Data Visualization allows users to summarize, analyze and reason about data. A map visualization tool first loads the designated geospatial data, processes the data and then applies the map visualization effect. Guaranteeing detailed and accurate geospatial map visualization (e.g., at multiple zoom levels) requires extremely high-resolution maps. Classic solutions suffer from limited computation resources and hence take a tremendous amount of time to generate maps for large-scale geospatial data. The paper presents GeoSparkViz a large-scale geospatial map visualization framework. GeoSparkViz extends a cluster computing system (Apache Spark in our case) to provide native support for general cartographic design. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark. It provides the data scientist a holistic system that allows her to perform data management and visualization on spatial data and reduces the overhead of loading the intermediate spatial data generated during the data management phase to the designated map visualization tool. GeoSparkViz also proposes a map tile data partitioning method that achieves load balancing for the map visualization workloads among all nodes in the cluster. Extensive experiments show that GeoSparkViz can generate a high-resolution (i.e., Gigapixel image) Heatmap of 1.7 billion Open-StreetMaps objects and 1.3 billion NYC taxi trips in ≈4 and 5 minutes on a four-node commodity cluster, respectively.