Welcome to Hallyu Tracker
Hallyu Tracker - is a free, open-access platform built to integrate Korean Wave data from diverse stakeholders.
How to Use the Hallyu Tracker
Focus: Start exploring by choosing a dedicated Data Cluster and its corresponding Index from the main menu on the left.
Explore Global Map: View global trends on the country level where the intensity of a country's color highlights higher values across various indicators. Analyze 14 key global cities with high Hallyu circulation, where the size of each icon reflects that city's index value for a specific variable.
Inspect Country Details: Click on any country to open a detailed menu on the right. Here, you can examine its specific Reach, Appeal, or Engagement Indexes, along with the underlying raw data of those indicators. Each country receives a normalised index score (0–100) based on weighted indicators, enabling global comparison.
Compare Regions: To contrast multiple countries across specific variables, simply click the pin icon to lock the data menu on your screen.
Track Trends Over Time: Use the timeline slider at the bottom of the screen to move between historical milestones and watch how the data changes across the years.
Run Advanced Analytics: On the top left Menu, use the Analytics function to select specific countries, regions, or indicators. This powerful tool helps you instantly find statistical correlations, extremes, and benchmarking data, uncovering hidden structural patterns that are difficult to see by toggling layers alone.
The Data Landscape
The Hallyu Tracker draws on a diverse data ecosystem, aggregating information from 4 types of sources. All data is fully annotated with source attributions within the application, accessible via mouseover on each data layer.
To prevent data-poor regions from appearing as blank spaces, the Hallyu Tracker uses AI-enabled imputation. Missing values are clearly categorised and visually marked.
Predicted Value: Modelled via Linear Regression when R2 is sufficient.
Rolling Value: Carried over from the most recent year when R2 is weak.
Real Value: Verified data extracted directly from original sources.
Data Intelligence
The Hallyu Tracker's analytical power comes from its dense, multi-layered datasets. Toggle, overlay, and contrast heterogeneous sources across three thematic clusters.
Reach Power — Infrastructure & Export Capacity
Examines both sides of Hallyu's global presence: local infrastructure and consumption capacity, alongside South Korea's export efforts.
22 Data Layers:
- Reach Power Index - Potential for Hallyu engagement (infrastructure + population capacity)
- Target Power Index - South Korean creative imports (Game, Animation, Film, Music, Publishing, Broadcasting, Cultural Goods)
Appeal Power — Cultural Proximity & Strategic Promotion
Maps Hallyu's potential appeal based on cultural/transnational proximity and strategic promotion efforts from Korea.
52 Data Layers:
- National Appeal Index - Population exposure + proximity to Korean social norms
- Universal Appeal Index - International inclusion, digital networks, global exposure, diversity
- Target Appeal Index - Korean industry/government promotion (localisation strategies)
Engagement Power — Audience Behaviour & Sentiment
Maps digital and physical consumption, circulation, and attitudes toward Hallyu.
44 Data Layers:
- Political Engagement - Geopolitical context, liberal values, bilateral ties with South Korea
- Hallyu Engagement - Digital/physical fandom behaviours + media coverage
- Anti-Hallyu - Anti-Korean sentiment, negative media, anti-Hallyu groups
Weights are clearly indicated on each indicator and accessible via mouseover on weighted data layers—ensuring the process remains open to scrutiny and replication.
Data Analytics
The platform is being engineered with enhanced feature engineering and data visualisation capabilities, reducing cognitive overload while surfacing hidden patterns. This collaboration is structured around two core techniques. First, Relationship Mapping uses Pearson correlation to statistically link trends across countries. Second, K-Means Clustering performs behavioural segmentation, automatically grouping nations by shared consumption patterns.
Relationship Mapping
- Pearson correlation links trends across countries
- Identifies similar dynamics beyond geography
- Enables confident knowledge transfer
K-Means Clustering
- Behavioural segmentation groups countries by shared patterns
- Reveals natural peer clusters
- Instantly spot leaders and laggards
Three Analytics Tools
Correlations
- Discover relationships across indicators
- Identify long-term patterns
Extremes
- Isolate statistical outliers
- Highlight highest/lowest values
Benchmarking
- Compare against peers
- Benchmark regionally or globally