<p>Visualizing multidimensional spatial data is an essential visual analysis strategy, it helps us interpret and communicate how different variables correlate to geographical information. In this study, we proposed an abstract contextual visualization that encodes data on the boundaries of spatial distributions and developed a new algorithm, AuroraMap. AuroraMap projects the spatial data to the boundaries of the distributions and color-encodes the densities continuously. We further conducted the user experiments, and the results show users can detect the relative locations and scopes of the clusters. Furthermore, users can quantitatively determine the peak value of each cluster’s density. The method provides three contributions: (1) freeing up and saving the graphical visualization space; (2) assisting the users to quantitatively estimate the clusters inside distributions; (3) facilitating the visual comparisons for multiple and multivariate spatial distributions. In the end, we demonstrated two applications with real-world religious infrastructural data by AuroraMap to visualize geospatial data within complex boundaries and compare multiple variables in one graph.</p><p> </p>