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Supporting Large-scale Geographical Visualization in a Multi-granularity Way

Published: 02 February 2018 Publication History

Abstract

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-the-art visualization-driven data structures, it exploits real-world geographic semantics (e.g., country, state, city) rather than using grid-based aggregation. Instead of calculating everything on demand, ConvexCubes utilizes existing visualization results to efficiently support different kinds of user interactions, such as zooming & panning, filtering and granularity control. Our system can be accessed at http://115.146.89.158/ConvexCubes/.

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  • (2024)ScaleTraversal: Creating Multi-Scale Biomedical Animation with Limited Hardware ResourcesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681191(2603-2612)Online publication date: 28-Oct-2024
  • (2022)SAFEProceedings of the VLDB Endowment10.14778/3494124.349413515:3(513-526)Online publication date: 4-Feb-2022
  • (2022)Location-Centered House Price Prediction: A Multi-Task Learning ApproachACM Transactions on Intelligent Systems and Technology10.1145/350180613:2(1-25)Online publication date: 5-Jan-2022
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cover image ACM Conferences
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
February 2018
821 pages
ISBN:9781450355810
DOI:10.1145/3159652
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 February 2018

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Author Tags

  1. data structure
  2. geographical visualization
  3. interactive visualization
  4. visual analytics

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  • Research-article

Funding Sources

  • Australian Research Council
  • National Natural Science Foundation of China
  • Google

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WSDM 2018

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;
Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

View all
  • (2024)ScaleTraversal: Creating Multi-Scale Biomedical Animation with Limited Hardware ResourcesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681191(2603-2612)Online publication date: 28-Oct-2024
  • (2022)SAFEProceedings of the VLDB Endowment10.14778/3494124.349413515:3(513-526)Online publication date: 4-Feb-2022
  • (2022)Location-Centered House Price Prediction: A Multi-Task Learning ApproachACM Transactions on Intelligent Systems and Technology10.1145/350180613:2(1-25)Online publication date: 5-Jan-2022
  • (2021)Fast augmentation algorithms for network kernel density visualizationProceedings of the VLDB Endowment10.14778/3461535.346154014:9(1503-1516)Online publication date: 1-May-2021
  • (2020)QUAD: Quadratic-Bound-based Kernel Density VisualizationProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3380561(35-50)Online publication date: 11-Jun-2020
  • (2019)VisCrimePredictProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297388(1099-1106)Online publication date: 8-Apr-2019
  • (2019)VisCrimeProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3290617(802-805)Online publication date: 30-Jan-2019
  • (2018)POIsamProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3193549(1677-1680)Online publication date: 27-May-2018
  • (2018)ConcaveCubes: Supporting Cluster‐based Geographical Visualization in Large Data ScaleComputer Graphics Forum10.1111/cgf.1341437:3(217-228)Online publication date: 10-Jul-2018

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