Intensity Estimate of Point Pattern Using Tessellation
Computes an adaptive estimate of the intensity function of a point pattern.
adaptive.density(X, f = 0.1, ..., nrep = 1)
- Point pattern dataset (object of class
- Fraction (between 0 and 1) of the data points that will be removed from the data and used to determine a tessellation for the intensity estimate.
- Arguments passed to
as.imdetermining the pixel resolution of the result.
- Number of independent repetitions of the randomised procedure.
This function is an alternative to
computes an estimate of the intensity function of a point pattern
X is randomly split into two patterns
B containing a fraction
of the original data. The subpattern
A is used to construct a
Dirichlet tessellation (see
dirichlet). The subpattern
B is retained for counting. For each tile of the Dirichlet
tessellation, we count the number of points of
B falling in the
tile, and divide by the area of the same tile, to obtain an estimate
of the intensity of the pattern
B in the tile.
This estimate is divided by
1-f to obtain an estimate
of the intensity of
X in the tile. The result is a pixel image
of intensity estimates which are constant on each tile of the tessellation.
nrep is greater than 1, this randomised procedure is
nrep times, and the results are averaged.
This technique has been used by Ogata et al. (2003), Ogata (2004) and Baddeley (2007).
- A pixel image (object of class
"im") whose values are estimates of the intensity of
Baddeley, A. (2007) Validation of statistical models for spatial point patterns. In J.G. Babu and E.D. Feigelson (eds.) SCMA IV: Statistical Challenges in Modern Astronomy IV, volume 317 of Astronomical Society of the Pacific Conference Series, San Francisco, California USA, 2007. Pages 22--38.
Ogata, Y. (2004) Space-time model for regional seismicity and detection of crustal stress changes. Journal of Geophysical Research, 109, 2004.
Ogata, Y., Katsura, K. and Tanemura, M. (2003). Modelling heterogeneous space-time occurrences of earthquake and its residual analysis. Applied Statistics 52 499--509.