This article navigates through the challenge of preserving and presenting uncertainties in digital maps, which are used to reconstruct practical knowledge in early modern artists’ businesses. It introduces a novel methodology—deep mapping—as a multi-layered spatial visualization within the Geographical Information Systems (GIS). This method adeptly facilitates the processing and visualization of complex art historical data, offering a nuanced approach that addresses the dual need of managing large-scale spatial analysis and maintaining the precision requisite in scholarly work. To operationalize the concept of deep mapping in knowledge production, this research has collected and integrated location-related descriptions of early modern addresses from various sources, translating them into geo-referenced areas and visualizing them on historical maps with varying levels of uncertainties. Applying deep mapping to visualize painters’ distribution patterns in seventeenth-century Amsterdam as an example, this article discusses two ways of presenting uncertainties in digital maps to facilitate historical observation. It shows that uncertainty is most effectively presented as fuzzy heat maps in the background to accentuate painters’ choices of locations for their painting businesses. The deep maps demonstrate that painters in early seventeenth-century Amsterdam pragmatically practiced their business knowledge by making clustering decisions following market conditions.