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Human Migration and Spatial Synchrony: Spatial Patterns in Temporal Trends

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Recapturing Space: New Middle-Range Theory in Spatial Demography

Part of the book series: Spatial Demography Book Series ((SPDE,volume 1))

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Abstract

In some ways, demographic research is changing as a result of the availability of new forms of data and the computational ability to deal with such data. In the past, analytical methods that include both spatial and temporal components have been lacking. However, this is no longer the case. As an illustration I introduce spatial synchrony for human demography using migration data from a demographic surveillance system in rural Thailand. The method is borrowed from population ecology and can be used to analyze spatial correlations in time series data. I finish the chapter by discussing new forms of data which require some new ways to approach data from theoretical, computational, and ethical considerations.

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Notes

  1. 1.

    Short-term migrations may also be referred to as human mobility or circular migration and in population ecology parlance, dispersal.

  2. 2.

    Nathan Keyfitz, arguably among the most influential demographers, was a strong proponent of this line of thought. While his PhD was in Sociology, he collaborated with population scientists from quite a wide array of disciplines.

  3. 3.

    Patrick Alfred Pierce Moran also developed the Moran’s I statistic, a commonly used measure of spatial autocorrelation.

  4. 4.

    In fact, Lotka saw the study of human populations as being divided into mathematical demography and statistical demography.

  5. 5.

    Some holidays are highly localized (consider Juneteenth in Texas or Patriots day in Massachusetts and Maine), whereas others have an extremely broad range (i.e. New Year’s day, Christmas, etc.).

  6. 6.

    There is a literature concerning “Time Geography” that is relevant to this and is of general interest with regard to space-time interactions.

  7. 7.

    Here I use the terms virilocal and uxorilocal rather than patrilocal and matrilocal, respectively. My reasoning is that the terms patrilocal and matrilocal assume a unilineal descent system, which is not always the case.

  8. 8.

    With 5-year age groups in order to control for potential age-heaping in self reported ages among survey respondents.

  9. 9.

    Ottar Bjørnstad gave a talk on “wombling for wait times” at an NSF meeting on “Challenges in Modeling the Spatial and Temporal Dimensions of the Ecology of Infectious Diseases” at Ohio State University (Sep. 18, 2012).

  10. 10.

    For example, a “Beowulf cluster” is a group of mainstream computers that are typically linked together in a central location (rather than spread across many different locations). Beowulf clusters offer a relatively cheap approach to high performance computing.

  11. 11.

    I should note here that the IRB standard is minimal risk to research subjects, weighted against the potential gains from the research. I leave it to individual researchers to decide if that goes far enough and the answer to that would necessarily vary by research topic and research subject. Official ethics policy is an extremely important topic to consider with regard to these new forms of data.

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Acknowledgements

This study is partially supported by NIAID, NIH (U19AI089672). I would also like to acknowledge data collection by staff at the Vivax Research Center, Mahidol University, the Vector Borne Disease Training Center in Saraburi and staff from the Department of Public Health in Tha Song Yang District, Thailand. Finally, Stephen Matthews, Ottar Bjørnstad and James Wood offered advice with regard to the statistical analysis.

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  1. The Population Research Institute, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, 16802, USA

    Daniel M. Parker Ph.D

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  1. Department of Sociology, Mississippi State University, Ridgeland, Mississippi, USA

    Frank M. Howell

  2. Brooklyn College and Graduate Center, City University of New York, Brooklyn, New York, USA

    Jeremy R. Porter

  3. Department of Sociology & Department of Anthropology, The Pennsylvania State University, University Park, Pennsylvania, USA

    Stephen A. Matthews

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Parker, D.M. (2016). Human Migration and Spatial Synchrony: Spatial Patterns in Temporal Trends. In: Howell, F., Porter, J., Matthews, S. (eds) Recapturing Space: New Middle-Range Theory in Spatial Demography. Spatial Demography Book Series, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-22810-5_16

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