Natural language processing (NLP) has shown potential as a promising tool to exploit under-utilized urban data sources. This paper presents a systematic review of urban studies published in peer-reviewed journals and conference proceedings that adopted NLP. The review suggests that the application of NLP in studying cities is still in its infancy. Current applications fell into five areas: urban governance and management, public health, land use and functional zones, mobility, and urban design. NLP demonstrates the advantages of improving the usability of urban big data sources, expanding study scales, and reducing research costs. On the other hand, to take advantage of NLP, urban researchers face challenges of raising good research questions, overcoming data incompleteness, inaccessibility, and non-representativeness, immature NLP techniques, and computational skill requirements. This review is among the first efforts intended to provide an overview of existing applications and challenges for advancing urban research through the adoption of NLP.

Public perception of the next generation of vehicles will affect their design, deployment, and ultimately their use. As the engineering terms of self-driving, driverless, fully automated, and autonomous were introduced to the social sciences and the public at large, the subtle differences among these terms have been lost. However, using them as synonyms even though they may not be interchangeable is problematic. To explore the semantics of different future vehicle terms we surveyed 963 Michigan residents on their understanding of “autonomous” and “self-driving.” We found significant differences in perceptions between the terms autonomous and self-driving vehicles. While the former invokes many more uncertain responses, the latter is laden with concerns. These results suggest that the language used to describe the next generation of vehicles may shape public reaction and acceptance. As new mobility options are introduced to the public, our understanding of them will be shaped, in part, by the language used to name and explain the technology. Far from being inconsequential, word choice plays a major, yet underappreciated, role in shaping public opinion.

Synergies and trade-offs among the United Nations Sustainable Development Goals (SDGs) have been hotly debated. Although the world is increasingly metacoupled (socioeconomic-environmental interactions within and across adjacent or distant systems), there is little understanding of the impacts of globally widespread and important flows on enhancing or compromising sustainability in different systems. Here, we used a new integrated framework to guide SDG synergy and trade-off analysis within and across systems, as influenced by cross-boundary tourism and wildlife translocations. The world’s terrestrial protected areas alone receive approximately 8 billion visits per year, generating a direct economic impact of US $600 billion. Globally, more than 5000 animal species and 29,000 plant species are traded across country borders, and the wildlife trade has arguably contributed to zoonotic disease worldwide, such as the ongoing COVID-19 pandemic. We synthesized 22 cases of tourism and wildlife translocations across six continents and found 33 synergies and 14 trade-offs among 10 SDGs within focal systems and across spillover systems. Our study provides an empirical demonstration of SDG interactions across spillover systems and insights for holistic sustainability governance, contributing to fostering synergies and reducing trade-offs to achieve global sustainable development in the metacoupled Anthropocene.