The future of urban models in the Big Data and AI era: a bibliometric analysis (2000-2019)
This work provides insights for urban researchers and policymakers on the evolving integration of AI and Big Data in urban management, though it is incremental as it analyzes existing trends rather than proposing new methods.
This study examined how the Big Data and AI turn has impacted urban research dynamics, specifically in transportation and water systems, by analyzing bibliometric data from 2000-2019 and conducting interviews. It found measurable increases in AI/Big Data keywords and publications in computer science journals related to urban traffic and water quality.
This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. To identify these effects, we use two complementary materials: bibliometric data and interviews. We consider two areas in urban research: one, covering the academic research dealing with transportation systems and the other, with water systems. First, we measure the evolution of AI and Big Data keywords in these two areas. Second, we measure the evolution of the share of publications published in computer science journals about urban traffic and water quality. To guide these bibliometric analyses, we rely on the content of interviews conducted with academics and higher education officials in Paris and Edinburgh at the beginning of 2018.