DeepSeek R1: Mapping the World’s Most Visited Cities

DeepSeek R1: Mapping the World's Most Visited Cities

DeepSeek R1, a large language model (LLM) gaining prominence as a potential competitor to OpenAI's ChatGPT, was recently tested on a simple yet insightful task: plotting the 20 most visited cities in the world on an interactive map. The experiment aimed to evaluate DeepSeek R1's capabilities in data retrieval, reasoning, and code generation.

Task Definition

The task involved instructing DeepSeek R1 to generate a Python script, suitable for execution in a Google Colab environment, that would:

1. Identify the 20 most visited cities globally.
2. Obtain their respective latitudes and longitudes.
3. Generate an interactive map using the Folium library, displaying these cities as markers.
4. Enable pop-up information on each marker, revealing the city's name, rank, and visitor count upon clicking.

Crucially, the web browsing option within DeepSeek R1 was enabled, allowing the model to access and process real-time information from the internet.

DeepSeek's Reasoning

One notable feature of DeepSeek R1 is its reasoning model, providing a glimpse into the decision-making process. During the task, the model demonstrated its ability to identify and resolve conflicting information obtained from web searches, ultimately arriving at a well-reasoned conclusion.

Implementation and Results

DeepSeek R1 successfully generated the required Python code, which was then executed in a Google Colab environment. The model accurately identified the top 20 most visited cities, retrieved their geographical coordinates, and created an interactive map. The pop-up information on each marker functioned as intended, displaying the city's name, rank, and visitor count.

Furthermore, DeepSeek R1 exhibited an understanding of context by including the year to which the visitor data corresponded, even though it was not explicitly requested. This demonstrates a capacity for nuanced comprehension and proactive information inclusion.

Enhancements and Refinements

To further enhance the map's readability, a subsequent instruction was given to color-code the markers based on city rank. The top five cities were colored red, the next five blue, followed by green and yellow for the remaining cities. DeepSeek R1 successfully implemented this modification, creating a visually informative map that instantly conveyed the relative ranking of the most visited cities.

Implications and Comparisons

This experiment highlights the potential of LLMs like DeepSeek R1 to automate complex tasks involving data retrieval, processing, and visualization. Previously, assembling such a map would require manual data collection and coding. Now, it can be achieved with a single prompt.

While this specific task may not be overly complex, it serves as a foundation for comparing the performance of different LLMs, such as DeepSeek R1 and ChatGPT. As tasks become more challenging, the strengths and weaknesses of each model become more apparent, influencing their suitability for various applications.

Which model will ultimately provide a more user-friendly and efficient experience? The answer likely lies in continued experimentation and real-world application.

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