<p>These files represent the source code and technical fitting details of the Random Forest-based population mapping algorithm as descrbed in Stevens, et al. (2015). Though the randomForest R package provides the functionality to fit a model with an arbitrarily large number of covariates and observations (limited only by memory and disk space) a limiting feature of our approach is the time spent during the prediction phase. This code and sample data provides the details of a data reduction method that greatly increases the prediction-phase for new data, necessitated by running per-pixel predictions on large countries for WorldPop population mapping products.</p
Here we introduce the popRF package in R that largely addresses these issues. This is done by functi...
In the current big data era, naive implementations of well-known learning algorithms cannot efficien...
High-resolution gridded population data are important for understanding and responding to many socio...
<p>These files are supplementary information to illustrate the metadata reports and default visualiz...
<p>The source stored here represent the Python and R scripts backing the WorldPop project's mapping ...
wpgpRFPMS is a population modelling R script utilizing Random Forests to inform a dasymetric redistr...
Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditional...
High resolution, contemporary data on human population distributions are vital for measuring impacts...
High resolution, contemporary data on human population distributions are vital for measuring impacts...
This package gives users the ability to produce gridded population density estimates using a Random ...
International audienceThis book offers an application-oriented guide to random forests: a statistica...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This package gives users the ability to produce gridded population density estimates using a Random ...
<div><p>High resolution, contemporary data on human population distributions are vital for measuring...
Random Forests are an effective ensemble method which is becoming increasingly popular, particularly...
Here we introduce the popRF package in R that largely addresses these issues. This is done by functi...
In the current big data era, naive implementations of well-known learning algorithms cannot efficien...
High-resolution gridded population data are important for understanding and responding to many socio...
<p>These files are supplementary information to illustrate the metadata reports and default visualiz...
<p>The source stored here represent the Python and R scripts backing the WorldPop project's mapping ...
wpgpRFPMS is a population modelling R script utilizing Random Forests to inform a dasymetric redistr...
Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditional...
High resolution, contemporary data on human population distributions are vital for measuring impacts...
High resolution, contemporary data on human population distributions are vital for measuring impacts...
This package gives users the ability to produce gridded population density estimates using a Random ...
International audienceThis book offers an application-oriented guide to random forests: a statistica...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This package gives users the ability to produce gridded population density estimates using a Random ...
<div><p>High resolution, contemporary data on human population distributions are vital for measuring...
Random Forests are an effective ensemble method which is becoming increasingly popular, particularly...
Here we introduce the popRF package in R that largely addresses these issues. This is done by functi...
In the current big data era, naive implementations of well-known learning algorithms cannot efficien...
High-resolution gridded population data are important for understanding and responding to many socio...