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Spatial Demography: An Opportunity to Improve Policy Making at Diverse Decision Levels

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Abstract

The number of applications of spatial demography has been growing mostly since the 1990s. Ranging from simple visualization to sophisticated spatial analytical techniques, these applications bring a new layer of explanation to demographic phenomena. This paper reviews demographic studies that specifically addressed space with spatial statistical models, and that focused on fertility, mortality, migration, and population models. Additionally, it summarizes different spatial datasets and software freely available, as well as the challenges that exist for the development of spatial demography applications. These challenges include confidentiality issues, scale problems, and the lack of training on spatial analysis in population centers. Although the first and second challenges involve modeling and technical solutions, the latter depends only on demographers’ commitment and willingness to promote change. Several topics for future spatially focused research are also outlined. Finally, the paper makes a strong case regarding the significant contribution that spatial demography can make to the monitoring, evaluation, and implementation of population policies.

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Notes

  1. Dykstra and Wissen (1999) highlight a difference between multidisciplinary and interdisciplinary research. The former assumes that the same topic may be addressed by several disciplines in isolation (researchers from one discipline do not necessarily consider the results obtained in another). The latter is broader and more promising. It combines ideas and methods from different disciplines to address a particular topic.

  2. For a comparison between multilevel and spatial models check Chaix et al. (2005) and Chaix et al. (2005).

  3. Regarding the impact of family planning policies on fertility decline, the Taiwanese case is a good example (Li 1973). In fact, one of the studies conducted in the area had a spatial-based design, in which different interventions were carefully assigned to locations based on a regular grid imposed on the study area (Freedman and Takeshita 1969).

  4. There has also been significant advancement in software for data visualization (Samet and Webber 2006; Xiao and Armstrong 2006), but this will not be discussed since this technique is not part of the concept of “spatial demography” used in this paper.

  5. Although not an example of spatial demography as defined in this paper, Lesthaeghe and Neels (2002) address this issue through statistical association and visualization of mapped variables.

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Acknowledgments

I wish to thank Deborah Balk, Noreen Goldman and Burton Singer for insightful comments and suggestions on earlier drafts. I also thank Glenn Deane for his suggestions during the 2006 Annual Meeting of the Population Association of America, and three anonymous referees for their helpful remarks.

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  1. Harvard School of Public Health, Harvard University, 665 Huntington Avenue, Bldg. I, 11th floor, Room 1113, Boston, MA, 02115, USA

    Marcia Caldas de Castro

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de Castro, M.C. Spatial Demography: An Opportunity to Improve Policy Making at Diverse Decision Levels. Popul Res Policy Rev 26, 477–509 (2007). https://doi.org/10.1007/s11113-007-9041-x

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