spatstat.explore

Exploratory/nonparametric data analysis for the spatstat family

The original spatstat package has been split into several sub-packages (See spatstat/spatstat)

This package spatstat.explore is one of the sub-packages. It contains the main user-level functions that perform exploratory and nonparametric statistical analysis of spatial data, with the exception of data on linear networks.

Most of the functionality is for spatial point patterns in two dimensions. There is a very modest amount of functionality for 3D and higher dimensional patterns and space-time patterns.

Overview

spatstat.explore supports

• data manipulation and exploratory graphics
• exploratory analysis
• smoothing
• cluster detection
• nonparametric estimation
• hypothesis tests (simulation-based and nonparametric)

Detailed contents

For a full list of functions, see the help file for spatstat.explore-package.

Exploratory analysis

• Clark-Evans index, Hopkins-Skellam index
• quadrat counting estimates of intensity, quadrat counting test
• Fry plot
• Morisita plot
• scan statistic
• cluster detection (Allard-Fraley cluster set, Byers-Raftery cleaning)

Nonparametric estimation of trend

• kernel estimation of intensity of a point pattern
• kernel smoothing of mark values attached to point locations
• kernel estimation of relative risk
• kernel smoothing of a line segment pattern
• bandwidth selection
• spatial CDF
• nonparametric estimation of intensity as a function of a covariate
• ROC curve, AUC
• Sufficient Data Reduction
• optimal thresholding of a covariate

Nonparametric estimation of dependence between points

• summary functions (K-function, pair correlation function, empty space function, nearest neighbour distance function, J-function, etc) and multi-type versions of these functions
• mark correlation function, mark independence diagnostoc
• local summary functions (LISA)
• simulation envelopes of summary functions
• manipulation of summary functions (plot, evaluate, differentiate, smooth etc)
• spatial bootstrap

Formal inference

• hypothesis tests (quadrat test, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, envelope tests, Dao-Genton test, balanced independent two-stage test)

Data manipulation

• image blurring
• Choi-Hall data sharpening of point locations
• transects of an image along a line or curve
• programming tools