The globally important carbon sink of intact, old-growth tropical humid forests is declining
because of climate change, deforestation and degradation from fire and logging …

Geocomputation with R is for people who want to analyze, visualize and model geographic
data with open source software. It is based on R, a statistical programming language that …

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We
designed an innovative and extensive mobile monitoring campaign to characterize TRAP …

Urban microclimate exerts an increasing influence on urban buildings, energy, and
sustainability. This study uses 10-year measured hourly weather data at 27 sites in San …

Over the past decades, mitigation strategies have been adopted both by federal and state
agencies in the United States (US) to improve air quality. Between 2007 and 2009, the US …

It is quite common in digital soil mapping (DSM) to quantify the uncertainty of issued
predictions, that is to make probabilistic predictions. Yet, little attention has been paid to its …

Three-dimensional digital soil mapping (3D-DSM) quantifies both the horizontal and the
vertical variability of soil properties. Most current studies in 3D-DSM were based on either …

Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly
becoming feasible in many environmental applications due to the recent advances in both …

Spatial data is crucial to improve decision-making in a wide range of fields including
environment, health, ecology, urban planning, economy, and society. Spatial Statistics for …

Digital soil mapping (DSM) involves the use of georeferenced information and statistical
models to map predictions and uncertainties related to soil properties. Many remote regions …