Working with Spatial Data
A collection of notes, examples, references, and thoughts on working with spatial data.
- Customizing Maps in R: spplot() and latticeExtra functions
- Converting Alpha-Shapes into SP Objects
- Some Ideas on Interpolation of Categorical Data
- Visual Interpretation of Principal Coordinates (of) Neighbor Matrices (PCNM)
- Visualizing Random Fields and Select Components of Spatial Autocorrelation
- Generation of Sample Site Locations [sp package for R]
- Target Practice and Spatial Point Process Models
- Ordinary Kriging Example: GRASS-R Bindings
- Point-process modelling with the sp and spatstat packages
- Simple Map Creation
Software
- General Purpose Programming with Scripting Languages
- LaTeX Tips and Tricks
- PostGIS: Spatially enabled Relational Database Sytem
- PROJ: forward and reverse geographic projections
- GDAL and OGR: geodata conversion and re-projection tools
- R: advanced statistical package
- Access Data Stored in a Postgresql Database
- Additive Time Series Decomposition in R: Soil Moisture and Temperature Data
- Aggregating SSURGO Data in R
- Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset
- Color Functions
- Comparison of Slope and Intercept Terms for Multi-Level Model
- Comparison of Slope and Intercept Terms for Multi-Level Model II: Using Contrasts
- Creating a Custom Panel Function (R - Lattice Graphics)
- Customized Scatterplot Ideas
- Estimating Missing Data with aregImpute() {R}
- Exploration of Multivariate Data
- Interactive 3D plots with the rgl package
- Making Soil Property vs. Depth Plots
- Numerical Integration/Differentiation in R: FTIR Spectra
- Plotting XRD (X-Ray Diffraction) Data
- Using lm() and predict() to apply a standard curve to Analytical Data
- Working with Spatial Data
- Customizing Maps in R: spplot() and latticeExtra functions
- Converting Alpha-Shapes into SP Objects
- Some Ideas on Interpolation of Categorical Data
- Visual Interpretation of Principal Coordinates (of) Neighbor Matrices (PCNM)
- Visualizing Random Fields and Select Components of Spatial Autocorrelation
- Generation of Sample Site Locations [sp package for R]
- Target Practice and Spatial Point Process Models
- Ordinary Kriging Example: GRASS-R Bindings
- Point-process modelling with the sp and spatstat packages
- Simple Map Creation
- Comparison of PSA Results: Pipette vs. Laser Granulometer
- GRASS GIS: raster, vector, and imagery analysis
- Generic Mapping Tools: high quality map production
