Mapping the Rails

Unraveling Investment and Usage in the Global Rail Networks

Zhi Ray Wang, MIT Spring 2025

zrwang@mit.edu

Dataset Overview

In this research, I worked with the Human Development Indicators dataset 1960-2020, which was curated by the World Bank, offers clean, formatted indicators on various aspects of global human development. For my analysis, I focused on metrics related to rail transport, including total railway length, goods transported (measured in million ton-kilometers), and passengers carried (in million passenger-kilometers), along with investment figures. This focused view allows me to delve into the interplay between physical infrastructure and financial inputs in the rail sector.

Using this comprehensive dataset, I was able to compare operational performance with capital flows across countries. The metadata accompanying the dataset provided essential context, clarifying how each indicator is defined and its source. This context is critical because, while the data appears neutral at first glance, the way it is presented can significantly influence the conclusions drawn about infrastructure efficiency and investment strategies. For me, the dataset serves as both a source of quantitative insight and a reminder of the ethical responsibility in visualizing complex data.

Research Statement

My central proposition is that increased investment—especially through private channels and public-private partnerships—drives higher efficiency in rail usage. In this view, countries that attract more capital for rail infrastructure tend to show greater throughput, measured by freight and passenger traffic per kilometer of track. I believe that modernized networks, supported by robust financial backing, are more likely to operate at peak efficiency, maximizing the utility of each unit of infrastructure.

However, I also recognize that the data can be interpreted in multiple ways. It is entirely possible to argue that high usage rates sometimes emerge independently of investment levels, driven instead by factors such as population density, historical development, or even geographic constraints. This duality is reflected in my visualizations, which I designed to both support and challenge the notion that investment alone is the key driver of rail efficiency. In doing so, I strive to demonstrate that no single narrative captures the full complexity of global rail dynamics.

Aggregate Railway Network Length by Country (Kilometers), Source: Author

Private Investment in Transport (Current US$), Source: Author

Public-Private Partnerships Investment in Transport (Current US$), Source: Author

Global Railway Length & Investment Comparison

In comparing the physical extent of rail networks with transport investments, I observed that larger networks do not always secure higher levels of investment. Some countries with expansive railway systems seem to attract only modest private or PPP funding, suggesting that factors beyond sheer network length influence capital flows. My treemap visualizations vividly depict this disconnect, using color and size to emphasize cases where investment lags behind the physical scale of the network.

By carefully adjusting the visual scales and filters, I highlighted outliers where investment and network length diverge markedly. This comparison prompts a critical question: are investors more inclined to support smaller, high-potential networks rather than larger, sprawling systems? The data suggests that while network size is an important factor, strategic investment decisions are also shaped by local economic conditions and policy environments. This insight challenges the assumption that more extensive networks naturally warrant proportionately higher investment.

Ranking of Countries by Total Goods Transport (Million Ton-Kilometers), Source: Author

Ranking of Countries by Total Passenger Movement (Million Passenger-Kilometers), Source: Author

Ranking of Freight and Passenger Pattern

I then turned my attention to the raw patterns of freight and passenger transport, ranking countries by their total ton-kilometers of goods moved and million passenger-kilometers carried. The freight ranking chart reveals that a small group of nations dominate in cargo movement, reflecting their industrial capacities and export-driven economies. In contrast, the passenger ranking points to a different narrative, where high numbers indicate well-developed commuter systems or densely populated urban centers.

These patterns, when juxtaposed, highlight the multifaceted nature of rail usage. It became clear that while some countries excel in moving large volumes of goods, others invest more in providing efficient passenger services. This divergence suggests that policy makers and investors need to consider these different usage profiles when planning future infrastructure investments. My visualizations here serve as a tool to prompt a deeper inquiry into whether rail investment should be tailored more specifically to the type of demand—freight versus passenger—that a country experiences.

Freight Efficiency Index: Ton-Kilometers per Kilometer of Rail Network, Source: Author

Passenger Efficiency Index: Passenger-Kilometers per Kilometer of Rail Network, Source: Author

Efficiency Index: Freight and Passenger per Length

To gauge efficiency, I computed normalized metrics: freight per route kilometer and passenger per route kilometer. By dividing total ton-kilometers or passenger-kilometers by the total length of the rail network, I obtained a measure of usage intensity that levels the playing field among countries with different network sizes. These derived ratios reveal which nations are getting the most out of every kilometer of track, regardless of the overall scale of their infrastructure.

The normalized metrics tell a story that differs from the raw totals. For example, a country with a compact but heavily used rail system may rank much higher on a per-unit basis than a nation with an extensive network that is underutilized. This nuanced perspective forces me—and the audience—to rethink what it means to be “efficient” in rail transport. It challenges the simplistic notion that more is always better, emphasizing instead the value of optimizing existing assets.

Global Distribution of Freight: Ton-Kilometers per Rail Route Kilometer, Source: Author

Global Distribution of Passenger-Kilometers per Rail Route Kilometer, Source: Author

Mapping the Global Investment and Usage Patterns

To add a geographic dimension to my analysis, I created two world maps: one displaying passenger per route kilometer (shaded in pink) and the other showing freight per route kilometer (in green). These maps revealed distinct regional patterns, with certain areas, such as parts of East Asia and Europe, standing out for their high usage intensity. This spatial perspective provides an immediate visual context for understanding how rail efficiency varies across the globe.

When I overlaid these maps with my investment treemaps, I observed interesting correlations—and some notable exceptions. In several cases, regions with high usage intensity also received robust private or PPP investments, suggesting that capital inflows can indeed stimulate higher efficiency. However, other areas with high intensity had relatively low investment, raising questions about the factors driving their performance. To further underscore this, I designed two contrasting visualization sets: one that persuades the viewer that increased investment is the primary driver of efficiency, and another that challenges this view by highlighting outliers and alternative explanations.

Correlation of Freight Efficiency with Private Investment, Source: Author

Correlation of Passenger Efficiency with Combined Private and PPP Investment, Source: Author

Conclusion: Correlation of Transport Investment and Efficiency

In my final visualizations, I used two key plots—one comparing freight per route kilometer with private investment and another contrasting passenger per route kilometer with combined private and Public-Private Partnership investments. By looking at these scatter plots, it became clear how a single dataset can yield conflicting interpretations:

High Investment & High Usage:

For freight, China stands out with both substantial private investment (around 1000 million USD) and the highest freight usage (roughly 23 ton-km per route km). On the passenger side, China also shows significant PPP investment, coupled with a strong (though not the absolute highest) passenger usage. This could be framed to support the proposition that bigger capital inflows drive greater rail efficiency.

Moderate Investment & Surprising Efficiency:

Mexico invests around 200 million USD in freight yet achieves a respectable ~10 ton-km per route km, suggesting it can do more with less compared to countries investing larger sums. Myanmar shows a similar pattern for passengers, with very low investment (under 20 million USD) but unexpectedly high passenger usage (~9 passenger-km per route km). Such cases might challenge the idea that investment is the sole driver of performance. On the other hand, this might potentially be red-flagged as an overloaded system and requires more investments.

High Investment & Low Usage:

Brazil allocates roughly 750 million USD in private freight investment but posts only ~1 ton-km per route km—raising questions about whether this funding is being efficiently deployed. On the passenger side, Turkey and others show non-trivial investments yet modest usage, which could refute the notion that money alone guarantees high performance.

Balanced Approach:

India offers an interesting middle ground in both plots: moderate-to-high investment levels (~600 million USD) and fairly strong usage (around 3 ton-km for freight, ~10 passenger-km for passengers). This balance can be interpreted as a sustainable synergy, or it could also be read as a sign that India’s vast network might still be under strain, given its enormous population and the demands placed on its rail infrastructure.

Reflection

These contrasting data points illustrate how we can craft two opposing narratives from the same dataset. In one version, I highlight China’s success and de-emphasize countries like Brazil or Myanmar to argue that capital investments strongly correlate with rail efficiency. In another, I emphasize on outliers—Mexico and Myanmar, for instance—to demonstrate that even low-investment systems can achieve decent or even robust usage.

Every design choice—from axis ranges to which countries we label—can subtly reshape the viewer’s perception of whether investment truly drives performance. Recognizing and disclosing these decisions is key to maintaining credibility, especially in politically charged domains like infrastructure development.