Importance of the Geocoding Level for Historical Demographic Analyses : A Case Study of Rural Parishes in Sweden, 1850–1914
Hedefalk, Finn LU
; Pantazatou, Karolina
LU
; Quaranta, Luciana
LU
and Harrie, Lars
LU
(2018)
In Spatial Demography
6(1).
p.35-69
- Abstract
- Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for... (More)
- Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850–1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20–50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider the quantification methods that are the most logical for the geographic context and the type of analyses. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1338f4d8-9f1b-426b-b882-903eabc52ec4
- author
- Hedefalk, Finn
LU
; Pantazatou, Karolina
LU
; Quaranta, Luciana
LU
and Harrie, Lars
LU
- organization
- publishing date
- 2018-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- geocoding, survival analyses, Cox proportional hazards model, historical demography, mortality, spatial resolution, longitudinal historical databases, wetlands, micro-level
- in
- Spatial Demography
- volume
- 6
- issue
- 1
- pages
- 35 - 69
- publisher
- Springer
- ISSN
- 2164-7070
- DOI
- 10.1007/s40980-017-0039-7
- project
- Life histories across time and space
- Landskrona Population Study
- language
- English
- LU publication?
- yes
- id
- 1338f4d8-9f1b-426b-b882-903eabc52ec4
- date added to LUP
- 2017-10-31 13:40:00
- date last changed
- 2022-01-24 21:08:01
@article{1338f4d8-9f1b-426b-b882-903eabc52ec4,
abstract = {{Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850–1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20–50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider the quantification methods that are the most logical for the geographic context and the type of analyses.}},
author = {{Hedefalk, Finn and Pantazatou, Karolina and Quaranta, Luciana and Harrie, Lars}},
issn = {{2164-7070}},
keywords = {{geocoding; survival analyses; Cox proportional hazards model; historical demography; mortality; spatial resolution; longitudinal historical databases; wetlands; micro-level}},
language = {{eng}},
number = {{1}},
pages = {{35--69}},
publisher = {{Springer}},
series = {{Spatial Demography}},
title = {{Importance of the Geocoding Level for Historical Demographic Analyses : A Case Study of Rural Parishes in Sweden, 1850–1914}},
url = {{http://dx.doi.org/10.1007/s40980-017-0039-7}},
doi = {{10.1007/s40980-017-0039-7}},
volume = {{6}},
year = {{2018}},
}