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![Geographic Data Science with R: Visualizing and Analyzing Environmental Change](/uploads/posts/2023-02/1676395623_701_g_ographic_data_sci_nc__with_r.jpg)
Автор: Michael C. Wimberly
Издательство: CRC Press
Серия: Data Science Series
Год: 2023
Страниц: 310
Язык: английский
Формат: pdf (true)
Размер: 44.1 MB
The burgeoning field of Data Science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic Data Science. Other critical steps include screening for suspect data values, handling missing data, harmonizing data from multiple sources, summarizing the data, and visualizing data and analysis results. Although there are many books available on statistical and Machine Learning methods, few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment, which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular, vector, and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems.