Spatial Interaction Patterns Research Articles

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Traumatic Brain Injury and Post-Traumatic Stress Disorder and Their Influence on Development and Pattern of Alzheimer's Disease Pathology in Later Life.

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are potential risk factors for the development of dementia including Alzheimer's disease (AD) in later life. The findings of studies investigating this question are inconsistent though. To investigate if these inconsistencies are caused by the existence of subgroups with different vulnerability for AD pathology and if these subgroups are characterized by atypical tau load/atrophy pattern. The MRI and PET data of 89 subjects with or without previous TBI and/or PTSD from the DoD ADNI database were used to calculate an age-corrected gray matter tau mismatch metric (ageN-T mismatch-score and matrix) for each subject. This metric provides a measure to what degree regional tau accumulation drives regional gray matter atrophy (matrix) and can be used to calculate a summary score (score) reflecting the severity of AD pathology in an individual. The ageN-T mismatch summary score was positively correlated with whole brain beta-amyloid load and general cognitive function but not with PTSD or TBI severity. Hierarchical cluster analysis identified five different spatial patterns of tau-gray matter interactions. These clusters reflected the different stages of the typical AD tau progression pattern. None was exclusively associated with PTSD and/or TBI. These findings suggest that a) although subsets of patients with PTSD and/or TBI develop AD-pathology, a history of TBI or PTSD alone or both is not associated with a significantly higher risk to develop AD pathology in later life. b) remote TBI or PTSD do not modify the typical AD pathology distribution pattern.

Revealing the Spatial Interactions and Driving Factors of Ecosystem Services: Enlightenments under Vegetation Restoration

Large-scale vegetation restoration has caused complex changes in ecosystem service (i.e., ES) interactions. However, current analysis on the spatial interactions of ESs and their driving mechanisms remains deficient, limiting the adaptive management in vegetation restoration areas. This study focused on a representative restoration area (Yan’an) to analyze the relationships among carbon sequestration, water yield, baseflow regulation, and soil conservation from 1990 to 2020. Employing the bivariate boxplot and spatial autocorrelation methods, we identified the overall changes and spatial patterns of ES interactions. The geographically and temporally weighted regression (i.e., GTWR) model was applied to elucidate the driving factors of these spatial ES interactions. The results indicated the following: (1) Over the past three decades, synergies between carbon sequestration and water yield emerged as the joint results of spatial ‘low–low’ interactions and ‘high–high’ interactions between the two ESs, while other ES pairs generally exhibited comparatively weaker synergies, due to their spatial ‘low–high’ interactions in southern semi-humid areas. (2) In the northern semi-arid areas, both fractional vegetation cover (i.e., FVC) and climatic factors consistently exerted negative influences on all ‘low–low’ ES interactions, which caused a reduced area in synergies, while in the southern semi-humid areas, FVC suppressed the ‘low–high’ trade-offs between ESs, indicating the adaptability of grassland restoration efforts. (3) The impact of human activities on ES interactions has increased in the last 10 years, and exhibited positive effects on the ‘low–low’ ES interactions in northern semi-arid areas. However, the expansion of trade-off between soil conservation and carbon sequestration warrants attention. This study offers important insights into understanding the spatial interactions among carbon, water, and soil-related ESs in drylands.

Abstract 3644: Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma

Abstract Background: Tumor progression and therapeutic response are regulated by the tumor microenvironment. Understanding the spatial association and architecture of molecular characteristics and composition of tumor immune microenvironment at single-cell and subcellular resolution encourages improvements in clinical prognosis and immunotherapy benefits. Single-cell Spatial profiling technologies permit the study of transcriptional activity at the spatial, single-cell level and provide abundant, high-resolution information required for the identification of clinical-related features in immuno-oncology. Methods: We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on FFPE human skin squamous cell carcinoma using the CosMx™ Spatial Molecular Imager (SMI). For the selection of regions of interest, four protein markers and DAPI were co-detected on the same tissue slide. Tertiary analysis algorithms were developed for cell typing, co-localization of genes and ligands, cell-cell interaction, and pathway analysis. Results: Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE skin squamous cell carcinoma tissue section at single-cell subcellular resolution. We investigated the tumor microenvironment including cell types, their spatial distribution and proportion of diversified immune cells in the tumor compartment. The cell typing results revealed many cancerous subpopulations, which resolved spatially from cells that lacked any defining marker genes. We also assessed distances between immune cells and their nearest functional-related neighbors. Moreover, we revealed the ligands co-localization, as well as spatial patterns of direct cell-cell interactions and signaling pathways. We found that some genes have elevated expression in macrophages which are near cancer compared to those are far from cancer cells, empowering the molecular investigation of novel signaling between immune and cancer cells in tumor immune microenvironment. Conclusions: Single-cell spatial measurements of 6,000 RNA and four proteins on the same tissue section, along with a large viewing area on archival FFPE tissue, provide a novel tool to reveal the spatial signature of tumor microenvironment and oncogenic pathways, facilitating the next level of cancer and therapeutic research. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Michael Patrick, Shanshan He, Saskia Ilcisin, David Kroeppler, Patrick Danaher, Jason Reeves, Mark Gregory, Haiyan Zhai, Michael Rhodes, Joseph Beechem. Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3644.

Abstract 2341: Morph: A feature extraction toolset for spatial transcriptomics

Abstract Accurately defining spatial characteristics of tumors has been a challenge in cancer research. Specifically, there is still a lack of spatial transcriptomic (ST) bioinformatic methods that infer tumor boundaries, a necessity for tumor microenvironment (TME) analyses, that are fully automated and handle non-rectangular grids (like the one found in Visium). Here we introduce Morph, a toolset that not only addresses these limitations, but also accurately extracts tumor regions, layers surrounding them, and distances related to such regions. Morph was tested on a dataset composed of 117 ST slides across 6 different cancer types, including primary and metastatic tumors. More broadly, Morph is a computationally efficient tool based on mathematical morphology that is designed to work without any approximations on ST slides (that can be composed of spots in a hexagonal lattice) of any resolution. The toolset accepts a variety of input types, such as tumor purity and manual annotation, and can perform a diverse set of morphological operations, such as erosion, dilation, opening and closing, using a number of structuring elements with different shapes and sizes, such as a hexagon of side 1. Moreover, Morph runs quickly (seconds per sample). Its main focus is to unveil the aforementioned features of any spatially distinct tumor region (here defined as microregion) for various downstream analyses, such as spatially-varying copy number variations, cell-type distribution, and tumor-microenvironment interaction. Overall, we developed a spatial transcriptomics toolset that is not limited to any specific technology platform, and that can accurately extract ST features facilitating the discovery of spatial transcriptomic and interaction patterns. Citation Format: André Luiz N. Targino da Costa, Jingxian Liu, Chia-Kuei Mo, Erik Storrs, Austin N. Southard-Smith, Reyka G. Jayasinghe, Julia T. Wang, Michael D. Iglesia, Michael Wendl, Siqi Chen, Andrew Houston, Alla Karpova, Yize Li, Li Ding. Morph: A feature extraction toolset for spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2341.

Spatio-Temporal Relationship between Land Use Carbon Emissions and Ecosystem Service Value in Guanzhong, China

In the context of escalating global concerns for “carbon neutrality and peak carbon” and the urgent need for ecological conservation, deciphering the spatiotemporal interactions between carbon emissions and the ecosystem service value (ESV) in relation to land use changes becomes critically significant. Identifying areas to bolster ecosystem services and curtail carbon emissions, especially within the Guanzhong urban agglomeration, is crucial for advancing sustainable and low-carbon regional development. The study focuses on the urban agglomeration of Guanzhong, using land use and socio-economic data from three periods between 2010 and 2020. Methods such as grid analysis and bivariate spatial autocorrelation models are employed to explore the temporal and spatial evolution characteristics and interaction patterns of carbon emissions and ESV in relation to land use. The findings reveal: (1) during 2010–2020, the Guanzhong urban agglomeration experienced varied transitions in land use types, marked by a significant net decrease in arable land and net increases in grasslands and urban construction areas. (2) The ESV in the Guanzhong urban agglomeration witnessed a consistent rise, exhibiting a spatial distribution pattern with higher values in the southwest and lower in the northeast. Among the categorized ecosystem service functions, services related to hydrological and climate regulation stood out. (3) The Guanzhong urban agglomeration observed an average annual growth rate of 5.03% in carbon emissions due to land use, with a spatial trend that was higher in the center and tapered towards the periphery. Predominant carbon sources included arable lands and urban construction areas, while forests accounted for 94% of carbon sequestration. (4) A pronounced negative correlation between the ESV and carbon emissions was discerned in Guanzhong. Regions with a stronger correlation were primarily centered in Guanzhong, notably around Xi’an and Baoji. The results emphasize the pivotal role of the primary sector’s qualitative development in harmonizing the ESV and carbon emission dynamics in the Guanzhong urban agglomeration. This research provides valuable insights for optimizing land resource management, aligned with the rural revitalization strategy, streamlining carbon dynamics, bolstering ESV, augmenting carbon sequestration efficiency, and guiding ecological spatial planning.

A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics

ABSTRACT Understanding the participation characteristics of VGI (volunteered geographic information) contributors is important as their active participation and consistent data contribution are key to success of any VGI project. Existing studies on this matter focused primarily on deriving and interpreting participation patterns of contributor groups. There is a lack of investigation into the participation pattern of individual contributors, which can be complementary to a comprehensive understanding of VGI contributors’ participation characteristics. Building and using a custom web-based geovisualization framework, this study explores the individual-level participation characteristics of VGI contributors from the perspectives of spatial, temporal, thematic, and social interaction patterns. I conducted geovisual exploratory data analysis on VGI datasets from the iNaturalist biodiversity citizen science project to gain intuitions on the clustering and variabilities of participation patterns in iNaturalist, detect participation pattern shifts over time and form explanation hypotheses, and assess and develop metrics to measure participation. The geovisualization framework is expected to be generally applicable to other VGI datasets for exploring individual-level contributor participation patterns. This work is among the first efforts to explore individual-level VGI participation characteristics through geovisualization and geovisual analytics.

Structure and dynamics of urban freight truck movements: A complex network theory perspective

Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in-depth studies on the system core structure and underlying dynamics are still absent. This study contributes to the field by analyzing large scale freight truck trip data from Chinese cities, using complex network analysis. We empirically reconstruct and characterize the urban freight truck mobility networks and reveal the underlying spatial interaction patterns. We develop a spatial network growth model which explains how hub-and-spoke core structure of urban freight transport systems are formed. The developed model captures the essential interaction dynamics of freight locations, and explains the effects of spatial distance, economic size and business pattern replication. Inspired by the model, we provide policy implications for land-use planning, transportation planning and sustainable urban development.

Intraepithelial CD8+ cytotoxic T-cells are the main component of the immune tumor microenvironment that drive prognosis in muscle-invasive bladder cancer

Abstract Introduction/Objective Quantity and the spatial relationship of individual immune cell (sub)types can provide prognostic information in muscle-invasive bladder cancer. Methods/Case Report To study the prognostic role of different immune cell subpopulations in muscle-invasive bladder cancer, we stained 521 muscle-invasive urothelial bladder carcinomas with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry (mfIHC) in a tissue microarray format. A framework of neuronal networks was trained and used for image analysis. Results (if a Case Study enter NA) The identification of more than 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. 40 immune parameters showed prognostic significance in univariate analyses of which 15 were independent from pT, pN, and histologic grade. The strongest association to clinical outcome was identified for intraepithelial CD8+cytotoxic T-cells (tAUC: 0.70) compared to all other 15 independent prognosis parameters (AUC: ≤0.68, p=0.039). Deeper spatial analysis revealed an increased expression levels of TIM3, CTLA-4 and PD-1 on CD8+ T cells that were located in the intraepithelial compartment. Furthermore, the cell-to-cell interaction profile of intraepithelial CD8+ T-cells was dominated by intraepithelial dendritic cells as well as intraepithelial M1-macrophages. Conclusion The intraepithelial CD8+T-cells that showed the highest expression level of TIM-3, PD-1 and CTLA-4 and were strongly interacting with M1-macrophages as well as dendritic cells and represented the strongest prognostic parameter in muscle-invasive bladder cancer. This can be explained by the fact that tumor cell destruction by CD8+ cytotoxic T-lymphocytes through direct cell-to-cell-contacts represents the “terminal-end-route” of anti-tumor immunity. The strongest prognostic immune-factor – intraepithelial CD8+T-cells – can be measured straightaway in routine pathology as the CD8-labelling index using conventional bright field immunohistochemistry.

Spatiotemporal heterogeneity of ecosystem service interactions and their drivers at different spatial scales in the Yellow River Basin

Accurately understanding ecosystem service (ES) interactions and an analysis of the complex, multiscale driving mechanisms are foundational prerequisites for implementing effective multiscale ES management. This study dives into the spatial and temporal variations of ES interactions in the Yellow River Basin across four spatial scales. The eXtreme Gradient Boosting (XGBoost) model is later deployed to pinpoint the key drivers of ecosystem services and their indirect pathways to ESs are illuminated utilizing Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results indicate that (1) The synergistic effect between ES pairs in the Yellow River Basin surpasses that of trade-offs. Various types of ecosystem service bundles have transformed into each other from 2000 to 2020, and the spatial patterns of ES interactions bear resemblances at different scales. (2) The factors driving habitat quality (HQ), carbon sequestration (CS), and landscape aesthetics (LA) are mainly the landscape configuration and biophysical conditions. The factor driving food production (FP) is mainly the level of urbanization, whereas soil conservation (SC) and water yield (WY) are mainly subject to climate. (3) When biophysical conditions and level of urbanization serve as mediating variables in pathways, driving factors invariably have negative indirect effects on ESs. When landscape configuration serves as a mediating variable, biophysical conditions positively influence HQ and CS, and negatively impact FP, WY, and LA. Conversely, the level of urbanization negatively affects all ESs. (4) The combination of XGBoost and PLS-SEM offers a comprehensive and innovative lens for analyzing ESs driving mechanisms. Based on our findings, scientific management of ESs should account not only for the direct impacts of driving elements but also for their scale and indirect effects.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Effects of microplastics on denitrification and associated N2O emission in estuarine and coastal sediments: insights from interactions between sulfate reducers and denitrifiers

Global estuarine and coastal zones are facing severe microplastics (MPs) pollution. Sulfate reducers (SRB) and denitrifiers (DNB) are two key functional microorganisms in these zones, exhibiting intricate interactions. However, whether and how MPs modulate the interactions between SRB and DNB, with implications for denitrification and associated N2O emissions, remains poorly understood. Here, we simultaneously investigated the spatial response patterns of SRB-DNB interactions and denitrification and associated N2O emissions to different MPs exposure along an estuarine gradient in the Yangtze Estuary. Spatial responses of denitrification to polyvinyl chloride (PVC) and polyadipate/butylene terephthalate (PBAT) MPs exposure were heterogeneous, while those of N2O emissions were not. Gradient-boosted regression tree and multiple regression model analyses showed that sulfide, followed by nitrate (NO3−), controlled the response patterns of denitrification to MPs exposure. Further mechanistic investigation revealed that exposure to MPs resulted in a competitive and toxic (sulfide accumulation) inhibition of SRB on DNB, ultimately inhibiting denitrification at upstream zones with high sulfide but low NO3− levels. Conversely, MPs exposure induced a competitive inhibition of DNB on SRB, generally promoting denitrification at downstream zones with low sulfide but high NO3− levels. These findings advance the current understanding of the impacts of MPs on nitrogen cycle in estuarine and coastal zones, and provide a novel insight for future studies exploring the response of biogeochemical cycles to MPs in various ecosystems.

DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network

AbstractAccurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi‐horizon prediction. With a multi‐term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic‐temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real‐world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic.

Presence of CD8+ cytotoxic T-cells in proximity to tumor cells, TIM3, CTLA-4 and prognostic implications for muscle-invasive urothelial cancer.

4582 Background: Although it is well known that the quantity of specific immune cell types can provide prognostic information in bladder cancer, only little is known about the prognostic relevance of the localization of immune cells within the tumor microenvironment. Methods: To study the spatial interplay of different immune cell subpopulations and its prognostic role in bladder cancer, 2463 urothelial bladder carcinomas were immunostained with 21 antibodies (i.e., TIM-3, PD-L1, PD-1, CTLA4, CD3, CD8, CD4, FOXP3, CD20, CD68, CD163, CD11c, panCK, Ki67, CD31, Vimentin, HLA-DRa, Myosin-11, Desmoglein 3, PAX-8, and CDH16) using BLEACH&STAIN multiplex fluorescence immunohistochemistry in a tissue microarray format and analyzed using a framework of neuronal networks for image analysis. Thus, Spatial immune parameters were compared with histopathological parameters and overall survival data. Results: The identification of more than 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. 32 immune parameters showed prognostic significance in univariate analyses of which 16 were independent from pT, pN, and histologic grade in muscle-invasive bladder cancer. The strongest association to clinical outcome was identified for intraepithelial CD8+ cytotoxic T-cells (AUC: 0.70) while stromal CD8+ cells were less relevant (AUC: 0.64, p=0.016). A favorable prognosis of inflamed cancers with high levels of “exhaustion markers” suggests that TIM3, PD-L1, PD-1, and CTLA-4 on inflammatory cells do not hinder anti-tumoral immune response in tumors rich of tumor infiltrating tumor cells. Conclusions: The density of intraepithelial CD8+ T-cells was the strongest prognostic feature in muscle-invasive bladder cancer. Given that tumor cell killing by CD8+ cytotoxic T-lymphocytes through direct cell-to-cell-contacts represents the “terminal end route” of anti-tumor immunity, the quantity of “tumor-cell adjacent CD8+ T-cells” may constitute a surrogate for the efficiency of cancer recognition by the immune system that can be measured straightaway using conventional brightfield IHC in routine pathology as the CD8 labelling index.

Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization.

Verification of Geographic Laws Hidden in Textual Space and Analysis of Spatial Interaction Patterns of Information Flow

The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler’s first law of geography and Goodchild’s second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow.

Spatiotemporal interaction pattern of the Beijing agricultural product circulation

Identifying the spatiotemporal interaction pattern of agricultural product circulation (APC) is crucial for agricultural resource adjustment and food security. Current studies are mostly based on static statistical data over an entire year or a specific period, which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale. Thus, this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit. The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form, city node function role, and transportation corridors. The results are as follows: (1) Compared with methods based on static statistical data, the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC. (2) The flow network of the Beijing APC involves 316 cities in China, of which 143 cities play a major role with typical seasonal characteristics. These cities can be divided into perennial core cities, perennial major cities, core cities in winter-spring, major cities in winter-spring, core cities in summer-autumn, and major cities in summer-autumn, contributing 2.6%–40.3% to the Beijing APC. (3) There are 6 transportation corridors for the Beijing APC. The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5% and 12.8% of the annual supply, respectively, with a balanced supply in all seasons. The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3% and 9.0%, respectively, with much higher supplies in winter and spring. The northeast and northwest corridors contribute 7.3% and 3.3%, respectively, mainly in the summer and autumn. These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.

Abstract 3138: Topological analysis of spatial transcriptomics reveals different spatial interaction patterns in tumor microenvironment of lung cancer

Abstract Background: As the tumor microenvironment (TME) consists of various cell types with complex spatial interaction, its spatial organization patterns affect response to immune-oncology treatment. Therefore, describing the spatial composition and interaction of cells in the tumor microenvironment (TME) is necessary. Here, we developed a tool, STopover, which adopts topological analysis in spatial transcriptomics to reveal cell-cell colocalization patterns in TME and capture the key components and niche of intercellular communication, and applied it to human lung cancer data. Methods: The spatial colocalization pattern of pairs of features was defined using topological data analysis with Morse filtration. The spatial network of spatial gene expression data was generated for each sample and then the patterns were summarized as connected components (CCs) based on the spatial distance between unit tissue regions and the persistence of each CC. The global and local extent of spatial overlap of a feature pair was calculated as Jaccard indices between extracted CC pairs. We applied STopover to 11 barcode-based spatial transcriptomic data of human lung adenocarcinoma with different PD-L1 expressions. Spatial mapping of cell types in TME was performed by CellDART. In addition, image-based spatial transcriptomic data of lung cancer were also used to find key spatial molecular interactions in TME using STopover. Results: First, STopover disclosed the distinct immune and stromal infiltration patterns in lung cancer tissues. Spatial colocalization of cancer cells and T-cells was heterogeneous and correlated with the immune-related markers such as MHC class I signature. The cancer types were clustered according to spatial colocalization patterns of various immune cells with cancer cells, which showed different infiltration patterns of immune cells. Moreover, STopover could estimate the top cell-cell interaction and emphasize the key locations based on the literature-supported ligand-receptor database. Conclusion: STopover is expected to account for significant spatial cell interactions in tumor-immune and tumor-stromal components and could be utilized as a platform to decipher the mechanisms underlying immune-oncology treatment response. Citation Format: Sungwoo Bae, Hyekyoung Lee, Kwon Joong Na, Dong Soo Lee, Hongyoon Choi, Young Tae Kim. Topological analysis of spatial transcriptomics reveals different spatial interaction patterns in tumor microenvironment of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3138.

Ranking Spatial Units with Structural Property and Traffic Distributions for Uncovering Spatial Interaction Patterns in a City

Travel activity data mining is critical to numerous urban applications such as transportation and location‐based services. This article studies the spatial units ranking algorithm for uncovering spatial interaction patterns based on the flow properties of people's travel trajectories. For example, using a taxi origin–destination flow database, a user may want to rank the origin and destination with respect to their functional importance within the urban activity space. In the literature, such an importance concept is usually specified via the frequency function of trip flows. Considering the case that the less frequently visited place in reality may still be an important origin or an important destination, we propose a different method for the ranking of spatial units by introducing the structural property of trip network. The proposed method is inspired from the mutual reinforcing relationship between the trip origins and destinations: important destinations attract travel flows from important origins and at the same time important origins have many flows toward important destinations. Our experimental results show that the proposed method is effective in uncovering spatial interaction patterns of urban activities.

Deriving spatially explicit direct and indirect interaction networks from animal movement data.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.