IDEAS home Printed from https://ideas.repec.org/a/spr/astaws/v15y2021i3d10.1007_s11943-021-00294-z.html
   My bibliography  Save this article

Pendler Mobil: Die Verwendung von Mobilfunkdaten zur Unterstützung der amtlichen Pendlerstatistik
[Pendler Mobil: The use of mobile network data to support official commuter statistics]

Author

Listed:
  • Sandra Hadam

    (Statistisches Bundesamt)

Abstract

Zusammenfassung Die Verfügbarkeit von kleinräumigen und aktuellen Pendlerverflechtungen sind für politische wie auch kommunale Entscheidungsfindungen von hoher Bedeutung. Aus dem Pendlerverhalten lassen sich Rückschlüsse auf Arbeitsmarktregionen und die Verteilung der Wohnbevölkerung ziehen, was unter anderem zu einer laufenden Verbesserung der Verkehrsinfrastruktur beiträgt. Die dafür notwendigen Daten veröffentlicht die amtliche Pendlerrechnung. Jedoch weist sie Verbesserungspotenzial im Hinblick auf die zeitliche und räumliche Darstellung der Pendlerverflechtungen von Erwerbstätigen sowie eine fachliche Erweiterung hinsichtlich der Bildungspendler auf. Dieser Artikel beschreibt die mit dem Projekt Pendler Mobil geprüften Erweiterungsmöglichkeiten der amtlichen Pendlerrechnung auf Basis von Quelle-Ziel-Matrizen aus Mobilfunkdaten. Mobilfunkdaten stellen aufgrund ihrer zeitlichen Aktualität und räumlich feinen Auflösung eine robuste Datengrundlage zur flexiblen Abbildung von potenziellen und regelmäßigen Pendlerbewegungen dar. Die potenzielle Leistungsfähigkeit der Mobilfunkdaten ermöglicht damit eine externe Validierung bestehender Pendlerrechnungen oder Pendlerstatistiken sowie eine beiderseitige Ergänzung zur Ermittlung und Darstellungen weiterer Formen des Pendelns der Erwerbsbevölkerung. Am Fallbeispiel des Bundeslandes Nordrhein-Westfalen werden im Folgenden Gemeinsamkeiten und Unterschiede der übereinstimmenden Pendlerverflechtungen auf Basis von Mobilfunkdaten und der amtlichen Pendlerrechnung erörtert. Dabei gehen wir auf die Herausforderungen der Aufbereitung und Definition geeigneter Mobilfunkdaten durch den Datenanbieter sowie weitere Einflüsse auf die Mobilfunkdaten, wie bspw. durch die zurückgelegte Distanz oder die Verweilzeiten mobiler Aktivitäten, ein. Besonders die Unterschätzung der mobilen Pendlerströme im Vergleich zur amtlichen Pendlerrechnung legt nahe, Modifizierungsansätze der Mobilfunkdaten zu diskutieren. Im Ergebnis können die vorliegenden Mobilfunkdaten potenziell die amtliche Pendlerrechnung durch kleinräumige Pendlerbewegungen in Städten in Form einer erweiterten Zielorts-Bestimmung unterstützen und die Identifizierung von stark frequentierten Arbeitsorten in Städten ermöglichen.

Suggested Citation

  • Sandra Hadam, 2021. "Pendler Mobil: Die Verwendung von Mobilfunkdaten zur Unterstützung der amtlichen Pendlerstatistik [Pendler Mobil: The use of mobile network data to support official commuter statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 15(3), pages 197-235, December.
  • Handle: RePEc:spr:astaws:v:15:y:2021:i:3:d:10.1007_s11943-021-00294-z
    DOI: 10.1007/s11943-021-00294-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11943-021-00294-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11943-021-00294-z?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. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Jakub Novak & Rein Ahas & Anto Aasa & Siiri Silm, 2013. "Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia," Journal of Maps, Taylor & Francis Journals, vol. 9(1), pages 10-15, March.
    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. Timo Schmid & Markus Zwick, 2021. "Vorwort der Herausgeber," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 15(3), pages 151-154, December.
    2. Sandra Hadam, 2023. "Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten [Experimental georeferenced population figure based on intercensal population updates an," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 17(1), pages 35-69, March.

    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. Totterman, Stephen, 2021. "Vehicle-based recreation and compliance for three beaches in northern New South Wales," OSF Preprints ja8h6, Center for Open Science.
    2. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    3. Sewando, Ponsian T. & Mdoe, N. Y. S. & Mutabazi, K. D. S, 2011. "Farmers’ preferential choice decisions to alternative cassava value chain strands in Morogoro rural district, Tanzania," MPRA Paper 29797, University Library of Munich, Germany.
    4. Lawrence N Kazembe, 2013. "A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
    5. Ina Falfán & Luis Zambrano, 2023. "Lacustrine Urban Blue Spaces: Low Availability and Inequitable Distribution in the Most Populated Cities in Mexico," Land, MDPI, vol. 12(1), pages 1-18, January.
    6. Guarino, Ernestino de Souza Gomes & Barbosa, Ana Márcia & Waechter, Jorge Luiz, 2012. "Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams," Ecological Modelling, Elsevier, vol. 230(C), pages 22-33.
    7. Evgenii V. Gilenko & Elena A. Mironova, 2017. "Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1311097-131, January.
    8. Andre Jungmittag, 2019. "Service trade restrictiveness and internationalisation of retail trade," International Economics and Economic Policy, Springer, vol. 16(2), pages 293-333, April.
    9. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    10. Erni, Birgit & Bonnevie, Bo T. & Oschadleus, Hans-Dieter & Altwegg, Res & Underhill, Les G., 2013. "moult: An R Package to Analyze Moult in Birds," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i08).
    11. Zeileis, Achim & Koenker, Roger, 2008. "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i01).
    12. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    13. Lindsay P Campbell & Daniel C Reuman & Joel Lutomiah & A Townsend Peterson & Kenneth J Linthicum & Seth C Britch & Assaf Anyamba & Rosemary Sang, 2019. "Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-19, December.
    14. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    15. Malhado, Ana C.M. & Santos, Janisson & Correia, Ricardo A. & Campos-Silva, João V. & Teles, Davi & Costa, Marcos H. & Jepson, Paul & Ladle, Richard J., 2020. "Monitoring and mapping non-governmental conservation action in Amazonia," Land Use Policy, Elsevier, vol. 94(C).
    16. Filipe Sengo Furtado & Thomas Reutterer & Nadine Schröder, 2022. "The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices," Journal of Business Economics, Springer, vol. 92(4), pages 565-590, May.
    17. Taro Kanatani & Kuninori Nakagawa, 2023. "Analysis of reporting lag in daily data of COVID-19 in Japan," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-20, December.
    18. Benedikt Preuß & Lasse Fischer & Annika Schmidt & Kathrin Seibert & Viktoria Hoel & Dominik Domhoff & Franziska Heinze & Werner Brannath & Karin Wolf-Ostermann & Heinz Rothgang, 2022. "COVID-19 in German Nursing Homes: The Impact of Facilities’ Structures on the Morbidity and Mortality of Residents—An Analysis of Two Cross-Sectional Surveys," IJERPH, MDPI, vol. 20(1), pages 1-13, December.
    19. Weko, Silvia & Goldthau, Andreas, 2022. "Bridging the low-carbon technology gap? Assessing energy initiatives for the Global South," Energy Policy, Elsevier, vol. 169(C).
    20. Taghouti, Ibtissem & Martinez-Gomez, Victor & Coque, José María Garcia Alvarez, 2015. "Exploring Eu Food Safety Notifications On Agro-Food Imports: Are Mediterranean Partner Countries Discriminated?," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(2), pages 1-15, April.

    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:spr:astaws:v:15:y:2021:i:3:d:10.1007_s11943-021-00294-z. 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.