Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Spatial tuning studies of the pattern evoked electroretinogram

The locus of origin of the pattern evoked electroretinogram, (PERG), has been the subject of considerable discussion. A novel approach was adopted in this study to further elaborate the nature of the PERG evoked by pattern onset/offset presentation. The PERG was found to be linearly related to stimulus contrast and in particular was linearly related to the temporal contrast of the retinal image, when elicited by patterns of low spatial frequency. At high spatial frequencies the retinal image contrast is significantly reduced because of optical degradation. This is described by the eye's modulation transfer function (MTF). The retinal contrast of square wave grating and chequerboard patterns of increasing spatial frequency were found by filtering their Fourier transforms by the MTF. The filtered pattern harmonics were then resynthesised to constitute a profile of retinal image illuminance from which the temporal and spatial contrast of the image could be calculated. If the PERG is a pure illuminance response it should be spatially insensitive and dependent upon the temporal contrast of stimulation. The calculated loss of temporal contrast for finer patterns was expressed as a space-averaged temporal contrast attentuation factor. This factor, applied to PERGs evoked by low spatial frequency patterns, was used to predict the retinal illuminance response elicited by a finer pattern. The predicted response was subtracted from the recorded signal and residual waveform was proposed to represent specific activity. An additional correction for the attenuation of spatial contrast was applied to the extracted pattern specific response. Pattern specific responses computed for different spatial frequency patterns in this way are the predicted result of iso-contrast pattern stimulation. The pattern specific responses demonstrate a striking bandpass spatial selectivity which peaks at higher spatial frequencies in the more central retina. The variation of spatial sensitivity with eccentricity corresponds closely with estimated ganglion receptive field centre separation and psychophysical data. The variation of retinal structure with eccentricity, in the form of the volumes of the nuclear layers, was compared with the amplitudes of the computed retinal illuminance and pattern specific responses. The retinal illuminance response corresponds more closely to the outer and inner nuclear layers whilst the pattern specific response appears more closely related to the ganglion cell layer. In general the negative response transients correspond to the more proximal retinal layers. This thesis therefore supports the proposed contribution of proximal retinal cell activity to the PERG and describes techniques which may be further elaborated for more detailed studies of retinal receptive field dimensions.

Pooling and suppression in human spatial vision

A distinct feature of several recent models of contrast masking is that detecting mechanisms are divisively inhibited by a broadly tuned ‘gain pool’ of narrow-band spatial pattern mechanisms. The contrast gain control provided by this ‘cross-channel’ architecture achieves contrast normalisation of early pattern mechanisms, which is important for keeping them within the non-saturating part of their biological operating characteristic. These models superseded earlier ‘within-channel’ models, which had supposed that masking arose from direct stimulation of the detecting mechanism by the mask. To reveal the extent of masking, I measured the levels produced with large ranges of pattern spatial relationships that have not been explored before. Substantial interactions between channels tuned to different orientations and spatial frequencies were found. Differences in the masking levels produced with single and multiple component mask patterns provided insights into the summation rules within the gain pool. A widely used cross-channel masking model was tested on these data and was found to perform poorly. The model was developed and a version in which linear summation was allowed between all components within the gain pool but with the exception of the self-suppressing route typically provided the best account of the data. Subsequently, an adaptation paradigm was used to probe the processes underlying pooled responses in masking. This delivered less insight into the pooling than the other studies and areas were identified that require investigation for a new unifying model of masking and adaptation. In further experiments, levels of cross-channel masking were found to be greatly influenced by the spatio-temporal tuning of the channels involved. Old masking experiments and ideas relying on within-channel models were re-elevated in terms of contemporary cross-channel models (e.g. estimations of channel bandwidths from orientation masking functions) and this led to different conclusions than those originally arrived at. The investigation of effects with spatio-temporally superimposed patterns is focussed upon throughout this work, though it is shown how these enquiries might be extended to investigate effects across spatial and temporal position.

The Riesz transform and simultaneous representations of phase, energy and orientation in spatial vision

To represent the local orientation and energy of a 1-D image signal, many models of early visual processing employ bandpass quadrature filters, formed by combining the original signal with its Hilbert transform. However, representations capable of estimating an image signal's 2-D phase have been largely ignored. Here, we consider 2-D phase representations using a method based upon the Riesz transform. For spatial images there exist two Riesz transformed signals and one original signal from which orientation, phase and energy may be represented as a vector in 3-D signal space. We show that these image properties may be represented by a Singular Value Decomposition (SVD) of the higher-order derivatives of the original and the Riesz transformed signals. We further show that the expected responses of even and odd symmetric filters from the Riesz transform may be represented by a single signal autocorrelation function, which is beneficial in simplifying Bayesian computations for spatial orientation. Importantly, the Riesz transform allows one to weight linearly across orientation using both symmetric and asymmetric filters to account for some perceptual phase distortions observed in image signals - notably one's perception of edge structure within plaid patterns whose component gratings are either equal or unequal in contrast. Finally, exploiting the benefits that arise from the Riesz definition of local energy as a scalar quantity, we demonstrate the utility of Riesz signal representations in estimating the spatial orientation of second-order image signals. We conclude that the Riesz transform may be employed as a general tool for 2-D visual pattern recognition by its virtue of representing phase, orientation and energy as orthogonal signal quantities.

A multivariate spatial analysis of vowel formants in American English

This paper presents the results of a multivariate spatial analysis of 38 vowel formant variables in the language of 402 informants from 236 cities from across the contiguous United States, based on the acoustic data from the Atlas of North American English (Labov, Ash & Boberg, 2006). The results of the analysis both confirm and challenge the results of the Atlas. Most notably, while the analysis identifies similar patterns as the Atlas in the West and the Southeast, the analysis finds that the Midwest and the Northeast are distinct dialect regions that are considerably stronger than the traditional Midland and Northern dialect region indentified in the Atlas. The analysis also finds evidence that a western vowel shift is actively shaping the language of the Western United States.

The two forms of visuo-spatial perspective taking are differently embodied and subserve different spatial prepositions

We set out to distinguish level 1 (VPT-1) and level 2 (VPT-2) perspective taking with respect to the embodied nature of the underlying processes as well as to investigate their dependence or independence of response modality (motor vs. verbal). While VPT-1 reflects understanding of what lies within someone else’s line of sight, VPT-2 involves mentally adopting someone else’s spatial point of view. Perspective taking is a high-level conscious and deliberate mental transformation that is crucially placed at the convergence of perception, mental imagery, communication, and even theory of mind in the case of VPT-2. The differences between VPT-1 and VPT-2 mark a qualitative boundary between humans and apes, with the latter being capable of VPT-1 but not of VPT-2. However, our recent data showed that VPT-2 is best conceptualized as the deliberate simulation or emulation of a movement, thus underpinning its embodied origins. In the work presented here we compared VPT-2 to VPT-1 and found that VPT-1 is not at all, or very differently embodied. In a second experiment we replicated the qualitatively different patterns for VPT-1 and VPT-2 with verbal responses that employed spatial prepositions. We conclude that VPT-1 is the cognitive process that subserves verbal localizations using “in front” and “behind,” while VPT-2 subserves “left” and “right” from a perspective other than the egocentric. We further conclude that both processes are grounded and situated, but only VPT-2 is embodied in the form of a deliberate movement simulation that increases in mental effort with distance and incongruent proprioception. The differences in cognitive effort predict differences in the use of the associated prepositions. Our findings, therefore, shed light on the situated, grounded and embodied basis of spatial localizations and on the psychology of their use.

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Perception of global image contrast is predicted by the same spatial integration model of gain control as detection and discrimination

How are the image statistics of global image contrast computed? We answered this by using a contrast-matching task for checkerboard configurations of ‘battenberg’ micro-patterns where the contrasts and spatial spreads of interdigitated pairs of micro-patterns were adjusted independently. Test stimuli were 20 × 20 arrays with various sized cluster widths, matched to standard patterns of uniform contrast. When one of the test patterns contained a pattern with much higher contrast than the other, that determined global pattern contrast, as in a max() operation. Crucially, however, the full matching functions had a curious intermediate region where low contrast additions for one pattern to intermediate contrasts of the other caused a paradoxical reduction in perceived global contrast. None of the following models predicted this: RMS, energy, linear sum, max, Legge and Foley. However, a gain control model incorporating wide-field integration and suppression of nonlinear contrast responses predicted the results with no free parameters. This model was derived from experiments on summation of contrast at threshold, and masking and summation effects in dipper functions. Those experiments were also inconsistent with the failed models above. Thus, we conclude that our contrast gain control model (Meese & Summers, 2007) describes a fundamental operation in human contrast vision.

Grid-texture mechanisms in human vision:contrast detection of regular sparse micro-patterns requires specialist templates

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.