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Animated Flow Maps for Visualizing Human Movement: Two Demonstrations with Air Traffic and Twitter Data

Authors:

Keith C. Clarke,

Ming-Hsiang TsouAuthors Info & Claims

LENS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News

Article No.: 5, Pages 1 - 10

https://doi.org/10.1145/3148044.3148049

Published: 07 November 2017 Publication History

Abstract

We present a novel flow mapping method called Animated Flow Mapper. Conventional flow mapping uses the thickness of a flow line to differentiate the volume of a flow and arrows to represent the direction. These flow mapping methods have suffered from problems of occlusion especially for visualizing large volumes of data, and when many flows converge at--or diverge from--single points. The Animated Flow Mapper aims to circumvent these problems of occlusion and to provide a more effective visualization of the patterns of the flows. The method uses thin dashed lines of equal width, which are animated to represent the directions and volumes of flows of moving objects by repeatedly redrawing each dashed-line at different speeds. The case studies and results of a human subject opinion survey show the advantages of the Animated Flow Mapper are comparable to other flow mapping methods.

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Cited By

Alsudais SBai QZhao SLi CRenz MSarwat M(2022)GSVizProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560983(1-12)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560983
Li MHe XSun ZWen Y(2022)A framework for social media information flow analytics in cyberspace and physical spaceTransactions in GIS10.1111/tgis.1292326:3(1485-1508)Online publication date: 8-Apr-2022
https://doi.org/10.1111/tgis.12923
Rey SHan SKang WKnaap ECortes R(2020)A Visual Analytics System for Space–Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank‐based Markov ChainsGeographical Analysis10.1111/gean.1223952:4(537-557)Online publication date: 26-Jun-2020
https://doi.org/10.1111/gean.12239

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Published In

cover image ACM Conferences

LENS'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News

November 2017

50 pages

ISBN:9781450355001

DOI:10.1145/3148044

Copyright © 2017 ACM.

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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National Science Foundation

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SIGSPATIAL'17

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SIGSPATIAL

SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7, 2017

CA, Redondo Beach, USA

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Cited By

Alsudais SBai QZhao SLi CRenz MSarwat M(2022)GSVizProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560983(1-12)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560983
Li MHe XSun ZWen Y(2022)A framework for social media information flow analytics in cyberspace and physical spaceTransactions in GIS10.1111/tgis.1292326:3(1485-1508)Online publication date: 8-Apr-2022
https://doi.org/10.1111/tgis.12923
Rey SHan SKang WKnaap ECortes R(2020)A Visual Analytics System for Space–Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank‐based Markov ChainsGeographical Analysis10.1111/gean.1223952:4(537-557)Online publication date: 26-Jun-2020
https://doi.org/10.1111/gean.12239

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