ArticlePloS one2018
Predicting residential structures from open source remotely enumerated data using machine learning.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty.Scientific reports · 2025Article
- Unsupervised detection of building destruction during war from publicly available radar satellite imagery.PNAS nexus · 2025Article
- Assessing the impact of building footprint dataset choice for health programme planning: a case study of indoor residual spraying (IRS) in Zambia.International journal of health geographics · 2025Article
- Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.BMC medical informatics and decision making · 2025Article
- Global population datasets overestimate flood exposure in Sweden.Scientific reports · 2024Article
- A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap.Nature communications · 2023Article
- EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings.Scientific data · 2023Article
- Article
- Predicting building types using OpenStreetMap.Scientific reports · 2022Article
- How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia.PloS one · 2022Article
- Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot.PloS one · 2021Article
- Estimating malaria chemoprevention and vector control coverage using program and campaign data: A scoping review of current practices and opportunities.Journal of global health · 2020Article
- Finding hotspots: development of an adaptive spatial sampling approach.Scientific reports · 2020Article
- Learning from urban form to predict building heights.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Having accurate maps depicting the locations of residential buildings across a region benefits a range of sectors. This is particularly true for public health programs focused on delivering services at the household level, such as indoor residual spraying with insecticide to help prevent malaria. While open source data from OpenStreetMap (OSM) depicting the locations and shapes of buildings is rapidly improving in terms of quality and completeness globally, even in settings where all buildings have been mapped, information on whether these buildings are residential, commercial or another type is often only available for a small subset. Using OSM building data from Botswana and Swaziland, we identified buildings for which 'type' was indicated, generated via on the ground observations, and classified these into two classes, "sprayable" and "not-sprayable". Ensemble machine learning, using building characteristics such as size, shape and proximity to neighbouring features, was then used to form a model to predict which of these 2 classes every building in these two countries fell into. Results show that an ensemble machine learning approach performed marginally, but statistically, better than the best individual model and that using this ensemble model we were able to correctly classify >86% (using independent test data) of structures correctly as sprayable and not-sprayable across both countries.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.