Numerical approaches for representing and reasoning about information are ineffective when data is too imprecise or uncertain. People on the other hand cope very effectively with vague information in daily life, for example when using spatial or temporal information. This has motivated the field of qualitative spatiotemporal reasoning (QSTR), which focuses on coarse, qualitative distinctions between spatial and temporal entities and relations. A substantial body of work has emerged from the QSTR community, however, serious difficulties prevent a uniform and general qualitative treatment of data representing space and time. Without unifying principles there is no basis for comparing the various QSTR approaches, and it is not always clear when and how QSTR should be applied. These issues must be addressed before QSTR can be properly integrated into standard software tools and practices. In this paper the first author’s PhD programme is outlined, covering (a) the research aim of developing a framework for supporting the design and implementation of QSTR solutions, and (b) the research approach, which is based around the analysis of case studies, two of which are discussed.