00000nam a2200000 i 4500 UP-8027390931311530544 Buklod 20250701082250.0 m |o d | cr ||||||||||| 250127s2023 xx a b 001 0 eng 9781032347714 (hardback : acid-free paper) (Buklod)UP-1685954869149809328 E-38450 Linar Php8,031.60 Asst. Prof. Ayin Tamondong DGE DLC DENG rda eng G 70.217 G46 W56 2023 Wimberly, Michael C. author. Geographic data science with R visualizing and analyzing environmental change Michael C. Wimberly. Boca Raton CRC Press [2023] xxv, 283 pages illustrations (chiefly color) 25 cm text rdacontent unmediated rdacontent volume rdacarrier Includes bibliographies and index. "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. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R, it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R." Geospatial data Computer processing. Environmental monitoring Data processsing. R (Computer program language) Geographic information systems: theory and applications GIM 165 College of Engineering, University of the Philippines Diliman. Principles of geographic information systems GmE 203 College of Engineering, University of the Philippines Diliman. FO UPD DENG G 70.217 G46 W56 2023 Book