research-article

A time-series clustering algorithm for analyzing the changes of mobility pattern caused by COVID-19

Authors:

Ziyi Zhang,

Diya Li,

Zhe Zhang,

Nicholas DuffieldAuthors Info & Claims

HANIMOB '21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility

Pages 13 - 17

https://doi.org/10.1145/3486637.3489489

Published: 02 November 2021 Publication History

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Abstract

The coronavirus (COVID-19) has spread to more than 135 countries and continues to spread. The virus sickened more than 90,201,652 people until January 2021 and caused 1,937,091 deaths in the world. So far, social distancing plays a vital role in controlling the coronavirus. Governments issued restrictions on traveling, institutions cancel gatherings, and citizens socially distance themselves to limit the spread of the virus. This paper aims to develop a novel time-series clustering algorithm to analyze the changes in mobility patterns caused by the COVID-19. This work will produce broader impacts in many areas, such as helping local governments locate the medical facilities and improving the social distancing recommendations for infectious disease control.

References

[1]

Li, D., Chaudhary, H., & Zhang, Z. (2020). Modeling spatiotemporal pattern of depressive symptoms caused by COVID-19 using social media data mining. International Journal of Environmental Research and Public Health, 17(14), 1--23.

Crossref

Google Scholar

[2]

Zhang, Z., Yin, D., Virrantaus, K., Ye, X., & Wang, S. (2021). Modeling human activity dynamics: an object-class oriented space-time composite model based on social media and urban infrastructure data. Computational Urban Science 2021 1:1, 1(1), 1--13.

Crossref

Google Scholar

[3]

Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12(1), 16--26.

Digital Library

Google Scholar

[4]

Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E. C., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L. V., Coupland, H., Cucunuba, Z. M., Cuomo-Dannenburg, G., Dighe, A., Djaafara, B. A., Dorigatti, I., Eales, O. D., van Elsland, S. L., Nascimento, F. F., ... Donnelly, C. A. (2021). Reduction in mobility and COVID-19 transmission. Nature Communications, 12(1), 1--9.

Crossref

Google Scholar

[5]

Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015). Time-series clustering - A decade review. Information Systems, 53, 16--38.

Digital Library

Google Scholar

[6]

Places Data & Foot Traffic Insights | SafeGraph. (n.d.). Retrieved October 6, 2021, from https://www.safegraph.com

Google Scholar

[7]

Trips by Distance | Open Data | Socrata. (n.d.). Retrieved May 27, 2021, from https://data.bts.gov/Research-and-Statistics/Trips-by-Distance/w96p-f2qv

Google Scholar

[8]

Han, J., Kamber, M., and Pei, J., 2012. Data mining concepts and techniques. 3rd ed. Waltham, MA: Morgan Kaufman, MIT Press. https://tinman.cs.gsu.edu/~zcai/course/47406740/Slides/Chapter%201%20Introduction%20to%20Data%20Mining.pdf

Google Scholar

[9]

P. Rai, S. Singh, A survey of clustering techniques, Int. J. Comput. Appl. 7 (12) (2010) 1--5. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.206.5219&rep=rep1&type=pdf

Google Scholar

[10]

Ma, Q., Zheng, J., Li, S., & Cottrell, G. W. (2019). Learning Representations for Time Series Clustering. Advances in Neural Information Processing Systems, 32.

Google Scholar

[11]

Huang, X., Li, Z., Lu, J., Wang, S., Wei, H., & Chen, B. (2020). Time-series clustering for home dwell time during COVID-19: What can we learn from it? ISPRS International Journal of Geo-Information, 9(11), 675.

Crossref

Google Scholar

[12]

Muda, L., Begam, M., & Elamvazuthi, I. (2010). Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques. 2. https://arxiv.org/abs/1003.4083v1

Google Scholar

[13]

Saito, N. (2000). LOCAL FEATURE EXTRACTION AND ITS APPLICATIONS USING A LIBRARY OF BASES. In Topics in Analysis and Its Applications (pp. 269--451). WORLD SCIENTIFIC.

Crossref

Google Scholar

[14]

AssentIra, WichterichMarc, KriegerRalph, KremerHardy, & SeidlThomas. (2009). Anticipatory DTW for efficient similarity search in time series databases. Proceedings of the VLDB Endowment, 2(1), 826--837.

Digital Library

Google Scholar

Cited By

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Zhang ZLi DZhang ZDuffield N(2024)Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1311037413:11(374)Online publication date: 24-Oct-2024
https://doi.org/10.3390/ijgi13110374
Zhang ZRen SQian XDuffield NBaeza-Yates RBonchi F(2024)Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series DataProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672023(4408-4418)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672023
Li DZhang Z(2023)MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligencePLOS ONE10.1371/journal.pone.029303418:11(e0293034)Online publication date: 13-Nov-2023
https://doi.org/10.1371/journal.pone.0293034
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Index Terms

A time-series clustering algorithm for analyzing the changes of mobility pattern caused by COVID-19
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
2. Theory of computation
  1. Models of computation
    1. Timed and hybrid models

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HANIMOB '21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility

November 2021

53 pages

ISBN:9781450391221

DOI:10.1145/3486637

Editors:
Federico Ossi
Fondazione Edmund Mach, Italy
,
Fatima Hachem
University of Milan, Italy
,
Francesca Cagnacci
Fondazione Edmund Mach, Italy
,
Urška Demšar
University of St Andrews, Scotland, UK
,
Maria Luisa Damiani
University of Milan, Italy

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 the author(s) 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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Published: 02 November 2021

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SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2, 2021

Beijing, China

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Zhang ZLi DZhang ZDuffield N(2024)Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1311037413:11(374)Online publication date: 24-Oct-2024
https://doi.org/10.3390/ijgi13110374
Zhang ZRen SQian XDuffield NBaeza-Yates RBonchi F(2024)Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series DataProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672023(4408-4418)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672023
Li DZhang Z(2023)MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligencePLOS ONE10.1371/journal.pone.029303418:11(e0293034)Online publication date: 13-Nov-2023
https://doi.org/10.1371/journal.pone.0293034
Zhang ZLi DSong ZDuffield NZhang Z(2023)Location-Aware Social Network Recommendation via Temporal Graph NetworksProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628342(58-61)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615896.3628342
Ossi FHachem FCagnacci FDemšar UDamiani M(2022)HaniMob 2021 Workshop Report: The 1st ACM SIGSPATIAL Workshop on Animal Movement Ecology and Human MobilitySIGSPATIAL Special10.1145/3578484.357849213:3(33-36)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578492

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Fast and Accurate Time-Series Clustering

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Fast and Accurate Time-Series Clustering

Covid-19 Vaccines As a Condition of Employment: Impact on Uptake, Staffing, and Mortality in Elderly Care Homes

Telehealth utilization during the Covid-19 pandemic: A systematic review

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