University of Copenhagen, August 20th to 21st 2026
It has long been recognized that the mortality transition was shaped by spatial dynamics. The idea of the ‘urban penalty’, for example, describes the substantially higher mortality experienced in urban areas compared to rural environments in the nineteenth century. As the transition progressed, this pattern was often reversed, including in Copenhagen, where mortality levels fell below that of many rural areas. The description of this shift illustrates how space is commonly used in historical demography as a way of breaking down aggregate trends to identify and describe inequalities obscured by averages. The rise of individual-level microdata presents an opportunity to push further and to explore the new avenues of (geo)spatial methodology that are now emerging.
The value of spatial methods is well established for the study of disease outbreaks, tracing its origins to John Snow’s classic analysis of the 1854 Broad Street cholera outbreak. When it comes to mortality more broadly, however, the extent to which place matters remains less clear. Yet many of the possible drivers of health inequality, such as housing conditions, residential crowding and segregation, and proximity to clean water, sanitation, and pollution, are inherently spatial in nature. These factors often cluster unevenly across neighborhoods and regions, reinforcing patterns of disadvantage that can have profound effects on health outcomes.
Recent studies have introduced several important methodological innovations. One important advance is the use of multi-level regression models, which enables researchers to disentangle social class and space, and study multiple spatial levels simultaneously (Reid et al. 2023). Furthermore, the use of address-level geocoding allows us to capture spatial context through ‘nearest neighbors’ rather than (often arbitrarily drawn) administrative boundaries (Hedefalk et al. 2023). Finally, Bayesian modeling offers a promising but still underutilized approach in historical demography for addressing uncertainty in areas with low case counts (Matthes 2024).
At this workshop, we aim to foster a community of practice for historical demographers interested in integrating spatial approaches into their study of mortality in the past. We welcome all participants who either already incorporate a spatial dimension in their research or wish to do so in the near future, whether it be through the creation of digital maps or the use of spatial statistical techniques. While all spatial approaches to mortality are welcome, the workshop will have a special focus on intra-urban spatial mortality and discussions of when and how spatial measures can and should be applied to regression models.
The two day workshop will consist of both training sessions with prepared data, taught by expert practitioners, and research seminars, containing longer talks by invited speakers and short (10 min) talks by other attendees.
How to apply
To participate in the workshop, please submit a short description of your current research project (approximately 300 words). In your description, briefly outline:
Your research focus and main research focus;
The spatial level of your data (e.g., national, regional, intra-urban);
Any spatial methods you are currently using, have used, or plan to use;
What you hope to learn, explore, or develop further in terms of spatial approaches.