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DTSTART;TZID=America/Chicago:20231025T101500
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SUMMARY:Large-scale Geospatial Data Analysis and Visualization in R
DESCRIPTION:TAMIDS Data Science Ambassador Workshop SeriesLarge-scale Geospatial Data Analysis and Visualization in R\n\n\n\n\n\n\n\n\n\nIn-person Location: BLOC 220Zoom Link: https://tamu.zoom.us/j/94200148283?pwd=aCs5SjdZbFAzb2xnekpFQ1VmNmFWdzMeeting ID: 942 0014 8283 Passcode: 986864Fill out the form to register: https://docs.google.com/forms/d/e/1FAIpQLSebLgZgfUMEyM54LdPA0Wue0-7eO688Hocs7rpB5h5LEup4bw/viewform \n\n\n\n\n\n\n\nSummary: Analysis of large-scale geospatial data (regional to global scale at high spatial and temporal resolution) can be computationally expensive and time-consuming\, especially when working with multiple formats and sources of data. R provides a powerful computational alternative to popular Geographic Information System (GIS) software to organize\, analyze and visualize geospatial data. R enjoys a vast collection of open-source libraries for GIS-type operations and proven statistical analysis and data visualization capabilities. Taking examples from global satellite data in gridded/raster format\, we will demonstrate several geospatial operations like projections\, resampling\, spatial extraction\, cropping\, masking\, etc. using rasters\, shapefiles\, and spatial data frames. For a seamless analysis across different data types and platforms\, conversion to/from different data formats like data frames\, matrices\, rasters\, and structured data like NetCDF will be discussed. Advanced topics will include working with data cubes (terra spatrasters)\, layer-wise operations on data cubes\, cell-wise operations on raster time series by implementing user-defined functions with stackApply. Multicore parallel application (parApply and clusterR family) of user-defined functions in R will be demonstrated for large-scale dataset. \n\n\n\n\nInstruction type: Hands-on\n\n\n\nCourse level: Intermediate\n\n\n\nCourse requirements: Computer with installed and working R and RStudio (a.k.a. Posit). Working experience in R is required.\n\n\n\nFormat: Hybrid (In-person + Online) \n\n\n\n\nLearning Objectives \n\n\n\n\nPlotting and visualizing geospacial data\n\n\n\nEfficient GIS and statistical operations on large geospatial datasets\n\n\n\nApplication of custom functions for large-scale gridded/spatial analysis\n\n\n\nParallelization of geospatial operations in R\n\n\n\n\nHosts \n\n\n\n\nDebasish Mishra\, Data Science Ambassador\, TAMIDS\, debmishra@tamu.edu\n\n\n\nLeah Kocian\, PhD Student\, BAEN\, leahkay4@tamu.edu\n\n\n\n\nInstructors \n\n\n\n\nDebasish Mishra is a Ph.D. student at the Biological and Agricultural Engineering Department at Texas A&M University. Debashish studies the coupling of global scale water and energy cycles\, and inferring predictive skills for flash floods and droughts. His research interests include hydro-climatology\, soil science\, remote sensing\, data fusion\, and Machine Learning. \n\n\n\n\n\nLeah Kocian is a Ph.D. student at the Biological and Agricultural Engineering Department at Texas A&M University. Her research is centered on the investigation of contaminant fate and transport within urban garden root zones\, with a specific focus on its potential implications for human health. Her methodology encompasses a suite of experimental techniques. She supplements these methods with advanced modeling and machine learning approaches by utilizing existing functions and developing new ones in R. \n\n\n\n\n\nAidan Brown is a 4th year undergraduate at Texas A&M University majoring in Geographic Information Science and Technology. He has research experience in geospatial analysis\, remote sensing\, and statistical modeling. He is interested in large-scale data analysis\, geospatial image processing\, and environmental modeling. \n\n\n\n\n\n\n\n\nClick here to register \n\n\n\nTAMIDS Data Science Ambassador Scholarship Program \n\n\n\n\nThe Data Science Ambassador Scholarship Program is supported in part by external sponsorship. We gratefully acknowledge support from TAMIDS Industry Affiliates Chevron and ConocoPhillips for this and other student education\, research\, and engagement programs. \n\n\n\n\nLink to PDF Version
URL:https://tamids.tamu.edu/event/large-scale-geospatial-data-analysis-and-visualization-in-r/
CATEGORIES:TAMIDS Event
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