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Life histories across time and space : methods for including geographic factors on the micro-level in longitudinal demographic research

Hedefalk, Finn LU (2014)
Abstract
Historical demography, which is the study of human population dynamics in the past, is central for understanding human behaviours and traits, such as fertility, mortality and migration. An important factor in demographic research is the geographic context. Where people lived often determined their social ties, exposure to diseases and economic development. Such information is essential not only for historical demographic research but also for a wide range of disciplines.While the geographic context on an aggregated level has an important role in longitudinal historical studies, geographic contexts on a micro-level have only played a minor role.



This licentiate contributes to historical demographic research by studying... (More)
Historical demography, which is the study of human population dynamics in the past, is central for understanding human behaviours and traits, such as fertility, mortality and migration. An important factor in demographic research is the geographic context. Where people lived often determined their social ties, exposure to diseases and economic development. Such information is essential not only for historical demographic research but also for a wide range of disciplines.While the geographic context on an aggregated level has an important role in longitudinal historical studies, geographic contexts on a micro-level have only played a minor role.



This licentiate contributes to historical demographic research by studying how geographic factors on the micro-level can be included in longitudinal historical analyses. A primary focus is the methodological development for creating longitudinally detailed locations that can be linked to individuals in demographic databases. This research should offer a variety of possibilities for studying how geographic factors on the micro-level affected human living conditions throughout history.



The thesis has four research objectives. The first objective is to extend a standardised data model for longitudinal demographic data to include geographic data. This is achieved by introducing IDS-Geo, which is a geographically extended version of the standardised data model IDS. The second objective is to develop and evaluate harmonisation methods to ensure that source data comply with standardised data models. This is achieved by testing and developing a method for first harmonising Swedish environmental data and metadata and then testing the data for compliance against standardised data models and specifications. The third objective is to develop a methodology for creating integrated longitudinal demographic and geographic databases that include geographic factors on the microlevel in demographic research. The core of the methodology is to transform geographic objects in snapshot time representations (digitised from historical maps) into longitudinal object lifeline time representations, and to link individuals to these geographic objects using standardised locations. The methodology is implemented in a case study in which we integrate information from approximately 60 digitised historical maps with longitudinal individual-level data from the Scanian Economic Demographic Database (SEDD). We link 80,431 individuals in five rural parishes in Sweden during 1813-1914 to the property units where they lived. The resulting database is tested using fundamental queries for spatio-temporal data. Additional historical geographic data used for computing context variables are constructed. The results are a unique contribution in terms of linking individuals over such long time periods to longitudinal geographic data on the micro-level. Lastly, the fourth objective of the thesis is to

perform longitudinal demographic analyses where geographic factors can subsequently be included. This is performed by analysing the intergenerational effects of child bearing by relatively older women on the longevity of adult offspring in pre-transitional Utah, USA. (Less)
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author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
pages
142 pages
publisher
Department of Physical Geography and Ecosystem Science, Lund University
ISBN
978-91-85793-41-9
language
English
LU publication?
yes
id
45723e13-a505-4b74-90f4-09d6cb8b2941 (old id 4732650)
date added to LUP
2014-11-04 10:44:00
date last changed
2016-09-19 08:45:11
@misc{45723e13-a505-4b74-90f4-09d6cb8b2941,
  abstract     = {Historical demography, which is the study of human population dynamics in the past, is central for understanding human behaviours and traits, such as fertility, mortality and migration. An important factor in demographic research is the geographic context. Where people lived often determined their social ties, exposure to diseases and economic development. Such information is essential not only for historical demographic research but also for a wide range of disciplines.While the geographic context on an aggregated level has an important role in longitudinal historical studies, geographic contexts on a micro-level have only played a minor role.<br/><br>
<br/><br>
This licentiate contributes to historical demographic research by studying how geographic factors on the micro-level can be included in longitudinal historical analyses. A primary focus is the methodological development for creating longitudinally detailed locations that can be linked to individuals in demographic databases. This research should offer a variety of possibilities for studying how geographic factors on the micro-level affected human living conditions throughout history. <br/><br>
<br/><br>
The thesis has four research objectives. The first objective is to extend a standardised data model for longitudinal demographic data to include geographic data. This is achieved by introducing IDS-Geo, which is a geographically extended version of the standardised data model IDS. The second objective is to develop and evaluate harmonisation methods to ensure that source data comply with standardised data models. This is achieved by testing and developing a method for first harmonising Swedish environmental data and metadata and then testing the data for compliance against standardised data models and specifications. The third objective is to develop a methodology for creating integrated longitudinal demographic and geographic databases that include geographic factors on the microlevel in demographic research. The core of the methodology is to transform geographic objects in snapshot time representations (digitised from historical maps) into longitudinal object lifeline time representations, and to link individuals to these geographic objects using standardised locations. The methodology is implemented in a case study in which we integrate information from approximately 60 digitised historical maps with longitudinal individual-level data from the Scanian Economic Demographic Database (SEDD). We link 80,431 individuals in five rural parishes in Sweden during 1813-1914 to the property units where they lived. The resulting database is tested using fundamental queries for spatio-temporal data. Additional historical geographic data used for computing context variables are constructed. The results are a unique contribution in terms of linking individuals over such long time periods to longitudinal geographic data on the micro-level. Lastly, the fourth objective of the thesis is to<br/><br>
perform longitudinal demographic analyses where geographic factors can subsequently be included. This is performed by analysing the intergenerational effects of child bearing by relatively older women on the longevity of adult offspring in pre-transitional Utah, USA.},
  author       = {Hedefalk, Finn},
  isbn         = {978-91-85793-41-9},
  language     = {eng},
  pages        = {142},
  publisher    = {ARRAY(0xa0e3eb0)},
  title        = {Life histories across time and space : methods for including geographic factors on the micro-level in longitudinal demographic research},
  year         = {2014},
}