demonstration

DiSA: A Display-driven Spatial Analysis Framework for Large-Scale Vector Data

Authors:

Mengyu Ma,

Anran Yang,

Ye Wu,

Luo Chen,

Jun Li,

Ning JingAuthors Info & Claims

SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems

Pages 147 - 150

https://doi.org/10.1145/3397536.3422333

Published: 13 November 2020 Publication History

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Abstract

We present DiSA, a Display-driven Spatial Analysis framework for interactive analysis of large-scale geographical vector data. DiSA calculates visualization of analysis results directly using a parallel per-pixel approach with efficient fine-grained spatial indexes. Compared with conventional object-based methods, DiSA can greatly reduce the computational complexity (from O(n) to O(log(n)) in some cases), making it less sensitive to data volumes. Experimental results verify that DiSA can provide analysis of billion-scale spatial objects in milliseconds. We demonstrate DiSA with various application scenarios including raw data exploration, spatial buffer and overlay analysis, and global cellular signal strength analysis. Users can explore 10 millions of spatial objects, adjust algorithm parameters, and always see the results in real-time on a personal computer.

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Cited By

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Liu ZChen LMa MYang AZhong ZJing N(2023)An efficient visual exploration approach of geospatial vector big data on the web mapInformation Systems10.1016/j.is.2023.102333(102333)Online publication date: Dec-2023
https://doi.org/10.1016/j.is.2023.102333
Chen LLiu ZMa M(2022)Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization ModelApplied Sciences10.3390/app1215771012:15(7710)Online publication date: 31-Jul-2022
https://doi.org/10.3390/app12157710
Liu ZChen LYang AMa MCao J(2021)HiIndex: An Efficient Spatial Index for Rapid Visualization of Large-Scale Geographic Vector DataISPRS International Journal of Geo-Information10.3390/ijgi1010064710:10(647)Online publication date: 26-Sep-2021
https://doi.org/10.3390/ijgi10100647
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Index Terms

DiSA: A Display-driven Spatial Analysis Framework for Large-Scale Vector Data
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms

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Published In

SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems

November 2020

687 pages

ISBN:9781450380195

DOI:10.1145/3397536

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2020

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SIGSPATIAL '20

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SIGSPATIAL

SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems

November 3 - 6, 2020

WA, Seattle, USA

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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Liu ZChen LMa MYang AZhong ZJing N(2023)An efficient visual exploration approach of geospatial vector big data on the web mapInformation Systems10.1016/j.is.2023.102333(102333)Online publication date: Dec-2023
https://doi.org/10.1016/j.is.2023.102333
Chen LLiu ZMa M(2022)Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization ModelApplied Sciences10.3390/app1215771012:15(7710)Online publication date: 31-Jul-2022
https://doi.org/10.3390/app12157710
Liu ZChen LYang AMa MCao J(2021)HiIndex: An Efficient Spatial Index for Rapid Visualization of Large-Scale Geographic Vector DataISPRS International Journal of Geo-Information10.3390/ijgi1010064710:10(647)Online publication date: 26-Sep-2021
https://doi.org/10.3390/ijgi10100647
Eldawy AHristidis VGhosh SSaeedan MSevim ASiddique ASingla SSivaram GVu TZhang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)BeastProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481897(3796-3807)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481897

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