# Target Practice and Spatial Point Process Models

Jun 11, 2007 metroadmin**Overview:**

Simple application of spatial point-process models to spread patterns after some backyard target practice. Note that only a cereal box and 2 sheets of graph paper were injured in this exercise. Data files are attached at the bottom of this page; all distance units are in cm.

A simple experiment was conducted, solely for the purpose of collecting semi-random coordinates on a plane, where a target was hit with 21 shots from a distance of 15 and 30 feet. The `ppm()` function (*spatstat* package) in R was used to create point density maps, along with a statistical description of the likelihood of where each target would be hit were the experiment to be conducted again (via point-process modeling). While normally used to model the occurrence of natural phenomena or biological entities, point-process models can be used to analyze one's relative accuracy at set target distances. One more way in which remote sensing or GIS techniques can be applied to smaller, non-georeferenced coordinate systems.

**Load Data and Compute Density Maps:**

**Fit Point-Process Models:**

**Tidy-up:**

### Attachments:

### Links:

Some Ideas on Interpolation of Categorical Data

Working with Spatial Data

Visual Interpretation of Principal Coordinates (of) Neighbor Matrices (PCNM)

## 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