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The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points.
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This study illustrates the importance of considering spatial information for outbreak detection, and demonstrates that using an interpoint distance distribution and precise address locations is a powerful approach.
The resulting dipolar coupling evolution data was analysed by Tikhonov regularization to a distance distribution using DeerAnalysis2006 developed and made freely available by Gunnar Jeschke (http://www.epr.ethz.ch/software/index) [ 18].
Microarray expression data were extracted using the Robust Multichip Average (RMA) method [ 17] and were analyzed using R. Prior to microarray analysis, one pair of male subjects was removed as one sample failed the quality control and was considered as an outlier using the distance distribution clustering analysis.
Further extensions to contractive mappings in complete fuzzy metric spaces using generalized distance distribution functions have been studied in [8, 9] and references therein.
We then fitted each of the 46,130 distance distributions using a negative binomial distribution function, and generated the corresponding cumulative density functions giving the p value as a function of the city block distance (Fig. 1i).
As a secondary check, we used the distance distribution plots provided by MotIV.
Then, we randomly chose the number of long distance dispersers using a normal distribution with the previously determined mean and standard deviation.
Each recorded location was resampled into 50 particles randomly selected using a bivariate normal distribution of distance (with μ = 0 and SD = error).
Two step analysis methods were used: the pairwise distance distribution (PWD) (Abbondanzieri et al., 2005; Dumont et al., 2006; Moffitt et al., 2009) and the step-fitting algorithm developed by Kerssemakers et al. (2006).
Since the typical cluster of points is not circular the distribution of points has been characterised using a Mahalanobis distance.
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