IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v18y2016i4d10.1007_s10109-016-0236-8.html
   My bibliography  Save this article

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Author

Listed:
  • Li Shi

    (Peking University)

  • Lun Wu

    (Peking University)

  • Guanghua Chi

    (Peking University)

  • Yu Liu

    (Peking University)

Abstract

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.

Suggested Citation

  • Li Shi & Lun Wu & Guanghua Chi & Yu Liu, 2016. "Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data," Journal of Geographical Systems, Springer, vol. 18(4), pages 359-376, October.
  • Handle: RePEc:kap:jgeosy:v:18:y:2016:i:4:d:10.1007_s10109-016-0236-8
    DOI: 10.1007/s10109-016-0236-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-016-0236-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10109-016-0236-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kang, Chaogui & Ma, Xiujun & Tong, Daoqin & Liu, Yu, 2012. "Intra-urban human mobility patterns: An urban morphology perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1702-1717.
    2. Theo Arentze & Harry Timmermans, 2008. "Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation," Environment and Planning B, , vol. 35(6), pages 1012-1027, December.
    3. Santi Phithakkitnukoon & Zbigniew Smoreda & Patrick Olivier, 2012. "Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    4. Carlo Ratti & Stanislav Sobolevsky & Francesco Calabrese & Clio Andris & Jonathan Reades & Mauro Martino & Rob Claxton & Steven H Strogatz, 2010. "Redrawing the Map of Great Britain from a Network of Human Interactions," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-6, December.
    5. Emmanouil Tranos & Peter Nijkamp, 2015. "Mobile phone usage in complex urban systems: a space–time, aggregated human activity study," Journal of Geographical Systems, Springer, vol. 17(2), pages 157-185, April.
    6. Juan Carrasco & Eric Miller, 2006. "Exploring the propensity to perform social activities: a social network approach," Transportation, Springer, vol. 33(5), pages 463-480, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yaoli & Kutadinata, Ronny & Winter, Stephan, 2019. "The evolutionary interaction between taxi-sharing behaviours and social networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 170-180.
    2. Bruno Emmanuel Ongo Nkoa & Jacques Simon Song, 2022. "Les canaux de transmission des effets des TIC sur la mobilisation des recettes fiscales en Afrique," African Development Review, African Development Bank, vol. 34(S1), pages 80-101, July.
    3. Wang, Yaoli & Winter, Stephan & Tomko, Martin, 2018. "Collaborative activity-based ridesharing," Journal of Transport Geography, Elsevier, vol. 72(C), pages 131-138.

    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. Kourtit, Karima & Nijkamp, Peter & Steenbruggen, John, 2017. "The significance of digital data systems for smart city policy," Socio-Economic Planning Sciences, Elsevier, vol. 58(C), pages 13-21.
    2. Miguel Picornell & Tomás Ruiz & Maxime Lenormand & José Ramasco & Thibaut Dubernet & Enrique Frías-Martínez, 2015. "Exploring the potential of phone call data to characterize the relationship between social network and travel behavior," Transportation, Springer, vol. 42(4), pages 647-668, July.
    3. Lin, Tao & Wang, Donggen & Zhou, Meng, 2018. "Residential relocation and changes in travel behavior: what is the role of social context change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 360-374.
    4. Roy, P. & Martínez, A.J. & Miscione, G. & Zuidgeest, M.H.P. & van Maarseveen, M.F.A.M., 2012. "Using Social Network Analysis to profile people based on their e-communication and travel balance," Journal of Transport Geography, Elsevier, vol. 24(C), pages 111-122.
    5. Maness, Michael & Cirillo, Cinzia & Dugundji, Elenna R., 2015. "Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior," Journal of Transport Geography, Elsevier, vol. 46(C), pages 137-150.
    6. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    7. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Lee, Jae Hyun & Goulias, Konstadinos G., 2018. "Companionship and time investment in social fields at different life cycle stages: Implications for activity and travel modeling and simulation," Research in Transportation Economics, Elsevier, vol. 68(C), pages 18-28.
    9. Jones, Peter & Lucas, Karen, 2012. "The social consequences of transport decision-making: clarifying concepts, synthesising knowledge and assessing implications," Journal of Transport Geography, Elsevier, vol. 21(C), pages 4-16.
    10. Ettema, Dick & Schwanen, Tim, 2012. "A relational approach to analysing leisure travel," Journal of Transport Geography, Elsevier, vol. 24(C), pages 173-181.
    11. Matous, Petr, 2017. "Complementarity and substitution between physical and virtual travel for instrumental information sharing in remote rural regions: A social network approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 61-79.
    12. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    13. Sanja Šćepanović & Igor Mishkovski & Pan Hui & Jukka K Nurminen & Antti Ylä-Jääski, 2015. "Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    14. Meead Saberi & Hani S. Mahmassani & Dirk Brockmann & Amir Hosseini, 2017. "A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks," Transportation, Springer, vol. 44(6), pages 1383-1402, November.
    15. Andreas Erlström & Markus Grillitsch & Ola Hall, 2022. "The geography of connectivity: a review of mobile positioning data for economic geography," Journal of Geographical Systems, Springer, vol. 24(4), pages 679-707, October.
    16. Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    17. Emanuele Strano & Matheus Viana & Luciano da Fontoura Costa & Alessio Cardillo & Sergio Porta & Vito Latora, 2013. "Urban Street Networks, a Comparative Analysis of Ten European Cities," Environment and Planning B, , vol. 40(6), pages 1071-1086, December.
    18. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    19. Di Ciommo, Floridea & Comendador, Julio & López-Lambas, María Eugenia & Cherchi, Elisabetta & Ortúzar, Juan de Dios, 2014. "Exploring the role of social capital influence variables on travel behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 46-55.
    20. Taede Tillema & Martin Dijst & Tim Schwanen, 2010. "Decisions concerning Communication Modes and the Influence of Travel Time: A Situational Approach," Environment and Planning A, , vol. 42(9), pages 2058-2077, September.

    More about this item

    Keywords

    Geographical impacts; Space and place; Spatially-embedded social networks; Mobile phone data; Individuals’ interaction;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    Statistics

    Access and download statistics

    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:kap:jgeosy:v:18:y:2016:i:4:d:10.1007_s10109-016-0236-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.