Abstract Multiplex IF (mIF) provides a detailed characterization of spatial relationships and complex cell phenotypes in the tumor microenvironment. However, the data-analysis and visualization is complex and time-consuming. Here, we developed a platform to analyze mIF data through flow cytometry workflows (image cytometry), while maintaining spatial information, and applied it to tissue microarrays of metastatic melanoma specimens (n=93; 6-plex mIF panel: PD-1, PD-L1, CD163, CD8, FoxP3, Sox10/S100). Then, we used a UMAP-based approach driven by cell-to-cell distances (rather than fluorescence intensity) to display and analyze geographic organization and cell interactions. Our pipeline provided equivalent results to the digital pathology gold standard with faster run times (5-fold reduction) and higher reproducibility. We identified key prognostic immune variables, including CD8PD1Low and CD8PD1Neg cells which associated with longer overall survival (OS, both p<0.01), and CD163PDL1Neg cells which associated with shorter OS (p=0.001). The spatial UMAPs showed that PD-L1 and PD-1 intensities were spatially encoded, and their expression on distinct cell subsets was organized in geographic clusters. Specifically, PD-L1Hi cells co-located to areas of CD8 cells, and PD-1Hi cells were observed near dense collections of tumor cells. Spatial UMAP subtraction analysis (survivors vs. non-survivors at 5 years) identified geographic and co-expression signatures associated with improved prognosis, i.e. CD8-driven PD-L1 expression and lacking CD163PDL1Neg macrophages. These data demonstrate the use of image cytometry and spatial UMAPs for improved visualization and interpretation of single-cell, spatially-resolved mIF data.