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Resilience, denoting the capacity to swiftly recover to a state of normalcy subsequent to the occurrence of a disaster, constitutes a multifaceted phenomenon necessitating in-depth investigation. This study undertakes the quantification of resilience pertaining to specific locales through the utilization of heterogeneous data encompassing visitation patterns, demographic particulars, and points of interest (POI). A heterogeneous graph neural network is applied to model the resilience of these locales in Galveston, TX, USA. Our model outperforms regression models and other homogeneous baseline methodologies. Subsequent analysis unveils discernible resilience patterns intertwined with metrics such as visitation frequencies, visitors’ travel behaviors, and geographical attributes. In comparison to resilience investigations solely predicated upon visitation counts, our approach captures a more extensive array of information, thereby yielding a comprehensive understanding of the locale’s resilience.

Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be “sliced” to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers’ workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur.

Techniques to study brain activities have evolved dramatically, yet tremendous challenges remain in acquiring high-throughput electrophysiological recordings minimally invasively. Here, we develop an integrated neuroelectronic array that is filamentary, high-density and flexible. Specifically, with a design of single-transistor multiplexing and current sensing, the total 256 neuroelectrodes achieve only a 2.3 × 0.3 mm²area, unprecedentedly on a flexible substrate. A single-transistor multiplexing acquisition circuit further reduces noise from the electrodes, decreases the footprint of each pixel, and potentially increases the device’s lifetime. The filamentary neuroelectronic array also integrates with a rollable contact pad design, allowing the device to be injected through a syringe, enabling potential minimally invasive array delivery. Successful acute auditory experiments in rats validate the ability of the array to record neural signals with high tone decoding accuracy. Together, these results establish soft, high-density neuroelectronic arrays as promising devices for neuroscience research and clinical applications.