R-Tutorial: Geospatial Data Science in R (Resent)

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R-Tutorial: Geospatial Data Science in R (Resent)

Zia Ahmed
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>

§  Naïve 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>

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