1 Background and Context

In February 2025, Asia Times published an article discussing the Thai government’s efforts to protect Chinese tourists from Beijing-based gangs involved in scams and kidnappings. These criminal networks were infiltrated through a joint multinational effort by Cambodia, Myanmar, Laos, Thailand, China, and Vietnam, underscoring the significance of raising awareness around fraud, scams, and rip-offs.

Reading that tourism accounts for around 12% of China’s GDP, a country already grappling with “uneven development and income inequality”, made me reflect on how crime can significantly impact the tourism and economic stability of a nation.

While historically lower-crime, tourism-dependent nations like Singapore, Portugal, and Japan do exist, many more popular tourism-dependent nations such as Mexico, Dominican Republic, and Jamaica depend on tourism for their GDP much more heavily—begging the question: Does crime affect tourism, and if so, in what ways?

Through this project and the data I collected, I plan to approach a better understanding of this theme using 4 main questions:

  1. Does a nation’s crime rate impact its international tourism, and if so, what specific types of crime and what about tourism is influenced?
  2. How does crime impact developing nations similarly or differently to developed nations?
  3. How are two common types of crimes (Intentional Homicide and Drug-Related Crime) affiliated with tourism incline and decline?
  4. Are there specific tourism-related crime hotspots in terms of citizen political involvement?

2 Data Collection and Preprocessing Rationales

(Skip if you want to get to visualizations)

Because we are working with country-level data (195 globally recognized nations), I made sure to use reliable sources that provided global coverage. Therefore, I chose the World Bank and its DataBank for all sub-datasets used in this project.

2.1 Data Pipeline

I split the dataset generation into two main steps:

  • Quantitative Dataset
  • Categorical and Quantitative Dataset

Starting with 15 quantitative variables, I used statistical techniques like:

  • Variance Inflation Factor (VIF) analysis
  • Stepwise regression
  • Standardization

…to reduce this to 11 final quantitative variables, while converting 2 into categorical variables using the World Bank’s indices.

2.2 Initial 15 Quantitative Variables with Descriptions

Initial 15 Quantitative Variables
Variable Description
Tourism Percent Total Expenditure Percentage of imports spent by residents abroad.
Tourism Expenditures in USD Total USD spent by residents traveling abroad.
Passenger Transport Items in USD International transport money paid to foreign carriers.
Receipt Travel Items in USD USD received from foreign visitors for lodging, food, etc.
Receipts for Passenger Transport Items in USD USD from transport by home country carriers.
Receipts % of Total Exports Tourism exports as percentage of total exports.
Receipts in USD Total international tourist income (USD).
Number of Departures Total international trips by residents.
Number of Arrivals Total foreign tourists entering the country.
Intentional Homicide Rate Intentional killings per 100,000 people.
Unemployment, total (% of total labor force) Modeled unemployment rate (ILO).
Voice and Accountability: Percentile Rank Freedom of expression and citizen participation score.
Total Drug-Related Incidents Total drug-related crimes (2018–2022).
Male Drug-Related Incidents Drug-related crimes committed by men.
Female Drug-Related Incidents Drug-related crimes committed by women.

2.2.1 Initial 15 Variable Quantitative Table

2.3 Final 11 Quantitative Variables with Descriptions

Final 11 Quantitative Variables
Variable Description
Tourist Transport Spending Avg annual transport spend by residents (2000–2020).
Tourist Non-Transport Earning Avg annual income from foreign visitors (2000–2020).
Tourist Transport Earning Avg annual transport income from visitors (2000–2020).
Percent Export Tourism Tourism-related income as a % of exports.
Income from Tourism Total tourism income (in billions, 2000–2020).
Number of Departures Avg annual resident trips abroad (in millions).
Number of Arrivals Avg annual tourist arrivals (in millions).
Intentional Homicide Rate Avg homicide rate per 100,000 (2000–2020).
Total Drug-Related Incidents Total drug-related crimes (2018–2022).
Male Drug-Related Incidents Drug-related crimes by men (2018–2022).
Female Drug-Related Incidents Drug-related crimes by women (2018–2022).

2.3.1 Final 11 Quantitative Variables Table

After getting the quantitative variables, I had to add in the qualitative variables into the dataset, for which I used 5 qualitative variables.

2.4 5 Qualititative Variables with Descriptions

Categorical Variable Breakdown
Variable Classes
Unemployment Low (0–5%), Moderate (5–9%), High (9–13%), Very High (>13%)
Voice Accountability Very Low (0–21), Low (21–41), Moderate (41–61), High (61–81), Very High (81–100)
Income Group Low Income, Lower Middle Income, Upper Middle Income, Higher Income
Departure Rate Low (0–0.7678), Moderate (0.7678–2.5224), High (2.5224–8.0376), Very High (8.0376+)
Arrival Rate Low (0–1.16255), Moderate (1.16255–4.09208), High (4.09208–8.35633), Very High (8.35633+)

3 Question 1:

3.1 Introduction

Question 1: Does a nation’s crime rate impact its international tourism, and if so, what specific types of crime and what about tourism is influenced?

3.2 Plot 1: Interactive Global Map & Tourism Map (Shiny App)

Shiny applications not supported in static R Markdown documents

3.2.1 Interpretation

3.3 Plot 2: Correlation Heatmap of Tourism and Crime Variables

Toursim Variable Legend
ID Label
1 Tourist_Transport_Spending
2 Tourist_NonTransport_Earning
3 Tourist_Transport_Earning
4 Percent_Export_Tourism
5 Income_from_Tourism
6 Num_Arrivals
7 Num_Departures
Crime Variable Legend
ID Label
1 Drug Related Incidents: Male
2 Drug Related Incidents: Female
3 Drug Related Incidents: Total
4 Homicide Incidents: Homicide Rate

3.3.1 Interpretation

The main takeaway from this plot is that, when comparing the two crime types, Homicide and Drug-Related crimes, Drug-Related crimes tend to show a stronger correlation with tourism-related quantitative variables, as indicated by higher average R-values. Specifically, the five highest correlations in the heatmap reveal that drug-related crimes impact three key areas of tourism: Tourist Transport Earnings, Income from Tourism, and Tourist Transport Spending. This suggests that drug-related crimes are positively correlated with both the revenue and spending on transportation within the tourism sector, as well as the overall income countries generate from tourism. This allows us to assume that in many countries, higher levels of drug-related crime may be linked to increased tourist spending on transport and greater national earnings from tourism, measured in billions of USD. A few possible explanations for this correlation could be increased spending on tourist security in developing nations with higher drug-related crime rates, greater spending in vacation destinations known for accessible drugs or party culture, and the promotion of such locations as tourist attractions where drug-related crime may be more prevalent.

4 Question 2

4.1 Introduction

Question 2: How does crime impact developing nations similarly or differently to developed nations?

4.2 Plot 3: Income Group Comparison (Shiny App)

4.2.1 Interpretation

4.3 Plot 4: Development Level Crime-Tourism Impact (Plotly Plot)

4.3.1 Interpretation

5 Question 3

5.1 Introduction

Question 3: How are two common types of crimes (Intentional Homicide and Drug-Related Crime) affiliated with tourism incline and decline?

5.3 Plot 6: Homicide Rate and Tourism

5.3.1 Interpretation

6 Question 4

6.1 Introduction

Question 4: Are there specific tourism-related crime hotspots in terms of citizen political involvement?

6.2 Plot 7: Voice Accountability, Crime, Tourism

6.2.1 Interpretation

6.3 Plot 8: Tourism Dependency and Political Voice

6.3.1 Interpretation

7 Conclusion

8 Future Work

9 Works Cited