Meaningful Spatial Patterns Research Articles

ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities.

Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities.

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Anxiety affects approximately 5–10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4–7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8–12 Hz) or beta (13–30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Considerations for Categorizing and Visualizing Numerical Information: A Case Study of Fire Occurrence Prediction Models in the Province of Ontario, Canada

Wildland fire management decision-makers need to quickly understand large amounts of quantitative information under stressful conditions. Categorization and visualization “schemes” have long been used to help, but how they are done affects the speed and accuracy of interpretation. Using traditional fire management schemes can unduly restrict the design of new products. Our design process for Ontario’s fine-scale, spatially explicit, daily fire occurrence prediction (FOP) models led us to develop guidance for designing new schemes. We show selected historical fire management schemes and describe our method. It includes specifying goals and requirements, exploring design options and making trade-offs. The design options include gradient continuity, hue selection, range completeness and scale linearity. We apply our method to a case study on designing the scheme for Ontario’s FOP models. We arrived at a smooth, nonlinear scale that accommodates data spanning many orders of magnitude. The colouring draws attention according to levels of concern, reveals meaningful spatial patterns and accommodates some colour vision deficiencies. Our method seems simple now but reconciles complex considerations and is useful for mapping many other datasets. Our method improved the clarity and ease of interpretation of several information products used by fire management decision-makers.

A modelling framework for pedogenon mapping

Soil entities are generally defined based on soil properties, using morphological, genetic, or utilitarian criteria. Alternatively, soil entities could be characterized by groupings of homogeneous soil-forming factors under the assumption that the dominant soil-forming processes occurring over a time period within each group are similar, and therefore develop unique soil entities with similar soil properties. We define the pedogenon as a conceptual soil taxon defined from a set of quantitative state variables that represent the soil-forming factors for a given reference time. The objective of this study was to develop a methodology for mapping pedogenon classes at the time of the European settlement in New South Wales (Australia). This period was chosen as reference because from 1788 onwards the intensification of land use has accelerated the rate of change of soil properties. We implemented a two-step modelling approach with a set of environmental covariates representing the soil-forming factors, including the estimated natural vegetation at 1750. The k-means algorithm was applied to generate pedogenon classes suitable for local management. Then, hierarchical clustering was applied to identify the organization of pedogenons into families or “branches” of higher level taxa. We tested the ability of the pedogenon classes for explaining the variance of stable soil properties (particle size fractions) in the subsoil (30–60 cm depth) with redundancy analysis (RDA). The results indicated that between 800 and 1000 pedogenon classes provide the desired level of detail for both local and regional management across New South Wales. The influence of the pre-1750 vegetation types (e.g. Acacia open woodlands and shrublands, Callitris forests and woodlands) was apparent in the distribution of some pedogenon branches. Pedogenon classes differed in their characteristics (median area ≈750 km2), but overall showed meaningful spatial patterns at local scale and formed regional assemblages. The RDA models indicated that pedogenon classes explained about 30% of the variance of silt and clay content. This flexible modelling framework allows the creation of pedogenon maps over large areas at high resolution (90 m) and is applicable at different scales. Potential applications of pedogenon maps include the quantitative assessment of soil change and designing soil monitoring surveys.

Geographic monitoring for early disease detection (GeoMEDD)

Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in vulnerable settings where even small numbers and low rates are important to pinpoint for early intervention. This paper identifies a gap in current analytical approaches and presents a near-real time assessment of emergent disease that can be used to guide a local intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data to provide multiple spatial and temporal perspectives on an ever changing disease landscape by connecting cases using different spatial and temporal thresholds. GeoMEDD has proven effective in revealing these different types of clusters, as well as the influencers and accelerators that give insight as to why a cluster exists where it does, and why it evolves, leading to the saving of lives through more timely and geographically targeted intervention.

An information-theoretic approach to study spatial dependencies in small datasets.

From epidemiology to economics, there is a fundamental need of statistically principled approaches to unveil spatial patterns and identify their underpinning mechanisms. Grounded in network and information theory, we establish a non-parametric scheme to study spatial associations from limited measurements of a spatial process. Through the lens of network theory, we relate spatial patterning in the dataset to the topology of a network on which the process unfolds. From the available observations of the spatial process and a candidate network topology, we compute a mutual information statistic that measures the extent to which the measurement at a node is explained by observations at neighbouring nodes. For a class of networks and linear autoregressive processes, we establish closed-form expressions for the mutual information statistic in terms of network topological features. We demonstrate the feasibility of the approach on synthetic datasets comprising 25-100 measurements, generated by linear or nonlinear autoregressive processes. Upon validation on synthetic processes, we examine datasets of human migration under climate change in Bangladesh and motor vehicle deaths in the United States of America. For both these real datasets, our approach is successful in identifying meaningful spatial patterns, begetting statistically-principled insight into the mechanisms of important socioeconomic problems.

Extracting spatial patterns of urban gas pipeline risk considering social and structural parameters of urban blocks

Due to growing cities and urbanization, the demand for natural gas is increasing rapidly around the world. To address this necessity, the density of urban natural gas pipelines will increase. However, urban pipelines pass through regions with the high density of people and buildings with low construction quality, possibly leading to accidents that may cause extreme damage to people and property. In this paper, a quantitative method is proposed for evaluation of hazards that are influenced by the inherent parameters of gas pipelines considering their density. Afterwards, considering the physical and social vulnerabilities of this study area, the structural and social parameters of urban blocks are added to the risk model. The output of this method is used to analyze the risk of spatial patterns of urban natural gas pipelines and the structural and social parameters of urban blocks. This research model consists of the following three main sections: hazard mapping of urban gas pipelines considering their inherent parameters, physical and social vulnerability mapping of urban blocks, and risk mapping of gas pipelines. The results show that there are meaningful spatial patterns in risk distributions that can be analyzed by the vulnerability results. The output of the proposed method has many applications in the fields of disaster management, urban planning and incident prediction.

Tapered composite likelihood for spatial max-stable models

Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a “taper range” to exclude distant pairs or triples. The “optimal taper range” is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

Spatial pattern recognition of seismic events in South West Colombia

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Increased Sensitivity to Mirror Symmetry in Autism

Can autistic people see the forest for the trees? Ongoing uncertainty about the integrity and role of global processing in autism gives special importance to the question of how autistic individuals group local stimulus attributes into meaningful spatial patterns. We investigated visual grouping in autism by measuring sensitivity to mirror symmetry, a highly-salient perceptual image attribute preceding object recognition. Autistic and non-autistic individuals were asked to detect mirror symmetry oriented along vertical, oblique, and horizontal axes. Both groups performed best when the axis was vertical, but across all randomly-presented axis orientations, autistics were significantly more sensitive to symmetry than non-autistics. We suggest that under some circumstances, autistic individuals can take advantage of parallel access to local and global information. In other words, autistics may sometimes see the forest and the trees, and may therefore extract from noisy environments genuine regularities which elude non-autistic observers.

Environmental Regulation and the Spatial Structure of the U.S. Dairy Sector

Substantial public scrutiny about adverse environmental impacts of the dairy sector has resulted in increased environmental regulations. A behavioral model of location and production is developed to examine the impacts of environmental regulations, traditional location factors, and agglomeration economies on the spatial structure and geographical location of dairy production. The results show that counties in the states with more stringent environmental regulations tend to lose dairy inventories to those with less stringent policies. There are substantially meaningful spatial patterns of dairy production. Current dairy production levels are positively correlated while changes in production levels are negatively correlated across counties.

Types of rapidly growing counties of the U.S., 1970–1990

Recent economic growth and in-migration have led to an interesting patchwork of rapidly growing counties in the United States. This paper identifies 299 U.S. counties that were growing at least 70% from 1970 to 1990, a rate greater than one standard deviation above the mean of all counties. Using a k-means cluster algorithm, seven distinct types of rapidly growing counties, with meaningful spatial patterns, were found. Four of these clusters were primarily urban: smaller fast-growing cities in the south, large sunbelt cities, as well as both white and blue-collar suburban counties ringing many of the more dynamic metropolitan regional centers of the country. Also, there were two predominately rural clusters: retirement counties and several counties in the Intermontane West characterized by environmental amenities and isolated growth. Lastly, we observed a distinct cluster of several Hispanic counties in the southwest primarily along the U.S.–Mexico border. We believe these seven clusters present a remarkably clear portrait of the significant forces underlying the rapid growth of counties in the United States.