Introduction to Environmental Data Science focuses on data science
methods in the R language applied to environmental research, with
sections on exploratory data analysis in R including data abstraction,
transformation, and visualization; spatial data analysis in vector and
raster models; statistics and modelling ranging from exploratory to
modelling, considering confirmatory statistics and extending to machine
learning models; time series analysis, focusing especially on carbon and
micrometeorological flux; and communication. Introduction to
Environmental Data Science is an ideal textbook to teach undergraduate
to graduate level students in environmental science, environmental
studies, geography, earth science, and biology, but can also serve as a
reference for environmental professionals working in consulting, NGOs,
and government agencies at the local, state, federal, and international
levels.
Features
- Gives thorough consideration of the needs for environmental research
in both spatial and temporal domains.
- Features examples of applications involving field-collected data
ranging from individual observations to data logging.
- Includes examples also of applications involving government and NGO
sources, ranging from satellite imagery to environmental data collected
by regulators such as EPA.
- Contains class-tested exercises in all chapters other than case
studies. Solutions manual available for instructors.
- All examples and exercises make use of a GitHub package for functions
and especially data.
