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1. From Spatial Relations to Spatial Configurations

   Dan, Soham; Kordjamshidi, Parisa; Bonn, Julia; Bhatia, Archna; Cai, Jon; Palmer, Martha; Roth, Dan (April 2020, Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020))

   Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos. Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole. 

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2. Nonnegative spatial factorization applied to spatial genomics

   https://doi.org/10.1038/s41592-022-01687-w  

   Townes, F. William; Engelhardt, Barbara E. (December 2022, Nature Methods)

   Abstract Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available fromhttps://github.com/willtownes/nsf-paper. 

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3. Finding the middle: Spatial language and spatial reasoning

   https://doi.org/10.1016/j.cogdev.2019.04.002  

   Simms, Nina K.; Gentner, Dedre (April 2019, Cognitive Development)

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4. Spatial Orchestra: Locomotion Music Instruments through Spatial Exploration

   https://doi.org/10.1145/3613905.3648659  

   Kim, You-Jin; Lee, Myungin; Peljhan, Marko; Kuchera-Morin, JoAnn; Höllerer, Tobias (May 2024, ACM)
5. Spatial Inequality

   https://doi.org/10.18335/region.v12i1.563  

   Rey, Sergio (January 2025, REGION)

   This paper explores the concepts and computational methods used tomeasure spatial inequality, emphasizing a reproducible approach thatsocial scientists can apply to their research. The analysis focuses ongeographic income disparities at the sub-national level, using Mexico asa case study. By examining various a-spatial and spatially explicitapproaches, the paper highlights the complexities of measuringinequality across places and over time. The discussion includes a reviewof traditional inequality measures and introduces spatial decompositionmethods that account for the geographical distribution of income. Thefindings underscore the importance of integrating spatial considerationsinto inequality analysis to better understand the patterns and driversof regional disparities, thereby informing more effective and equitablepolicy interventions. 

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