GeoSparkViz: a scalable geospatial data visualization framework in the Apache Spark ecosystem


Data Visualization allows users to summarize, analyze and reason about data. A map visualization tool frst loads the designated geospatial data, processes the data and then applies the map visualization efect. Guaranteeing detailed and accurate geospatial map visualization (e.g., at multiple zoom levels) requires extremely highresolution maps. Classic solutions sufer 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 OpenStreetMaps objects and 1.3 billion NYC taxi trips in ≈4 and 5 minutes on a four-node commodity cluster, respectively.

In International Conference on Scientific and Statistical Database Management, SSDBM
Jia Yu
Jia Yu
Assistant Professor (from Fall 2020)

Jia Yu obtained his PhD from Arizona State University in Summer 2020. His research interests include database systems, distributed data systems and geospatial data management.

Mohamed Sarwat
Mohamed Sarwat
Assistant Professor

Mohamed Sarwat is an assistant professor of computer science at Arizona State University. His general research interest lies in developing robust and scalable data systems for spatial and spatiotemporal applications.