IDEAS home Printed from https://ideas.repec.org/h/spr/adspcp/978-3-319-30196-9_9.html

A Spatial Interaction Model with Spatially Structured Origin and Destination Effects

In: Spatial Econometric Interaction Modelling

Author

Listed:
  • James P. LeSage

    (Texas State University)

  • Carlos Llano

    (Universidad Autónoma de Madrid)

Abstract

We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions to n destination regions resulting in a sample of N = n 2 observations that reflect an n by n flow matrix converted to a vector. Explanatory variables typically include origin and destination characteristics as well as distance between each region and all other regions. Our extension introduces latent spatial effects parameters structured to follow a spatial autoregressive process. Individual effects parameters are included in the model to reflect latent or unobservable influences at work that are unique to each region treated as an origin and destination. That is, we estimate 2n individual effects parameters using the sample of N = n 2 observations. We illustrate the method using a sample of commodity flows between 18 Spanish regions during the 2002 period.

Suggested Citation

  • James P. LeSage & Carlos Llano, 2016. "A Spatial Interaction Model with Spatially Structured Origin and Destination Effects," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 171-197, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-30196-9_9
    DOI: 10.1007/978-3-319-30196-9_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Nivedita Mukherji & Jonathan Silberman, 2013. "Absorptive Capacity, Knowledge Flows, And Innovation In U.S. Metropolitan Areas," Journal of Regional Science, Wiley Blackwell, vol. 53(3), pages 392-417, August.
    2. Wilfried Koch & James P. LeSage, 2015. "Latent Multilateral Trade Resistance Indices: Theory and Evidence," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(3), pages 264-290, July.
    3. Yin, Zhonghua & Wang, Fang & Gan, Jianbang, 2020. "Spatial spillover effects of global forest product trade," Forest Policy and Economics, Elsevier, vol. 113(C).
    4. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    5. Alessio Baldassarre & Danilo Carullo & Paolo Caro & Elisa Fusco & Pasquale Giacobbe & Carlo Orecchia, 2025. "A New Approach to Measure Italian Regional Trade Flows with Administrative Micro Firm-Level Data," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(1), pages 65-101, March.
    6. Carlos Llano-Verduras & Santiago Pérez-Balsalobre & Ana Rincón-Aznar, 2021. "Market fragmentation and the rise of sub-national regulation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(3), pages 765-797, December.
    7. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    8. Nivedita Mukherji & Jonathan Silberman, 2021. "Knowledge flows between universities and industry: the impact of distance, technological compatibility, and the ability to diffuse knowledge," The Journal of Technology Transfer, Springer, vol. 46(1), pages 223-257, February.
    9. Laura Serlenga & Yongcheol Shin, 2021. "Gravity models of interprovincial migration flows in Canada with hierarchical multifactor structure," Empirical Economics, Springer, vol. 60(1), pages 365-390, January.
    10. Llano, C. & De la Mata, T. & Díaz-Lanchas, J. & Gallego, N., 2017. "Transport-mode competition in intra-national trade: An empirical investigation for the Spanish case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 334-355.
    11. Luisa Alam᭓abater & Laura M uez-Ramos & Jos頍iguel Navarro-Azor󸀍 & Celestino Su z-Burguet, 2015. "A two-methodology comparison study of a spatial gravity model in the context of interregional trade flows," Applied Economics, Taylor & Francis Journals, vol. 47(14), pages 1481-1493, March.
    12. Dapeng Zhang, 2024. "Examining the Effects of Confirmed COVID-19 Cases and State Government Policies on Passenger Air Traffic Recovery by Proposing an OD Spatial Temporal Model," Networks and Spatial Economics, Springer, vol. 24(2), pages 341-359, June.
    13. Linglin Ni & Dapeng Zhang, 2024. "Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-14, June.
    14. James LeSage & Carlos Llano-Verduras, 2014. "Forecasting spatially dependent origin and destination commodity flows," Empirical Economics, Springer, vol. 47(4), pages 1543-1562, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

    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:spr:adspcp:978-3-319-30196-9_9. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.