DOI: 10.5061/dryad.cjsxksnf8
title: Data from: Spatial variation in housing construction material in low- and middle-income countries

description: Housing infrastructure and quality is a major determinant of infectious
 disease risk and other health outcomes in regions of the world where
 vector borne, waterborne and neglected tropical diseases are endemic. It
 is important to quantify the geographical distribution of improvements to
 the major dwelling components to identify and target resources towards
 populations at risk. The aim of this study was to model the sub-national
 spatial variation in housing materials using covariates with quasi-global
 coverage and use the resulting estimates to map the predicted coverage
 across the world’s low- and middle-income countries (LMICs). Data relating
 to the materials used in dwelling construction were sourced from
 nationally representative household surveys conducted since 2005.
 Materials used for construction of flooring, walls, and roof were
 reclassified as improved or unimproved. Households lacking location
 information were georeferenced using a novel methodology, and a suite of
 environmental and demographic spatial covariates were extracted at those
 locations for use as model predictors. Integrated nested Laplace
 approximation (INLA) models were fitted to obtain, and map predicted
 probabilities for each dwelling component. The dataset compiled included
 information from households in 283,000 clusters from 350 surveys. Low
 coverage of improved housing was predicted across the Sahel and southern
 Sahara regions of Africa, much of inland Amazonia, and areas of the
 Tibetan plateau. Coverage of improved roofs and walls was high in the
 Central Asia, East Asia and Pacific and Latin America and the Caribbean
 regions, while improvements in all three components, but most notably
 floors, was low in Sub-Saharan Africa. The strongest determinants of
 dwelling component quality related to urbanization and economic
 development, suggesting that housing improvement programs should focus on
 supply-side interventions that provide the resources for these
 improvements directly to the populations that need them. These findings
 are made available to the reader as files that can be imported into a GIS
 for integration into relevant analysis to derive improved estimates of
 preventable health burdens attributed to housing.
description: # Spatial variation in housing construction material in low- and
 middle-income countries ## Summary of analysis underlying this dataset
 Full background, methods, results and discussion can be found in this
 published
 article: [https://doi.org/10.1371/journal.pgph.0003338](https://doi.org/10.1371/journal.pgph.0003338) ## Description of file structure and contents The resulting model outputs are made available in the DRYAD repository as 6 GeoTIFF files. For each of the three dwelling component outcomes - floor, wall and roof materials - there is a file containing the predicted coverage values and a second containing the standard error of those predictions at all locations in eligible countries at a 5 decimal degree resolution. Each GeoTIFF contains a single band corresponding to the variable value. ## Definitions of all variables and units The definitions of the variables in each file are as follows: * Housing_materials_supplementary_file_S3a_Floors.tif - The predicted percentage coverage of improved floor material. * Housing_materials_supplementary_file_S3b_Floors_SE.tif - The standard error of the predicted percentage coverage of improved floor material. * Housing_materials_supplementary_file_S3c_Roofs.tif - The predicted percentage coverage of improved roof material. * Housing_materials_supplementary_file_S3d_Roofs_SE.tif - The standard error of the predicted percentage coverage of improved roof material. * Housing_materials_supplementary_file_S3e_Walls.tif - The predicted percentage coverage of improved wall material. * Housing_materials_supplementary_file_S3f_Walls_SE.tif - The standard error of the predicted percentage coverage of improved wall material. Predictions and standard errors are for all LMICs outside of Europe and are derived from integrated nested Laplace approximation (INLA) models fitted to household survey data. The units are percentages ranging from 0 - 100%. ## Other sources that the data was derived from The input data is publicly available, third party-owned data sourced from the following household survey programs: | Source | URL | | :------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- | | Demographic and Health Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | Malaria Indicator Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | AIDS Indicator Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | Multiple Indicator Cluster Survey | [https://mics.unicef.org/surveys](https://mics.unicef.org/surveys) | | Botswana Family Health Survey | [https://microdata.statsbots.org.bw/index.php/catalog/9](https://microdata.statsbots.org.bw/index.php/catalog/9) | | Encuesta Nicaragüense de Demografía y Salud | [https://www.inide.gob.ni/Home/endesa](https://www.inide.gob.ni/Home/endesa) | | Encuesta Nacional de Salud y Nutrición | [https://www.salud.gob.ec/encuesta-nacional-de-salud-y-nutricion-ensanut/](https://www.salud.gob.ec/encuesta-nacional-de-salud-y-nutricion-ensanut/) | | Pesquisa Nacional de Demografia e Saúde da Criança e da Mulher | [https://bvsms.saude.gov.br/bvs/pnds/](https://bvsms.saude.gov.br/bvs/pnds/) | The covariate datasets and the sources are listed in the accompanying article. ## Software The files can be imported to and analyzed in ArcGIS Pro, QGIS or any other standard GIS software. ## Version changes **15-oct-2024:** Fixed some discrepancies in the resolutions of the rasters, so that now they are all in the same 5 decimal degree resolution.