
## 35.44063 71.88813
Linear Regression - All
Countries
In the first model, all countries are used based on gender, age, and
year. This model is an equation that should predict the average number
of people in trafficking for all countries of the world. Since countries
have many differences, this model was expected to be inaccurate. Many
other factors than gender, age, and year affect the number of victims
for all those countries.
Results: The R squared value of 0.04019. This shows the model is
horrible at predicting outcomes since it is extremely close to 0. It
does not take into account the differences between countries.
# Based on age, gender, and year
finalDFTrafficking$yearsFrom2003 <- finalDFTrafficking$Year - 2003
bothSets <-
finalDFTrafficking |>
filter((Sex == "Male") | (Sex == "Female")) |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
fitAll <- lm(Num_Of_People ~ Sex + Age + yearsFrom2003, data = bothSets)
summary(fitAll)
##
## Call:
## lm(formula = Num_Of_People ~ Sex + Age + yearsFrom2003, data = bothSets)
##
## Residuals:
## Min 1Q Median 3Q Max
## -80.74 -44.98 -30.00 -6.36 1395.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.0232 5.2195 6.135 0.000000000899 ***
## SexMale -28.8344 2.6818 -10.752 < 0.0000000000000002 ***
## Age18 years or over 33.8895 2.6820 12.636 < 0.0000000000000002 ***
## yearsFrom2003 0.9351 0.3646 2.565 0.0103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109.9 on 6716 degrees of freedom
## (56 observations deleted due to missingness)
## Multiple R-squared: 0.04019, Adjusted R-squared: 0.03977
## F-statistic: 93.75 on 3 and 6716 DF, p-value: < 0.00000000000000022
The equation created from the regression:
y = 32.0232 - 28.8344(male) + 33.8895(18 or over) + 0.9351(years from
2003)
Guess the totals for the following scenarios:
- Males 18 years or more in the year 2023
32.0232 - 28.8344 + 33.8895 + (0.9351 * 20)
## [1] 55.7803
- Females 17 years or less in the year 2025
32.0232 - (28.8344 * 0) + (33.8895 * 0) + (0.9351 * 22)
## [1] 52.5954
- Males 17 years or less in the year 2025
32.0232 - 28.8344 + (33.8895 * 0) + (0.9351 * 22)
## [1] 23.761
- Females 18 years or more in the year 2023
32.0232 - (28.8344 * 0) + 33.8895 + (0.9351 * 20)
## [1] 84.6147
Findings:
In each of the scenarios, the estimate is a guess for the average
number of people in human trafficking. It would be like looking at one
country. If those numbers were correct, imagine those numbers times the
number of countries in the world (about 195). Population size of a
country, issues with law enforcement, poverty rates, and more along with
the factors considered here would affect the number. Also, remember that
the number of people known to be victims of human trafficking is lower
than the true numbers. We cannot know the actual number because many
people who are victims are unknown and not accounted for in the
total.
Linear Regression - United
States
In this other linear regression, the predictions are repeated for
only the United States.
Results: The R squared value is now 0.4143. This shows the model is
still not a very strong predictor, but it has much more predictive value
than the previous model created.
# Based on age, gender, and year
finalDFTrafficking$yearsFrom2003 <- finalDFTrafficking$Year - 2003
bothSets3 <-
finalDFTrafficking |>
filter(Country == "United States of America") |>
filter((Sex == "Male") | (Sex == "Female")) |>
filter((Age == "0 to 17 years") | (Age == "18 years or over"))
fitAll2 <- lm(Num_Of_People ~ Sex + Age + yearsFrom2003, data = bothSets3)
summary(fitAll2)
##
## Call:
## lm(formula = Num_Of_People ~ Sex + Age + yearsFrom2003, data = bothSets3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -606.75 -108.79 -18.15 109.91 726.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -88.74 159.95 -0.555 0.5818
## SexMale -170.15 81.82 -2.079 0.0434 *
## Age18 years or over 379.22 81.16 4.672 0.0000282 ***
## yearsFrom2003 28.69 11.11 2.583 0.0132 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 276.8 on 44 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.4143, Adjusted R-squared: 0.3744
## F-statistic: 10.38 on 3 and 44 DF, p-value: 0.00002757
The equation created from the regression:
y = -88.74 - 170.15(male) + 379.22(18 or over) + 28.69(years from
2003)
Guess the totals for the following scenarios:
- Males 18 years or more in the year 2023
-88.74 - 170.15 + 379.22 + (28.69 * 20)
## [1] 694.13
- Females 17 years or less in the year 2025
-88.74 - (170.15 * 0) + (379.22 * 0) + (28.69 * 22)
## [1] 542.44
- Males 17 years or less in the year 2025
-88.74 - 170.15 + (379.22 * 0) + (28.69 * 22)
## [1] 372.29
- Females 18 years or more in the year 2023
-88.74 - (170.15 * 0) + 379.22 + (28.69 * 20)
## [1] 864.28
Findings:
In each of those four scenarios, the estimate is a guess for the
number of people in human trafficking in only the United States. It is
much closer to the actual numbers based on the R squared value. Data for
2023 was not published to the website yet, and data for 2025 will not be
available for at least a few years.
Conclusion
Based on my findings, females and people older than 18 years old are
at the highest risk of being victims of human trafficking. In the United
States, my model predicted that about 28 to 29 new people become victims
every year. This estimate is less than the true number, but it
demonstrates the average increase over time. Based on many graphs,
numbers have increased more rapidly in recent years. The analysis
performed on this data is important because it shows the extent of the
issue, and it shows how different groups of people are affected
differently. I also hope this encourages you to be aware of this problem
so you can take action, whether that includes being aware of your
surroundings if this is happening around you or joining the cause to
bring an end to trafficking. Lastly, a lot of traffickers are caught,
but the victims are still enslaved. If you are in the field of law or
hospitality or health care, it is important to be aware of this so you
can make change. You have the power to save people.
One limitation is that not all the data was collected. Many more
people are in trafficking than we know because the traffickers want to
keep it secret. They do not want people to know about what they are
doing. It is bad for business, as awful as that sounds, and they don’t
want to go to jail or be prosecuted in any way. Another limitation is
that the data was not collected the same for all countries. Some places
showed information by age. Some showed information by gender. Some
showed information by the type of trafficking - forced labor or
commercial sex. As a result, some of the comparisons could not be done
for all countries.