I built a graph visualization of the Midwest food supply chain from 51 public datasets [OC]

What This Visualization Shows

This data visualization displays "I built a graph visualization of the Midwest food supply chain from 51 public datasets [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on [https://lodgeplatform.com/embed/exemb\_6ea24564bc317d3b45c31b8803fce86229be8ad30246ac21](https://lodgeplatform.com/embed/exemb_6ea24564bc317d3b45c31b8803fce86229be8ad30246ac21)

I used a combination of fine-tuned Qwen3 models and frontier LLMs to build a graph over the Midwest food supply chain. It uses 51 public datasets across 11 source families, covering nearly 300,000 source records. You can search for entities, filter by entity type and relationship type, and you can also view neighborhoods. I'm still improving it, so please let me know if there's anything else you think would be interesting!

I wanted to try to answer questions like

\- "When a serious safety incident occurs at one food facility, can we trace the graph to find other facilities with similar safety risks?

\- "When a food product is recalled, can we trace the recalling company to its facilities, related products, inspections, and other connected organizations and see what else may warrant review?"

\- "When a weather event happens, what facilities does it effect, and what are the downstream effects?"

I think making it live could be really cool for things like tracking food recalls live to show and limit exposure.

Here is a github of the project with the datasets and more info: [https://github.com/lodge-data/upper-midwest-food-supply-network](https://github.com/lodge-data/upper-midwest-food-supply-network)

How I did it:

First, I trained an embedder to minimize [blocking](https://moj-analytical-services.github.io/splink/topic_guides/blocking/blocking_rules.html) recall. Then, I processed all the entities from the datasets into blocks. Then, I trained a Qwen3 cross-encoder to decide if each pair within each block was the same or different (entity resolution). Then, I repeated those two steps until no new merges occurred.

Sources:

\- U.S. Food and Drug Administration recall, food, import-alert, and import-refusal records.

\- U.S. Department of Agriculture food, organic-operation, meat-establishment, and related facility records.

\- Occupational Safety and Health Administration inspection, citation, injury, and establishment records.

\- U.S. Environmental Protection Agency Facility Registry Service records.

\- National Weather Service alerts and geographic references.

\- USAspending federal contract award records.

\- -Wisconsin and other Upper Midwest public facility, licensing, dairy, workforce-notice, and regulatory records.

\- Public organization and product information used to connect named entities.

Note: I built this with my startup's software and models. The goal is to autonomously construct graphs from messy data. I think it is possible to construct very large, useful graphs with efficient specialized models.

, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.

Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.