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The low hanging fruit is gone: achievements and challenges of computational movement analysis

Author:

Patrick LaubeAuthors Info & Claims

SIGSPATIAL Special, Volume 7, Issue 1

Pages 3 - 10

https://doi.org/10.1145/2782759.2782762

Published: 20 May 2015 Publication History

Abstract

This position paper reviews the achievements and open challenges of movement analysis within Geographical Information Science. The paper argues that the simple problems of movement analysis have mostly been addressed to a sufficient level ("the low hanging fruit"), leaving the research community with the much more challenging problems for the years ahead ("the high hanging fruit"). Whereas the community has made good progress in structuring trajectory data (segmentation, similarity, clustering) and conceptualizing and detecting movement patterns, the much harder task of semantic annotation of structures and patterns remains difficult. The position paper summarizes both achievements and challenges with two sets assertions and calls for the establishment of a unifying theory of Computational Movement Analysis.

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Cited By

de Almeida Dde Vasconcelos Sde Andrade Fde Souza Baptista C(2021)Towards a Hybrid and Semantically Enriched Trajectory Data Warehouse2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA53542.2021.9686877(1-8)Online publication date: Nov-2021
https://doi.org/10.1109/AICCSA53542.2021.9686877
Demšar ULong JBenitez-Paez FBrum Bastos VMarion SMartin GSekulić SSmolak KZein BSiła-Nowicka K(2021)Establishing the integrated science of movement: bringing together concepts and methods from animal and human movement analysisInternational Journal of Geographical Information Science10.1080/13658816.2021.1880589(1-36)Online publication date: 19-Feb-2021
https://doi.org/10.1080/13658816.2021.1880589
Ribeiro de Almeida Dde Souza Baptista CGomes de Andrade FSoares A(2020)A Survey on Big Data for Trajectory AnalyticsISPRS International Journal of Geo-Information10.3390/ijgi90200889:2(88)Online publication date: 1-Feb-2020
https://doi.org/10.3390/ijgi9020088
Show More Cited By

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The low hanging fruit is gone: achievements and challenges of computational movement analysis

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Information & Contributors

Information

Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 7, Issue 1

March 2015

72 pages

EISSN:1946-7729

DOI:10.1145/2782759

Editor:
Chi-Yin (Ted) Chow
City University of Hong Kong

Issue’s Table of Contents

Copyright © 2015 Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2015

Published in SIGSPATIAL Volume 7, Issue 1

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Cited By

de Almeida Dde Vasconcelos Sde Andrade Fde Souza Baptista C(2021)Towards a Hybrid and Semantically Enriched Trajectory Data Warehouse2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA53542.2021.9686877(1-8)Online publication date: Nov-2021
https://doi.org/10.1109/AICCSA53542.2021.9686877
Demšar ULong JBenitez-Paez FBrum Bastos VMarion SMartin GSekulić SSmolak KZein BSiła-Nowicka K(2021)Establishing the integrated science of movement: bringing together concepts and methods from animal and human movement analysisInternational Journal of Geographical Information Science10.1080/13658816.2021.1880589(1-36)Online publication date: 19-Feb-2021
https://doi.org/10.1080/13658816.2021.1880589
Ribeiro de Almeida Dde Souza Baptista CGomes de Andrade FSoares A(2020)A Survey on Big Data for Trajectory AnalyticsISPRS International Journal of Geo-Information10.3390/ijgi90200889:2(88)Online publication date: 1-Feb-2020
https://doi.org/10.3390/ijgi9020088
Petri ASilva D(2020)Towards logical association rule mining on ontology-based semantic trajectories2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00098(586-591)Online publication date: Dec-2020
https://doi.org/10.1109/ICMLA51294.2020.00098
Laube PGudmundsson JWolle T(2019)Computer-gestützte BewegungsanalyseGeoinformatik10.1007/978-3-662-47096-1_68(157-184)Online publication date: 23-Nov-2019
https://doi.org/10.1007/978-3-662-47096-1_68
Wemegah TZhu SAtombo C(2018)Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxisEuropean Transport Research Review10.1186/s12544-018-0313-910:2Online publication date: 3-Sep-2018
https://doi.org/10.1186/s12544-018-0313-9
Long JWeibel RDodge SLaube P(2018)Moving ahead with computational movement analysisInternational Journal of Geographical Information Science10.1080/13658816.2018.144297432:7(1275-1281)Online publication date: 18-May-2018
https://doi.org/10.1080/13658816.2018.1442974
Hachem FDamiani MIssa H(2017)Discovering Gatherings Based on Individual Mobility Patterns: Challenges and Direction2017 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2017.41(266-273)Online publication date: Nov-2017
https://doi.org/10.1109/ICDMW.2017.41
Wemegah TZhu S(2017)Big data challenges in transportation: A case study of traffic volume count from massive Radio Frequency Identification(RFID) data2017 International Conference on the Frontiers and Advances in Data Science (FADS)10.1109/FADS.2017.8253194(58-63)Online publication date: Oct-2017
https://doi.org/10.1109/FADS.2017.8253194
Laube PGudmundsson JWolle T(2016)Computer-gestützte BewegungsanalyseHandbuch der Geodäsie10.1007/978-3-662-46900-2_68-1(1-28)Online publication date: 25-May-2016
https://doi.org/10.1007/978-3-662-46900-2_68-1
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