Conference Proceedings

Supporting large-scale geographical visualization in a multi-granularity way

ZHIFENG Bao

Association for Computing Machinery (ACM) | Published : 2018

Abstract

© 2018 Association for Computing Machinery. Urban data (e.g., real estate data, crime data) often have multiple attributes which are highly geography-related. With the scale of data increases, directly visualizing millions of individual data points on top of a map would overwhelm users' perceptual and cognitive capacity and lead to high latency when users interact with the data. In this demo, we present ConvexCubes, a system that supports interactive visualization of large-scale multidimensional urban data in a multi-granularity way. Comparing to state-of-theart visualization-driven data structures, it exploits real-world geographic semantics (e.g., country, state, city) rather than using gr..

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Grants

Awarded by ARC


Awarded by National Natural Science Foundation of China (NSFC)


Funding Acknowledgements

This work was partially supported by ARC DP170102726, DP170102231, DP180102050, and National Natural Science Foundation of China (NSFC) 61728204, 91646204. Zhifeng Bao is supported by a Google Faculty Award.