Mapping the Global Supply Chain Graph
Mapping the Global Supply Chain Graph


Panjiva maps the network of global trade with unprecedented scope and granularity. Using over one billion shipping records sourced from governments around the world, we perform large-scale entity extraction and entity resolution, identifying trading relationships among millions of companies and thousands of ports, located across the world. We have developed a powerful platform facilitating search, analysis, and visualization of this network as well as a data feed integrated into S&P Global's Xpressfeed platform.

Placing supply chain networks in the familiar setting of a world map lends context that facilitates understanding of stories contained in the data. The web browser is an apt setting for creating interactive maps and geospatial applications due to the powerful inbuilt visualization and computation tools and the rich ecosystem of compatible open source libraries. Through the process of developing interactive web maps, we will learn about open source technologies for analyzing and interacting with data and explore patterns in the flow of goods between geographic areas in the global supply chain graph.

Participants will learn how to create a data driven application using React, a JavaScript web application framework. In this application, we will create interactive maps using graphics technologies SVG (scalable vector graphics) and WebGL (web graphics library) with the help of open source JavaScript libraries including D3, mapbox, and


Robert Christie is a Front End Engineer at Panjiva, a division of S&P Global Market Intelligence. He specializes in interactive data visualization and cartography for the web and has a background in statistics and spatial analysis. Much of Robert's work has been in the domain of transportation, mobility, and logistics. He is passionate about the role of visualization in increasing the comprehensibility and observability of machine learning driven decision making. Robert received a B.A. from the McGill School of Environment and a Masters from the University of Toronto School of Information.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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