A pixel level evaluation of five multitemporal global gridded population datasets : a case study in Sweden, 1990–2015
(2020) In Population and Environment 42(2). p.255-277- Abstract
Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are... (More)
Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are identified and changes in algorithms over time affect accuracy. Our results provide guidance in navigating the differences between the most commonly used gridded population datasets and will help researchers and policy makers identify the most suitable datasets under varying conditions.
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- author
- Archila Bustos, Maria Francisca LU ; Hall, Ola LU ; Niedomysl, Thomas LU and Ernstson, Ulf
- organization
- publishing date
- 2020-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dasymetric mapping, Gridded population, Human population distribution, Population estimation
- in
- Population and Environment
- volume
- 42
- issue
- 2
- pages
- 23 pages
- publisher
- Springer
- external identifiers
-
- scopus:85090130289
- ISSN
- 0199-0039
- DOI
- 10.1007/s11111-020-00360-8
- language
- English
- LU publication?
- yes
- id
- 30729ae7-f2b3-476a-91c4-84330109d136
- date added to LUP
- 2020-09-15 07:35:44
- date last changed
- 2022-04-19 00:39:23
@article{30729ae7-f2b3-476a-91c4-84330109d136, abstract = {{<p>Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are identified and changes in algorithms over time affect accuracy. Our results provide guidance in navigating the differences between the most commonly used gridded population datasets and will help researchers and policy makers identify the most suitable datasets under varying conditions.</p>}}, author = {{Archila Bustos, Maria Francisca and Hall, Ola and Niedomysl, Thomas and Ernstson, Ulf}}, issn = {{0199-0039}}, keywords = {{Dasymetric mapping; Gridded population; Human population distribution; Population estimation}}, language = {{eng}}, number = {{2}}, pages = {{255--277}}, publisher = {{Springer}}, series = {{Population and Environment}}, title = {{A pixel level evaluation of five multitemporal global gridded population datasets : a case study in Sweden, 1990–2015}}, url = {{http://dx.doi.org/10.1007/s11111-020-00360-8}}, doi = {{10.1007/s11111-020-00360-8}}, volume = {{42}}, year = {{2020}}, }