Alex Singleton's research sits at the boundary between the social and computational sciences; and has extended a tradition of area classification within Urban Analytics where he has developed an empirically informed critique of the ways in which geodemographic methods (unsupervised machine learning) can be refined for effective yet ethical use; and how systems comprising artificial intelligence can assist in public resource allocation applications. This research has developed from substantive interests around the social, spatial and temporal dimensions of urban systems; specifically focused on access inequalities in Higher Education, digital exclusion, aspects of retail and school commuting behaviour.

His most recent work has focused on developing an understanding how both fast and slow human dynamics can be understood through emerging Geographic Data Science methodologies, and mapping such geographies at fine spatiotemporal scale. He has general interests in supporting the public understanding of science through online interfaces to data, and is a strong advocate of open source data, software and publishing.