TY - GEN
T1 - GeoSparkViz in action
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
AU - Yu, Jia
AU - Tahir, Anique
AU - Elsayed, Mohamed
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Distributed computing
KW - Geospatial
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85067926762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067926762&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00222
DO - 10.1109/ICDE.2019.00222
M3 - Conference contribution
AN - SCOPUS:85067926762
T3 - Proceedings - International Conference on Data Engineering
SP - 1992
EP - 1995
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
Y2 - 8 April 2019 through 11 April 2019
ER -