Journal article
Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia
- Abstract:
- Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 10.3MB, Terms of use)
-
- Publisher copy:
- 10.1080/00324728.2023.2190151
Authors
- Publisher:
- Taylor and Francis
- Journal:
- Population Studies More from this journal
- Volume:
- 78
- Issue:
- 1
- Pages:
- 3-20
- Publication date:
- 2023-03-28
- Acceptance date:
- 2022-11-22
- DOI:
- EISSN:
-
1477-4747
- ISSN:
-
0032-4728
- Pmid:
-
36977422
- Language:
-
English
- Keywords:
- Pubs id:
-
1335599
- Local pid:
-
pubs:1335599
- Deposit date:
-
2024-02-13
Terms of use
- Copyright holder:
- Sanchez-Cespedes et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record