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The definition of complexity through Statistical Complexity Measures (SCM) has recently seen major improvements. Mostly, the effort is concentrated in measures on time series. We propose a SCM definition for spatial dynamical systems. Our definition is in line with the trend to combine entropy with measures of structure (such as disequilibrium). We study the behaviour of our definition against the vectorial noise model of Collective Motion. From a global perspective, we show how our SCM is minimal at both the microscale and macroscale, while it reaches a maximum at the ranges that define the mesoscale in this model. From a local perspective, the SCM is minimum both in highly ordered and disordered areas, while it reaches a maximum at the edges between such areas. These characteristics suggest this is a good candidate for detecting the mesoscale of arbitrary dynamical systems as well as regions where the complexity is maximal in such systems. [ABSTRACT FROM AUTHOR]

Abstract: The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping. [Copyright &y& Elsevier]

Abstract: Objective: In EEG/MEG experiments, increasing the number of sensors improves the spatial resolution of the results. However, the standard statistical methods are inappropriate for these multivariate, highly correlated datasets. We introduce a procedure to identify spatially extended scalp fields that correlate with some external, continuous measure (reaction-time, performance, clinical status) and to test their significance. Methods: We formally deduce that the channel-wise covariance of some experimental variable with scalp field data directly represents intracerebral sources associated with that variable. We furthermore show how the significance of such a representation can be tested with resampling techniques. Results: Simulations showed that depending on the number of channels and subjects, effects can be detected already at low signal to noise ratios. In a sample analysis of real data, we found that foreign-language evoked ERP data were significantly associated with foreign-language proficiency. Inverse solutions of the extracted covariances pointed to sources in language-related areas. Conclusions: Covariance mapping combined with bootstrapping methods has high statistical power and yields unique and directly interpretable results. Significance: The introduced methodology overcomes some of the ‘traditional’ statistical problems in EEG/MEG scalp data analysis. Its application can improve the reproducibility of results in the field of EEG/MEG. [Copyright &y& Elsevier]

Network systems evolving by preferential attachment of new vertices and by link loading are considered. The probability of node attaching is assumed to depend linearly on degrees of existing nodes to which new ones are connected. The rule of the network loading allows both deterministic and random processes of distributing weights among links. Specifically, the rule is determined by assigning to each link a weight dependent on randomly modified intervals between times at which linked vertices are connected to the system. This enables the investigation of the influence of local and global loading processes on statistical properties of weighted networks. It is demonstrated that, for different values of parameters controlling these processes, the distribution of weights concentrated at vertices of such networks displays a large variety of scale-free and truncated scale-free behaviors. It is also shown that statistical properties of the studied weighted networks and statistical properties of some complex evolving spatial systems exhibit a close relationship. [Copyright &y& Elsevier]