---
_id: '12548'
abstract:
- lang: eng
  text: The limited exchange between human communities is a key factor in preventing
    the spread of COVID-19. This paper introduces a digital framework that combines
    an integration of real mobility data at the country scale with a series of modeling
    techniques and visual capabilities that highlight mobility patterns before and
    during the pandemic. The findings not only significantly exhibit mobility trends
    and different degrees of similarities at regional and local levels but also provide
    potential insight into the emergence of a pandemic on human behavior patterns
    and their likely socio-economic impacts.
article_number: '00093'
article_processing_charge: No
author:
- first_name: Mohammad
  full_name: Forghani, Mohammad
  last_name: Forghani
- first_name: Christophe
  full_name: Claramunt, Christophe
  last_name: Claramunt
- first_name: Farid
  full_name: Karimipour, Farid
  id: 2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
  last_name: Karimipour
  orcid: 0000-0001-6746-4174
- first_name: Georg
  full_name: Heiler, Georg
  last_name: Heiler
citation:
  ama: 'Forghani M, Claramunt C, Karimipour F, Heiler G. Visual analytics of mobility
    network changes observed using mobile phone data during COVID-19 pandemic. In:
    <i>2022 IEEE International Conference on Data Mining Workshops</i>. Institute
    of Electrical and Electronics Engineers; 2023. doi:<a href="https://doi.org/10.1109/icdmw58026.2022.00093">10.1109/icdmw58026.2022.00093</a>'
  apa: 'Forghani, M., Claramunt, C., Karimipour, F., &#38; Heiler, G. (2023). Visual
    analytics of mobility network changes observed using mobile phone data during
    COVID-19 pandemic. In <i>2022 IEEE International Conference on Data Mining Workshops</i>.
    Orlando, FL, United States: Institute of Electrical and Electronics Engineers.
    <a href="https://doi.org/10.1109/icdmw58026.2022.00093">https://doi.org/10.1109/icdmw58026.2022.00093</a>'
  chicago: Forghani, Mohammad, Christophe Claramunt, Farid Karimipour, and Georg Heiler.
    “Visual Analytics of Mobility Network Changes Observed Using Mobile Phone Data
    during COVID-19 Pandemic.” In <i>2022 IEEE International Conference on Data Mining
    Workshops</i>. Institute of Electrical and Electronics Engineers, 2023. <a href="https://doi.org/10.1109/icdmw58026.2022.00093">https://doi.org/10.1109/icdmw58026.2022.00093</a>.
  ieee: M. Forghani, C. Claramunt, F. Karimipour, and G. Heiler, “Visual analytics
    of mobility network changes observed using mobile phone data during COVID-19 pandemic,”
    in <i>2022 IEEE International Conference on Data Mining Workshops</i>, Orlando,
    FL, United States, 2023.
  ista: 'Forghani M, Claramunt C, Karimipour F, Heiler G. 2023. Visual analytics of
    mobility network changes observed using mobile phone data during COVID-19 pandemic.
    2022 IEEE International Conference on Data Mining Workshops. ICDMW: Conference
    on Data Mining Workshops, 00093.'
  mla: Forghani, Mohammad, et al. “Visual Analytics of Mobility Network Changes Observed
    Using Mobile Phone Data during COVID-19 Pandemic.” <i>2022 IEEE International
    Conference on Data Mining Workshops</i>, 00093, Institute of Electrical and Electronics
    Engineers, 2023, doi:<a href="https://doi.org/10.1109/icdmw58026.2022.00093">10.1109/icdmw58026.2022.00093</a>.
  short: M. Forghani, C. Claramunt, F. Karimipour, G. Heiler, in:, 2022 IEEE International
    Conference on Data Mining Workshops, Institute of Electrical and Electronics Engineers,
    2023.
conference:
  end_date: 2022-12-01
  location: Orlando, FL, United States
  name: 'ICDMW: Conference on Data Mining Workshops'
  start_date: 2022-11-28
date_created: 2023-02-14T07:56:21Z
date_published: 2023-02-08T00:00:00Z
date_updated: 2023-08-01T13:15:48Z
day: '08'
ddc:
- '600'
department:
- _id: HeEd
doi: 10.1109/icdmw58026.2022.00093
external_id:
  isi:
  - '000971492200145'
file:
- access_level: open_access
  checksum: c253bee25e6dfe484f96662daa119cb6
  content_type: application/pdf
  creator: fkarimip
  date_created: 2023-02-14T07:58:26Z
  date_updated: 2023-02-14T07:58:26Z
  file_id: '12549'
  file_name: Visual Analysis_Mobility_COVID19 - SocDM2022.pdf
  file_size: 1183339
  relation: main_file
  success: 1
file_date_updated: 2023-02-14T07:58:26Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Submitted Version
publication: 2022 IEEE International Conference on Data Mining Workshops
publication_identifier:
  eisbn:
  - '9798350346091'
  eissn:
  - 2375-9259
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
status: public
title: Visual analytics of mobility network changes observed using mobile phone data
  during COVID-19 pandemic
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2023'
...
---
_id: '11429'
abstract:
- lang: eng
  text: "This book constitutes the refereed proceedings of the 18th International
    Symposium on Web and Wireless Geographical Information Systems, W2GIS 2022, held
    in Konstanz, Germany, in April 2022.\r\nThe 7 full papers presented together with
    6 short papers in the volume were carefully reviewed and selected from 16 submissions.
    \ The papers cover topics that range from mobile GIS and Location-Based Services
    to Spatial Information Retrieval and Wireless Sensor Networks."
alternative_title:
- LNCS
article_processing_charge: No
citation:
  ama: 'Karimipour F, Storandt S, eds. <i>Web and Wireless Geographical Information
    Systems</i>. Vol 13238. 1st ed. Cham: Springer Nature; 2022. doi:<a href="https://doi.org/10.1007/978-3-031-06245-2">10.1007/978-3-031-06245-2</a>'
  apa: 'Karimipour, F., &#38; Storandt, S. (Eds.). (2022). <i>Web and Wireless Geographical
    Information Systems</i> (1st ed., Vol. 13238). Cham: Springer Nature. <a href="https://doi.org/10.1007/978-3-031-06245-2">https://doi.org/10.1007/978-3-031-06245-2</a>'
  chicago: 'Karimipour, Farid, and Sabine Storandt, eds. <i>Web and Wireless Geographical
    Information Systems</i>. 1st ed. Vol. 13238. Cham: Springer Nature, 2022. <a href="https://doi.org/10.1007/978-3-031-06245-2">https://doi.org/10.1007/978-3-031-06245-2</a>.'
  ieee: 'F. Karimipour and S. Storandt, Eds., <i>Web and Wireless Geographical Information
    Systems</i>, 1st ed., vol. 13238. Cham: Springer Nature, 2022.'
  ista: 'Karimipour F, Storandt S eds. 2022. Web and Wireless Geographical Information
    Systems 1st ed., Cham: Springer Nature, 153p.'
  mla: Karimipour, Farid, and Sabine Storandt, editors. <i>Web and Wireless Geographical
    Information Systems</i>. 1st ed., vol. 13238, Springer Nature, 2022, doi:<a href="https://doi.org/10.1007/978-3-031-06245-2">10.1007/978-3-031-06245-2</a>.
  short: F. Karimipour, S. Storandt, eds., Web and Wireless Geographical Information
    Systems, 1st ed., Springer Nature, Cham, 2022.
date_created: 2022-06-02T05:40:53Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2022-06-02T05:56:22Z
day: '01'
department:
- _id: HeEd
doi: 10.1007/978-3-031-06245-2
edition: '1'
editor:
- first_name: Farid
  full_name: Karimipour, Farid
  id: 2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
  last_name: Karimipour
  orcid: 0000-0001-6746-4174
- first_name: Sabine
  full_name: Storandt, Sabine
  last_name: Storandt
intvolume: '     13238'
language:
- iso: eng
month: '05'
oa_version: None
page: '153'
place: Cham
publication_identifier:
  eisbn:
  - '9783031062452'
  eissn:
  - 1611-3349
  isbn:
  - '9783031062445'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Web and Wireless Geographical Information Systems
type: book_editor
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13238
year: '2022'
...
---
_id: '10208'
abstract:
- lang: eng
  text: It is practical to collect a huge amount of movement data and environmental
    context information along with the health signals of individuals because there
    is the emergence of new generations of positioning and tracking technologies and
    rapid advancements of health sensors. The study of the relations between these
    datasets and their sequence similarity analysis is of interest to many applications
    such as health monitoring and recommender systems. However, entering all movement
    parameters and health signals can lead to the complexity of the problem and an
    increase in its computational load. In this situation, dimension reduction techniques
    can be used to avoid consideration of simultaneous dependent parameters in the
    process of similarity measurement of the trajectories. The present study provides
    a framework, named CaDRAW, to use spatial–temporal data and movement parameters
    along with independent context information in the process of measuring the similarity
    of trajectories. In this regard, the omission of dependent movement characteristic
    signals is conducted by using an unsupervised feature selection dimension reduction
    technique. To evaluate the effectiveness of the proposed framework, it was applied
    to a real contextualized movement and related health signal datasets of individuals.
    The results indicated the capability of the proposed framework in measuring the
    similarity and in decreasing the characteristic signals in such a way that the
    similarity results -before and after reduction of dependent characteristic signals-
    have small differences. The mean differences between the obtained results before
    and after reducing the dimension were 0.029 and 0.023 for the round path, respectively.
acknowledgement: The third author acknowledges the funding received from the Wittgenstein
  Prize, Austrian Science Fund (FWF), grant no. Z 342-N31.
article_processing_charge: No
article_type: original
author:
- first_name: Samira
  full_name: Goudarzi, Samira
  last_name: Goudarzi
- first_name: Mohammad
  full_name: Sharif, Mohammad
  last_name: Sharif
- first_name: Farid
  full_name: Karimipour, Farid
  id: 2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
  last_name: Karimipour
  orcid: 0000-0001-6746-4174
citation:
  ama: Goudarzi S, Sharif M, Karimipour F. A context-aware dimension reduction framework
    for trajectory and health signal analyses. <i>Journal of Ambient Intelligence
    and Humanized Computing</i>. 2022;13:2621–2635. doi:<a href="https://doi.org/10.1007/s12652-021-03569-z">10.1007/s12652-021-03569-z</a>
  apa: Goudarzi, S., Sharif, M., &#38; Karimipour, F. (2022). A context-aware dimension
    reduction framework for trajectory and health signal analyses. <i>Journal of Ambient
    Intelligence and Humanized Computing</i>. Springer Nature. <a href="https://doi.org/10.1007/s12652-021-03569-z">https://doi.org/10.1007/s12652-021-03569-z</a>
  chicago: Goudarzi, Samira, Mohammad Sharif, and Farid Karimipour. “A Context-Aware
    Dimension Reduction Framework for Trajectory and Health Signal Analyses.” <i>Journal
    of Ambient Intelligence and Humanized Computing</i>. Springer Nature, 2022. <a
    href="https://doi.org/10.1007/s12652-021-03569-z">https://doi.org/10.1007/s12652-021-03569-z</a>.
  ieee: S. Goudarzi, M. Sharif, and F. Karimipour, “A context-aware dimension reduction
    framework for trajectory and health signal analyses,” <i>Journal of Ambient Intelligence
    and Humanized Computing</i>, vol. 13. Springer Nature, pp. 2621–2635, 2022.
  ista: Goudarzi S, Sharif M, Karimipour F. 2022. A context-aware dimension reduction
    framework for trajectory and health signal analyses. Journal of Ambient Intelligence
    and Humanized Computing. 13, 2621–2635.
  mla: Goudarzi, Samira, et al. “A Context-Aware Dimension Reduction Framework for
    Trajectory and Health Signal Analyses.” <i>Journal of Ambient Intelligence and
    Humanized Computing</i>, vol. 13, Springer Nature, 2022, pp. 2621–2635, doi:<a
    href="https://doi.org/10.1007/s12652-021-03569-z">10.1007/s12652-021-03569-z</a>.
  short: S. Goudarzi, M. Sharif, F. Karimipour, Journal of Ambient Intelligence and
    Humanized Computing 13 (2022) 2621–2635.
date_created: 2021-11-02T09:28:55Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2023-08-02T13:31:48Z
day: '01'
ddc:
- '000'
department:
- _id: HeEd
doi: 10.1007/s12652-021-03569-z
external_id:
  isi:
  - '000712198000001'
file:
- access_level: open_access
  checksum: 0a8961416a9bb2be5a1cebda65468bcf
  content_type: application/pdf
  creator: fkarimip
  date_created: 2021-11-12T19:38:05Z
  date_updated: 2022-12-20T23:30:08Z
  embargo: 2022-11-12
  file_id: '10279'
  file_name: A Context‑aware Dimension Reduction Framework - Journal of Ambient Intelligence
    2021 (Preprint version).pdf
  file_size: 1634958
  relation: main_file
file_date_updated: 2022-12-20T23:30:08Z
has_accepted_license: '1'
intvolume: '        13'
isi: 1
keyword:
- general computer science
language:
- iso: eng
month: '05'
oa: 1
oa_version: Submitted Version
page: 2621–2635
project:
- _id: 268116B8-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z00342
  name: The Wittgenstein Prize
publication: Journal of Ambient Intelligence and Humanized Computing
publication_identifier:
  eissn:
  - 1868-5145
  issn:
  - 1868-5137
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A context-aware dimension reduction framework for trajectory and health signal
  analyses
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13
year: '2022'
...
---
_id: '9253'
abstract:
- lang: eng
  text: In March 2020, the Austrian government introduced a widespread lock-down in
    response to the COVID-19 pandemic. Based on subjective impressions and anecdotal
    evidence, Austrian public and private life came to a sudden halt. Here we assess
    the effect of the lock-down quantitatively for all regions in Austria and present
    an analysis of daily changes of human mobility throughout Austria using near-real-time
    anonymized mobile phone data. We describe an efficient data aggregation pipeline
    and analyze the mobility by quantifying mobile-phone traffic at specific point
    of interests (POIs), analyzing individual trajectories and investigating the cluster
    structure of the origin-destination graph. We found a reduction of commuters at
    Viennese metro stations of over 80% and the number of devices with a radius of
    gyration of less than 500 m almost doubled. The results of studying crowd-movement
    behavior highlight considerable changes in the structure of mobility networks,
    revealed by a higher modularity and an increase from 12 to 20 detected communities.
    We demonstrate the relevance of mobility data for epidemiological studies by showing
    a significant correlation of the outflow from the town of Ischgl (an early COVID-19
    hotspot) and the reported COVID-19 cases with an 8-day time lag. This research
    indicates that mobile phone usage data permits the moment-by-moment quantification
    of mobility behavior for a whole country. We emphasize the need to improve the
    availability of such data in anonymized form to empower rapid response to combat
    COVID-19 and future pandemics.
article_processing_charge: No
arxiv: 1
author:
- first_name: Georg
  full_name: Heiler, Georg
  last_name: Heiler
- first_name: Tobias
  full_name: Reisch, Tobias
  last_name: Reisch
- first_name: Jan
  full_name: Hurt, Jan
  last_name: Hurt
- first_name: Mohammad
  full_name: Forghani, Mohammad
  last_name: Forghani
- first_name: Aida
  full_name: Omani, Aida
  last_name: Omani
- first_name: Allan
  full_name: Hanbury, Allan
  last_name: Hanbury
- first_name: Farid
  full_name: Karimipour, Farid
  id: 2A2BCDC4-CF62-11E9-BE5E-3B1EE6697425
  last_name: Karimipour
  orcid: 0000-0001-6746-4174
citation:
  ama: 'Heiler G, Reisch T, Hurt J, et al. Country-wide mobility changes observed
    using mobile phone data during COVID-19 pandemic. In: <i>2020 IEEE International
    Conference on Big Data</i>. IEEE; 2021:3123-3132. doi:<a href="https://doi.org/10.1109/bigdata50022.2020.9378374">10.1109/bigdata50022.2020.9378374</a>'
  apa: 'Heiler, G., Reisch, T., Hurt, J., Forghani, M., Omani, A., Hanbury, A., &#38;
    Karimipour, F. (2021). Country-wide mobility changes observed using mobile phone
    data during COVID-19 pandemic. In <i>2020 IEEE International Conference on Big
    Data</i> (pp. 3123–3132). Atlanta, GA, United States: IEEE. <a href="https://doi.org/10.1109/bigdata50022.2020.9378374">https://doi.org/10.1109/bigdata50022.2020.9378374</a>'
  chicago: Heiler, Georg, Tobias Reisch, Jan Hurt, Mohammad Forghani, Aida Omani,
    Allan Hanbury, and Farid Karimipour. “Country-Wide Mobility Changes Observed Using
    Mobile Phone Data during COVID-19 Pandemic.” In <i>2020 IEEE International Conference
    on Big Data</i>, 3123–32. IEEE, 2021. <a href="https://doi.org/10.1109/bigdata50022.2020.9378374">https://doi.org/10.1109/bigdata50022.2020.9378374</a>.
  ieee: G. Heiler <i>et al.</i>, “Country-wide mobility changes observed using mobile
    phone data during COVID-19 pandemic,” in <i>2020 IEEE International Conference
    on Big Data</i>, Atlanta, GA, United States, 2021, pp. 3123–3132.
  ista: 'Heiler G, Reisch T, Hurt J, Forghani M, Omani A, Hanbury A, Karimipour F.
    2021. Country-wide mobility changes observed using mobile phone data during COVID-19
    pandemic. 2020 IEEE International Conference on Big Data. Big Data: International
    Conference on Big Data, 3123–3132.'
  mla: Heiler, Georg, et al. “Country-Wide Mobility Changes Observed Using Mobile
    Phone Data during COVID-19 Pandemic.” <i>2020 IEEE International Conference on
    Big Data</i>, IEEE, 2021, pp. 3123–32, doi:<a href="https://doi.org/10.1109/bigdata50022.2020.9378374">10.1109/bigdata50022.2020.9378374</a>.
  short: G. Heiler, T. Reisch, J. Hurt, M. Forghani, A. Omani, A. Hanbury, F. Karimipour,
    in:, 2020 IEEE International Conference on Big Data, IEEE, 2021, pp. 3123–3132.
conference:
  end_date: 2020-12-13
  location: Atlanta, GA, United States
  name: 'Big Data: International Conference on Big Data'
  start_date: 2020-12-10
date_created: 2021-03-21T11:34:07Z
date_published: 2021-03-19T00:00:00Z
date_updated: 2023-08-07T14:00:13Z
day: '19'
department:
- _id: HeEd
doi: 10.1109/bigdata50022.2020.9378374
external_id:
  arxiv:
  - '2008.10064'
  isi:
  - '000662554703032'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2008.10064
month: '03'
oa: 1
oa_version: Preprint
page: 3123-3132
publication: 2020 IEEE International Conference on Big Data
publication_identifier:
  isbn:
  - '9781728162515'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Country-wide mobility changes observed using mobile phone data during COVID-19
  pandemic
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...