Automated Technology Foresight for Urban Innovation Ecosystems: A Machine Learning Approach to Real‐Time Startup Detection and Technology Trend Mapping in a Mid‐Sized City

Emmanuel Candido Soriente Santos and Hien Duc Han

Futures & Foresight Science, 2026, vol. 8, issue 1

Abstract: This study examines the spatial distribution and temporal evolution of technology‐driven enterprises in Adelaide, South Australia, from 2019 to 2022, introducing a novel automated foresight methodology that combines natural language processing, machine learning, and geographic visualization. Using web scraping techniques and social media analytics, we analyzed 4001 posts from 856 founder and employee profiles, 20,000 tweets, and 10,000 news articles to map the emergence of technology hotspots across Greater Adelaide. The findings reveal significant clustering patterns in five key technological categories: machine learning and big data analytics, digital health and medical technology, agricultural technology, advanced manufacturing, and renewable energy. Our analysis identifies four primary innovation districts as emerging technology hotspots. The study demonstrates a 40% increase in technology‐related activities between 2019 and 2022, with renewable energy showing the most dramatic growth trajectory. The methodology successfully addresses the critical gap between static policy planning and rapidly evolving startup landscapes, providing policymakers and innovation stakeholders with dynamic, fine‐grained insights into emerging technology clusters and future innovation trajectories. These findings contribute to understanding regional innovation systems and provide a scalable framework for technology foresight in regional innovation ecosystems.

Date: 2026
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https://doi.org/10.1002/ffo2.70026

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Persistent link: https://EconPapers.repec.org/RePEc:wly:fufsci:v:8:y:2026:i:1:n:e70026

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