Publications
1.
Darin, Edith; Boo, Gianluca; Tatem, Andrew J
A bottom-up population modelling approach to complement the population and housing census Conference
IUSSP , International Population Conference 2021, 2021.
Abstract | Links | BibTeX | Tags: Africa, bottom-up modelling, census, Democratic Republic of Congo
@conference{nokey,
title = {A bottom-up population modelling approach to complement the population and housing census},
author = {Darin, Edith and Boo, Gianluca and Tatem, Andrew J},
url = {https://ipc2021.popconf.org/abstracts/210325},
year = {2021},
date = {2021-12-07},
urldate = {2021-12-07},
booktitle = {IUSSP , International Population Conference 2021},
abstract = {The population and housing census provides essential demographic information for decision-making and action at local, national and international levels. However, census data in the most vulnerable countries is often outdated or partial because political instability, conflict and natural disasters prevent a national count. The bottom-up modeling approach helps supplement outdated or incomplete census data by estimating population counts and age/sex structures in approximately 100m grid cells using population data collected over a set fully enumerated places and auxiliary geospatial covariates. We present the modeling effort carried out in the Democratic Republic of Congo — the last census was carried out in 1984 — and in Burkina Faso — the last census was carried out in 2020 but only covered 70% of the country. Both models showed good predictive performance, indicated by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up, gridded population estimates are currently used for census support and humanitarian response in both countries. This work highlights the flexibility of the bottom-up modeling approach, in terms of input population data, model specification, and aggregation of population estimates to support specific use cases.},
keywords = {Africa, bottom-up modelling, census, Democratic Republic of Congo},
pubstate = {published},
tppubtype = {conference}
}
The population and housing census provides essential demographic information for decision-making and action at local, national and international levels. However, census data in the most vulnerable countries is often outdated or partial because political instability, conflict and natural disasters prevent a national count. The bottom-up modeling approach helps supplement outdated or incomplete census data by estimating population counts and age/sex structures in approximately 100m grid cells using population data collected over a set fully enumerated places and auxiliary geospatial covariates. We present the modeling effort carried out in the Democratic Republic of Congo — the last census was carried out in 1984 — and in Burkina Faso — the last census was carried out in 2020 but only covered 70% of the country. Both models showed good predictive performance, indicated by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up, gridded population estimates are currently used for census support and humanitarian response in both countries. This work highlights the flexibility of the bottom-up modeling approach, in terms of input population data, model specification, and aggregation of population estimates to support specific use cases.
2.
Hay, S. I.; Noor, A. M.; Nelson, A.; Tatem, A. J.
The accuracy of human population maps for public health application Journal Article
In: Tropical Medicine & International Health, vol. 10, no. 10, pp. 1073-1086, 2005.
Abstract | Links | BibTeX | Tags: areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation
@article{https://doi.org/10.1111/j.1365-3156.2005.01487.x,
title = {The accuracy of human population maps for public health application},
author = {S. I. Hay and A. M. Noor and A. Nelson and A. J. Tatem},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-3156.2005.01487.x},
doi = {https://doi.org/10.1111/j.1365-3156.2005.01487.x},
year = {2005},
date = {2005-01-01},
journal = {Tropical Medicine & International Health},
volume = {10},
number = {10},
pages = {1073-1086},
abstract = {Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.},
keywords = {areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation},
pubstate = {published},
tppubtype = {article}
}
Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.
