mailitics

Tag: spatial

  • Which class should I take to help me get a job?

    Which class should I take to help me get a job? I’m in my final semester of my MS program and am deciding between Spatial and Non-Parametric statistics. I feel like spatial is less common but would make me stand out more for jobs specifically looking for spatial whereas NP would be more common but…

  • STARK denoises spatial transcriptomics images via adaptive regularization

    STARK denoises spatial transcriptomics images via adaptive regularization arXiv:2512.10994v1 Announce Type: new Abstract: We present an approach to denoising spatial transcriptomics images that is particularly effective for uncovering cell identities in the regime of ultra-low sequencing depths, and also allows for interpolation of gene expression. The method — Spatial Transcriptomics via Adaptive Regularization and Kernels…

  • Surrogate Graph Partitioning for Spatial Prediction

    Surrogate Graph Partitioning for Spatial Prediction arXiv:2510.07832v1 Announce Type: new Abstract: Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remains limited in industries that demand interpretability. To mitigate this gap, surrogate models that explain black-box…

  • A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data

    A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data arXiv:2509.20636v1 Announce Type: new Abstract: The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single…

  • The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated

    The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big…

  • Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks

    Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks arXiv:2505.03034v1 Announce Type: new Abstract: Data derived from remote sensing or numerical simulations often have a regular gridded structure and are large in volume, making it challenging to find accurate spatial models that can fill in missing grid cells or simulate the process…

  • DeepRV: pre-trained spatial priors for accelerated disease mapping

    DeepRV: pre-trained spatial priors for accelerated disease mapping arXiv:2503.21473v1 Announce Type: new Abstract: Recently introduced prior-encoding deep generative models (e.g., PriorVAE, $pi$VAE, and PriorCVAE) have emerged as powerful tools for scalable Bayesian inference by emulating complex stochastic processes like Gaussian processes (GPs). However, these methods remain largely a proof-of-concept and inaccessible to practitioners. We propose…

  • Variational Autoencoded Multivariate Spatial Fay-Herriot Models

    Variational Autoencoded Multivariate Spatial Fay-Herriot Models arXiv:2503.14710v1 Announce Type: new Abstract: Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay-Herriot model is one such approach that incorporates spatial dependence to improve estimation…

  • Unraveling Spatially Variable Genes: A Statistical Perspective on Spatial Transcriptomics

    Unraveling Spatially Variable Genes: A Statistical Perspective on Spatial Transcriptomics [ The article was written by Guanao Yan, Ph.D. student of Statistics and Data Science at UCLA. Guanao is the first author of the Nature Communications review article [1]. Spatially resolved transcriptomics (SRT) is revolutionizing Genomics by enabling the high-throughput measurement of gene expression while…

  • Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA

    Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA arXiv:2501.12582v1 Announce Type: new Abstract: Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information…

  • GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction

    GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction arXiv:2412.08661v1 Announce Type: new Abstract: Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally…