GeoAI in urban analytics

We are writing this editorial piece at the peak of the current Artificial Intelligence (AI) ‘spring’ as generative models quickly cross the bridge from the confines of academic and industry labs into our everyday lives. During times like this, one might be excused from forgetting how old the application of AI approaches in geography is. Geographers have been here before. About forty years ago, Smith (Citation1984) wrote:

AI techniques, if properly applied, should also allow researchers to spend a greater proportion of their time on creative thinking and less on technical drudgery. As with any set of tools, the techniques of AI cannot replace a hard-earned understanding of some phenomenon and will almost certainly be overvalued and misused by some practitioners. [Nevertheless], if used with care, the techniques of AI will prove of great benefit to such an applied, problem solving discipline as geography. (p. 157).

It is in the subsequent issue of the same journal that we find Nystuen’s (Citation1984) comment, suggesting that ‘[b]enefit to geography from such an alliance [with AI] is questionable considering that our own directions are murky enough’ (p. 358). Smith, in Nystuen’s view, should be ‘a little more critical in his appraisal of the scope of possible applications’ (Nystuen Citation1984, p. 359).