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Mapping the Geography of Products: Generative AI as a New Methodological Frontier

Author

Listed:
  • Milad Abbasiharofteh
  • Hamid Bekamiri

Abstract

This paper argues that generative artificial intelligence (GenAI) represents a methodological turning point in economic geography and regional studies and provides a proof of concept for this transition. Building on successive waves of data collection methods, from surveys and standardized secondary data to web-based data, we show how GenAI can complement existing methods by approximating firms’ product portfolios from unstructured information and linking them to standardized classifications. Moving beyond product mapping, we conclude by discussing a research agenda that addresses broader data and methodological challenges and outlines how GenAI can contribute to a new methodological frontier.

Suggested Citation

  • Milad Abbasiharofteh & Hamid Bekamiri, 2026. "Mapping the Geography of Products: Generative AI as a New Methodological Frontier," Papers in Evolutionary Economic Geography (PEEG) 2616, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Aug 2026.
    • Handle: RePEc:egu:wpaper:2616
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    File URL: http://econ.geo.uu.nl/peeg/peeg2616.pdf
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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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