This chapter discusses the concept of ‘uncertainty’ associated with complex systems, various scales, and representation issues while using geospatially integrated modeling approaches. The use of a geospatial analytical framework can facilitate the analysis of complex systems to gain insights about multi-scale interactions within and across the coupled physical-human systems and associated processes, thus providing an effective ‘way of knowing.’ However, this effective ‘way of knowing’ and gaining insights about a complex system is fraught with uncertainties. While it is easier to study the system’s physical/environmental and human dimensions separately using a reductionist approach, this separation is rooted in a false dichotomy. Physical/environmental and human interactions are complex and can benefit from holistic and integrative approaches to define the scope of a problem, analyze feedback and find solutions. However, the use of a holistic approach to understand the environment and human relationships as a coupled system is beleaguered with problems associated with conceptualization, data collection (methods, mismatch of scales, errors, imprecise knowledge), model selection, and geospatial technological issues (data formats including unified data language, storage, standards), common metrics, common architectures. Knowledge production of a complex system using integrated and interdisciplinary approaches can be done through a thoughtful application of geospatial analytical frameworks after selecting the appropriate scale of analysis and a comprehensive examination of the uncertainty and errors associated with data.