GeoSparkViz in Action: A Data System with built-in support for Geospatial Visualization


Visualizing data on maps is deemed a powerful tool for data scientists to make sense of geospatial data. The geospatial map visualization (abbr. MapViz) process first loads the designated geospatial data, processes the data and then applies the map visualization effect. Guaranteeing detailed and accurate geospatial MapViz (e.g., at multiple zoom levels) requires extremely high-resolution maps. Classic solutions suffer from limited computation resources while scalable MapViz system architectures are not able to co-optimize the data management and visualization phases in the same system. This paper demonstrates GEOSPARKVIZ, a full-fledged system that allows the user to load, prepare, integrate and execute MapViz tasks in the same system. For demonstration purpose, we implemented a web interface using a node.js web server, Baidu echarts library, and MapBox on top of GEOSPARKVIZ to visually explore patterns in the New York City Taxi Trips dataset. The demonstration scenarios show how the data preparation and map visualization phases are combined in GEOSPARKVIZ.

In IEEE International Conference on Data Engineering, ICDE
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.