IDEAS home Printed from https://ideas.repec.org/a/gam/jgeogr/v5y2025i1p7-d1596375.html
   My bibliography  Save this article

Analysing Gender and Temporal Dynamics in Human Mobility Patterns in Central Sweden

Author

Listed:
  • Paria Sadeghian

    (School of Technology and Business Studies, Dalarna University, SE-79188 Falun, Sweden)

  • Brian Babak Mojarrad

    (Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden)

Abstract

Human mobility plays a fundamental role in urban life, shaping the development of infrastructure, transportation systems, and public spaces. Understanding the dynamics of mobility patterns is essential for creating sustainable and inclusive urban environments. This study investigates the influence of gender and temporal variations on human mobility within a city in central Sweden, shedding light on how movement patterns fluctuate throughout the day and differ across gender groups. The findings reveal significant temporal shifts in mobility hotspots, with individuals travelling to different areas at varying intensities depending on the time of day. These variations highlight the dynamic nature of urban movement and emphasise the necessity of time-sensitive urban planning strategies. While overall journey patterns between genders exhibit relatively small differences, a closer analysis uncovers distinct gender-based disparities in mobility hotspots, indicating that men and women tend to frequent different locations with varying travel behaviours. These insights provide valuable input for urban planners, policymakers, and transportation authorities seeking to enhance accessibility, safety, and efficiency in urban mobility networks. Recognising the interplay between gender and temporal mobility patterns can lead to more equitable infrastructure design, ensuring that urban spaces accommodate diverse mobility needs. By emphasising the importance of these factors, this study contributes to a broader understanding of human mobility behaviour and underscores the need for data-driven planning approaches that address spatial and temporal variations in movement patterns.

Suggested Citation

  • Paria Sadeghian & Brian Babak Mojarrad, 2025. "Analysing Gender and Temporal Dynamics in Human Mobility Patterns in Central Sweden," Geographies, MDPI, vol. 5(1), pages 1-15, February.
    • Handle: RePEc:gam:jgeogr:v:5:y:2025:i:1:p:7-:d:1596375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2673-7086/5/1/7/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2673-7086/5/1/7/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    2. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. D. Woods & A. Cunningham & C. E. Utazi & M. Bondarenko & L. Shengjie & G. E. Rogers & P. Koper & C. W. Ruktanonchai & E. zu Erbach-Schoenberg & A. J. Tatem & J. Steele & A. Sorichetta, 2022. "Exploring methods for mapping seasonal population changes using mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    2. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    3. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    5. Jiang, Jincheng & Xu, Zhihua & Zhang, Zhenxin & Zhang, Jie & Liu, Kang & Kong, Hui, 2023. "Revealing the fractal and self-similarity of realistic collective human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    6. Kim, Kyoungok, 2018. "Exploring the difference between ridership patterns of subway and taxi: Case study in Seoul," Journal of Transport Geography, Elsevier, vol. 66(C), pages 213-223.
    7. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    8. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    9. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    10. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    11. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    12. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.
    13. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    14. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    15. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    16. Miotti, Marco & Needell, Zachary A. & Jain, Rishee K., 2023. "The impact of urban form on daily mobility demand and energy use: Evidence from the United States," Applied Energy, Elsevier, vol. 339(C).
    17. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    18. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    19. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    20. Vanky, Anthony & Courtney, Theodore & Verma, Santosh & Ratti, Carlo, 2016. "One to Many: Opportunities to Understanding Collective Behaviors in Urban Environments Through Individual's Passively-Collected Locative Data," SocArXiv f7mpd, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgeogr:v:5:y:2025:i:1:p:7-:d:1596375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.