Hi All, I have published an online tutorial related to spatial-data analysis with R (https://zia207.github.io/geospatial-r-github.io/). Nothing is new here, I have just organized several R-code and data that I have used in my several publications. Most of the codes were written with the help of postings in several online blogs: such as R-sig-Geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>, Stack Overflow <https://stackoverflow.com/>, and R bloggers <https://www.r-bloggers.com/update-can-we-predict-flu-outcome-with-machine-learning-in-r/> and on-line tutorials such as Spatial Data Science <https://rspatial.org/index.html> and Geostatistics & Open-source statistical computing <http://www.css.cornell.edu/faculty/dgr2/teach/degeostats.html>. I think It would help someone we have no prior knowledge of GIS, remote sensing or any other area of geoinformatics, but have some experience in R-coding. The data used in this tutorial also available for download. I appreciate any feedback for improving this tutorial. I appreciate any feedback for improving this tutorial. Best Zia Ahmed University at Buffalo Tutorial consist following topics: *1. **Spatial Data Processing* <https://zia207.github.io/geospatial-r-github.io/about.html> - Reading and Writing Spatial Data <https://zia207.github.io/geospatial-r-github.io/read-write-spatial-data.html> - Vector data - Raster data - Map Projection and Coordinate Reference Systems <https://zia207.github.io/geospatial-r-github.io/map-projection-coordinate-reference-systems.html> - Geographic coordinate system (GCS) - Projected coordinate system - Coordinate Reference System in R - Geoprocessing of Vector data <https://zia207.github.io/geospatial-r-github.io/geoprocessing-vector-data.html> - Clipping - Union - Dissolve - Intersect - Erase - Convex Hull - Buffer - Working with Spatial Point Data <https://zia207.github.io/geospatial-r-github.io/working-with-spatial-point-data.html> - Create a Spatial Point Data Frame - Extract Environmental Covariates to SPDF - Create a Prediction Grid - Exploratory Data Analysis - Plot Data on Web Map - Working with Spatial Polygon Data <https://zia207.github.io/geospatial-r-github.io/working-with-spatial-polygon.html> - Data Processing - Visualization - Animation of Time Series Data - Working with Raster Data <https://zia207.github.io/geospatial-r-github.io/working-with-raster-data.html> - Basic Raster Operation - Clipping - Reclassification - Focal Statistics - Raster Algebra - Aggregation - Resample - Mosaic - Convert Raster to Point Data - Convert Point Data to Raster - Raster Stack and Raster Brick - Digital Terrain Modeling - Slope - Aspect - Hillshade - Terrain Ruggedness Index - Topographic Position Index - Roughness - Curvature - Flow Direction - netCDF Data Processing <https://zia207.github.io/geospatial-r-github.io/netCDF-data-processing.html> *2. **Spatial Statistics* <https://zia207.github.io/geospatial-r-github.io/spatial-statistics.html> - Spatial Autocorrelation <https://zia207.github.io/geospatial-r-github.io/spatial-autocorrelation.html> - Moran?s I - Geary?s C - Getis?s Gi - Point Pattern Analysis <https://zia207.github.io/geospatial-r-github.io/point-pattern-analysis.html> - Geographically Weighted Mmodels <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-models.html> - Geographically Weighted Summary Statistics <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-summary-statistics.html> - Geographically Weighted Principal Components Analysis <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-principal-components-analysis.html> - Geographically Weighted Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-regression.html> - Geographically Weighted OLS Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-ols-regression.html> - Geographically Weighted Poisson Regression <https://zia207.github.io/geospatial-r-github.io/geographically-weighted-poisson-regression.html> - Global and local (Geographically Weighted) Random Forest <https://zia207.github.io/geospatial-r-github.io/geographically-wighted-random-forest.html> *3. **Spatial Interpolation* <https://zia207.github.io/geospatial-r-github.io/spatial-interpolation.html> ? Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/spatial-interpolation.html> o Deterministic Methods for Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/deterministic-methods-for-spatial-interpolation.html> ? Polynomial Trend Surface ? Proximity Analysis-Thiessen Polygons ? Nearest Neighbor Interpolation ? Inverse Distance Weighted ? Thin Plate Spline o Geostatistical Methods for Spatial Interpolation <https://zia207.github.io/geospatial-r-github.io/geostatistical-methods-for-spatial-interpolation.html> ? Semivariogram Modeling <https://zia207.github.io/geospatial-r-github.io/semivariogram-modeling.html> ? Kriging <https://zia207.github.io/geospatial-r-github.io/kriging.html> ? Ordinary Kriging <https://zia207.github.io/geospatial-r-github.io/ordinary-kriging.html> ? Universal Kriging <https://zia207.github.io/geospatial-r-github.io/universal-kriging.html> ? Co-Kriging <https://zia207.github.io/geospatial-r-github.io/cokriging.html> ? Regression kriging <https://zia207.github.io/geospatial-r-github.io/regression-kriging.html> ? Generalized Linear Model ? Random Forest ? Meta Ensemble Machine Learning ? Indicator kriging <https://zia207.github.io/geospatial-r-github.io/indicator-kriging.html> ? Assessing the Quality of Spatial Predictions <https://zia207.github.io/geospatial-r-github.io/assessing-quality-spatial-predictions.html> o Cross-validation <https://zia207.github.io/geospatial-r-github.io/cross-validation.html> o Validation with an Independent Dataset <https://zia207.github.io/geospatial-r-github.io/validation-independent-dataset.html> o Conditional Simulation for Spatial Uncertainty <https://zia207.github.io/geospatial-r-github.io/conditional-simulation-spatial-uncertainty.html> *4. **Remote Sensing Data Processing and Analysis* <https://zia207.github.io/geospatial-r-github.io/about-c.html> ? Remote Sensing Basic <https://zia207.github.io/geospatial-r-github.io/reomte-sensing-basic.html> ? Landsat 8 Image Processing & Visualization <https://zia207.github.io/geospatial-r-github.io/landsat-8-image-processing.html> o RGB image comparison o Pan Sharpening or Image Fusion o Radiometric Calibration and Atmospheric Correction ? Spectral Indices <https://zia207.github.io/geospatial-r-github.io/spectral-indices.html> o Normalized Difference Vegetation Index o Soil Adjusted Vegetation Index (SAVI) o Modified soil Adjusted Vegetation Index (MSAVI) o Enhanced Vegetation Index (EVI) o Two-bands Enhanced Vegetation (EVI2) o Normalized Difference Water Index (NDWI) ? Green Ground Cover from UAV Images <https://zia207.github.io/geospatial-r-github.io/uav-ground-cover.html> ? Texture Analysis <https://zia207.github.io/geospatial-r-github.io/texture-analysis.html> ? Image Classification <https://zia207.github.io/geospatial-r-github.io/image-classification.html> o Ground Truth Data Processing <https://zia207.github.io/geospatial-r-github.io/ground-truth-data-processing.html> o Unsupervised Classification <https://zia207.github.io/geospatial-r-github.io/unsupervised-classification.html> o Supervised Classification <https://zia207.github.io/geospatial-r-github.io/supervised-classification.html> ? Random Forest <https://zia207.github.io/geospatial-r-github.io/random-forest.html> ? Support Vector Machine <https://zia207.github.io/geospatial-r-github.io/support-vector-machine.html> ? Na?ve Bayes <https://zia207.github.io/geospatial-r-github.io/naive-bayes.html> ? eXBoost <https://zia207.github.io/geospatial-r-github.io/exboost.html> ? Deep Learning-H2O <https://zia207.github.io/geospatial-r-github.io/deep-learning-h2o.html> ? Stack-Ensemble-H20 <https://zia207.github.io/geospatial-r-github.io/stack-ensemble-h2o.html> ? Deep Learning Keras-TensorFlow <https://zia207.github.io/geospatial-r-github.io/deep-learning-keras-tensorflow.html>
R-Tutorial: Geospatial Data Science in R (Resent)
1 message · Zia Ahmed