Spatial variation in housing construction material in low- and middle-income countries

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 environment..., , , # 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 co...,