Yenny Andrea Marín Salazar
Research Subjects
Yenny Andrea Marín Salazar has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Andrew J. Tatem
WorldPop, University of Southampton
1 shared publication - Andryu Enrique Mendoza
National Administrative Department of Statistics, Colombia
1 shared publication - Douglas Ryan Leasure
University of Oxford, UK
1 shared publication - Glenn Harry Amaya Cruz
National Administrative Department of Statistics, Colombia
1 shared publication - Gustavo Adolfo Garcia Velez
National Administrative Department of Statistics, Colombia
1 shared publication - Lina María Sánchez-Céspedes
Agustín Codazzi Geographic Institute (IGAC)
1 shared publication
Latest Publications
- Open Access
- 28 March 2023
- Published by Taylor & Francis in Population Studies
- Vol. 78 (1) , 3-20
Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia
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.
- 28 March 2023
- Published by Taylor & Francis in Population Studies
- Vol. 78 (1) , 3-20
Open AccessSocial cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia
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.