Spatial Analysis Research Articles

Policy-Driven Vegetation Restoration in Qinghai Province: Spatiotemporal Analysis and Policy Evaluation

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies.

Environmental vulnerability applied to the territorial planning of a tropical semiarid basin.

Tropical semiarid regions are naturally prone to environmental damage. Human activity can worsen this situation. To understand how human actions affect the ecosystem, plan land use effectively, and establish targeted management practices, assessing environmental vulnerability is crucial. This study focuses on a sub-basin receiving water transfers from the São Francisco River in Brazil's semiarid region. Here, we map and evaluate how land use and occupation alter natural vulnerability. We also propose zoning strategies to support water resource management and implement sustainable development policies in the region. To achieve this, we conducted an integrated analysis of physical factors (soil types, geology, climate, vegetation, and landforms) and spatial land-use data using geographic information systems (GIS) and map algebra techniques. Map algebra allowed us to combine these various datasets within the GIS environment, enabling the creation of maps that synthesize both natural and environmental vulnerability across the study area. Following analysis of these vulnerability maps, our findings reveal a high level of vulnerability. The areas with high to very high degrees of natural vulnerability coincide with the places that have high slopes, high altitudes, Lithic Neosols, and thick vegetation. Furthermore, the interaction between environmental factors and human activity exacerbates vulnerability. Based on the environmental vulnerability assessment, we defined four environmental management zones. These zones require distinct protection measures and management approaches. As a method to potentially improve the basin's vulnerability scenario, soil conservation measures are recommended. This approach is highly relevant for managing land in tropical semiarid regions and, with adaptations to specific regional factors, can be applied globally.

Astrocytic Neuroligin-3 influences gene expression and social behavior, but is dispensable for synapse number.

Neuroligin-3 (Nlgn3) is an autism-associated cell-adhesion molecule that interacts with neurexins and is robustly expressed in both neurons and astrocytes. Neuronal Nlgn3 is an essential regulator of synaptic transmission but the function of astrocytic Nlgn3 is largely unknown. Given the high penetrance of Nlgn3 mutations in autism and the emerging role of astrocytes in neuropsychiatric disorders, we here asked whether astrocytic Nlgn3 might shape neural circuit properties in the cerebellum similar to neuronal Nlgn3. Imaging of tagged Nlgn3 protein produced by CRISPR/Cas9-mediated genome editing showed that Nlgn3 is enriched in the cell body but not the fine processes of cerebellar astrocytes (Bergmann glia). Astrocyte-specific knockout of Nlgn3 did not detectably alter the number of synapses, synaptic transmission, or astrocyte morphology in mouse cerebellum. However, spatial transcriptomic analyses revealed a significant shift in gene expression among multiple cerebellar cell types after the deletion of astrocytic Nlgn3. Hence, in contrast to neuronal Nlgn3, astrocytic Nlgn3 in the cerebellum is not involved in shaping synapses but may modulate gene expression in specific brain areas.

Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m ( q = 0.48).

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

A New Paradigm for Assessing Detailed Dynamics of Forest Landscape Fragmentation

There is an urgent need for a thorough assessment of forest landscape fragmentation to inform forest protection and restoration, and reforestation policies. However, there is currently a lack of an effective comprehensive index for forest landscape fragmentation, and detailed knowledge of the forest landscape fragmentation dynamics remains insufficient. Here, taking Putian City of Fujian Province in Southeastern China as a case, we employed a forest fragmentation comprehensive index (FFCI) to capture key features of forest landscape fragmentation, such as patch size, number, and distribution. Then, bivariate spatial autocorrelation analysis was employed to identify the spatial associations between the static forest landscape fragmentation (FFCI) and the dynamic forest landscape fragmentation (ΔFFCI), and the spatial coupling modes among the three individual components of FFCI (mean patch area, MPA; aggregation index, AI; patch density, PD) were identified to explore the detail process of forest landscape fragmentation. Finally, the random forest model was applied to observe the impact factors of forest landscape fragmentation dynamics. The findings showed that forest landscapes with different degrees of fragmentation exhibited more noticeable changes at both ends (i.e., either high or lower-level fragmentation), with the intermediate level remaining consistent from 2000 to 2020. Around 18.3% of forest landscapes experienced a decrease in fragmentation, particularly in the northern part of the study area, while approximately 81.7% of forest landscapes exhibited an increasing trend in fragmentation. The bivariate spatial autocorrelation analysis indicated that the proportion of Low–High-type grids was the highest at 17.3%, followed by the High–High type at 7.0%. We also identified eight forest landscape fragmentation modes, which indicate the most significant forest landscape fragmentation pattern is a decrease in MPA and an increase in PD. Moreover, the anthropogenic factors (e.g., population density and night light intensity) were found to dominate the FFCI dynamics during 2000–2020. This study offers an efficient research paradigm for the dynamics of forest landscape fragmentation. The outcomes are conducive to an in-depth comprehension of the detailed dynamic information of forest landscape fragmentation, and supply a scientific foundation for enhancing the overall ecological service function of the forest.

W47 - Using Spatial Social Network Analysis to Study the Journey to Overdose Deaths

W47 - Using Spatial Social Network Analysis to Study the Journey to Overdose Deaths

Disparities and similarities in the spatiotemporal dynamics of flash and slow droughts in China

Climate warming has induced significant transitions from slowly-developing droughts to rapidly-developing flash droughts in China, causing broad impacts on ecosystems, hydrological regimes, and society. To date, most studies focused on temporal evolution of flash droughts, while neglected the spatial expansion which is essential for understanding their origins and spatial propagations, especially for mega flash droughts. Based on the long-term (1940–2022) dataset of the 5th generation of the European ReAnalysis, here we use a three-dimensional drought identification method to analyze the disparities and similarities in the spatiotemporal dynamics of flash and slow droughts at the subseasonal time scale over China. Although half of the flash and slow droughts are characterized by small areas (<5000 km2), short durations (30–45 d) and short propagation distances of drought centroids (<50 km), the probability of large-scale (>30 000 km2) flash droughts with long propagation distances (>100 km) is twice of slow droughts. Moreover, global and local spatial autocorrelation analyses reveal that South China (SC) and North China are hotspots for large-scale flash and slow droughts, respectively, and they both show significant increasing trends (0.11–0.12 events/decade) during 1940–2022. Without these large-scale droughts, there is no obvious difference in spatial distributions of the frequency of flash and slow droughts. Despite disparities, both large-scale flash and slow droughts show a preferential westward propagation, with 60%–67% of the movements consistent with the pathways of atmospheric water vapor flux anomaly. Our study urges the understanding and prevention of large-scale flash drought events, especially in SC.

Characterization of Immune Infiltrate Along the Leading Edge of Differentiated Thyroid Cancer.

Background: Although the impact of tumor-immune infiltrate has been reported on differentiated thyroid cancer (DTC) behavior, the expression of immune checkpoints [programmed cell death protein 1 (PD-1) and its ligand (PD-L1)] alone has not been able to predict response to immunotherapies. We aimed to identify tumor-infiltrating immune cells and checkpoints associated with DTC. Methods: We performed multiplex immunofluorescence on deparaffinized thyroid tissue collected at thyroidectomy from 17 adults with DTC to characterize the tumor immune microenvironment for leukocytes (CD45+), T cells (CD3+), T regulatory cells (Tregs) (CD3+FOXP3+), CD4+ T cells (CD3+CD4+), CD8+ T cells (CD3+CD8+), macrophages (CD68+), M2 macrophages (CD68+CD163+), M1 Macrophages (CD68+ inducible nitric oxide synthase [iNOS]+), and immune checkpoints PD-1 and PD-L1. We compared the mean percentage expression of immune markers between tumor and adjacent thyroid tissue from the same patient by paired t-test and performed spatial analysis along the tumor's leading edge. Results: Immune checkpoints PD-1 and PD-L1 showed a significant increase in expression intratumorally as compared to adjacent thyroid tissue (p < 0.05). A higher trend for M2 macrophages was observed intratumorally compared to adjacent tissue. Along the leading edge, PD-L1 expression correlated negatively with CD45 and positively with CD163 intratumorally. On exploratory analysis, there was a nonsignificant trend for higher FOXP3 but less CD8 and iNOS expression in tumor from DTC with (n = 3) versus without distant metastases (n = 14). There was a nonsignificant trend for higher CD58 and iNOS expression in DTC with (n = 7) than without thyroiditis (n = 10). Conclusions: Higher tumoral PD-1 and PD-L1 expression indicate their role in DTC occurrence. A trend for more Tregs and M2 macrophages but less M1 macrophages intratumorally in patients with distant metastatic DTC, suggests their potential role as prognostic biomarkers. Future studies with larger sample sizes are needed to compare various clinicopathologic severities to harness tumor microenvironment for cancer prognostication and therapy.

Elaborating and performing optimization approaches for web-based 3D spatial analysis of 3D city models

ABSTRACT Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered.

Exploring the effective distance of 3D urban compact form correlated with building energy consumption and optimization strategies

ABSTRACT With the acceleration of urbanization, building energy consumption carbon emissions (BECCE) were increasing, weightily influencing global warming and the social-economic developments in cities. The detailed exploration of the most effective distance of urban three-dimensional (3D) compact form on BECCE guides urban space optimization. In this study, 135 People’s Banks of China (PBC) were taken as samples. Based on the banks building features, socioeconomic conditions, macroclimate, and urban 3D compact form at different buffers, the most effective distance on the BECCE was determined. According to Partial least-squares regression (PLSR), the results show that the most effective distance of the urban compact form on the BECCE was 150 m. The study divided the normalized 3D compactness index (NVCI) values from 135 buildings for the distance of 150 m into five categories and used the geographic detector to recognize statistically obvious differences between the subregions. The natural breakpoint method was used to categorize the study area into five classes, specifically: low, medium-low, medium, medium-high, and high compact form. Based on the geographic detector method result, the compact form of medium, medium high, and high compact form was optimized to low, medium low. The optimization can effectively reduce the BECCE due to changing the compact form of the building by 65.2% or 65.7%. The study combined mathematical statistics with spatial analysis approaches to identify the most effective distance for reducing energy consumption. Our results will contribute toward considerable reductions in the BECCE.

Spatial and trend analysis of gestational syphilis cases in Brazil from 2011 to 2020: an ecological study

ObjectivesTo analyze the rate of gestational syphilis (GS) based on temporal trends over 11 years, as well as the spatial distribution of GS in Brazil, based on the identification of spatial clusters.MethodologyAn ecological, using Brazil and its regions as an analysis unit, based on gestational syphilis data reported in the Notifiable Diseases Information System (SINAN), from 2011 to 2020. Thematic maps were built for spatial data analysis, and the Prais-Winsten autoregressive model was used to verify the trend. Spatial analysis identified the distribution of clusters (high-high; low-low; high-low and low-high) of distribution of GS across Brazilian municipalities, using a 5% significance level.ResultsGestational syphilis experienced a considerable increase in cases during the studied period, with a peak of 37,436 cases in 2018. The spatial distribution of the disease is heterogeneous in the country. A growing trend was observed in all states of Brazil, except for Espírito Santo, where it remained stationary, with a monthly variation of 10.32%.ConclusionThe spatial and temporal trend analysis point to syphilis as an important public health problem. The numbers are alarming and show the urgent need for measures to prevent and control syphilis during pregnancy.

Spatial transcriptomics implicates impaired BMP signaling in NF1 fracture pseudarthrosis in murine and patient tissues.

The Neurofibromatosis Type 1 (NF1) RASopathy is associated with persistent fibrotic nonunions (pseudarthrosis) in human and mouse skeletal tissue. Here, we first performed spatial transcriptomics to define the molecular signatures across normal endochondral healing following fracture in mice. Within the control fracture callus, we observed spatially restricted activation of morphogenetic pathways, such as TGF-β, WNT, and BMP. To investigate the molecular mechanisms contributing to Nf1-deficient delayed fracture healing, we performed spatial transcriptomic analysis on a Postn-cre;Nf1flox/- (Nf1Postn) fracture callus. Transcriptional analyses, subsequently confirmed through p-SMAD1/5/8 immunohistochemistry, demonstrated a lack of BMP pathway induction in Nf1Postn mice. To further inform the human disease, we performed spatial transcriptomic analysis of fracture pseudarthrosis tissue from a NF1 patient. Analyses detected increased MAPK signaling at the fibrocartilaginous-osseus junction. Similar to the Nf1Postn fracture, BMP pathway activation was absent within the pseudarthrosis tissue. Our results demonstrate the feasibility to delineate the molecular and tissue-specific heterogeneity inherent in complex regenerative processes, such as fracture healing, and to reconstruct phase transitions representing endochondral bone formation in vivo. Furthermore, our results provide in situ molecular evidence of impaired BMP signaling underlying NF1 pseudarthrosis, potentially informing the clinical relevance of off-label BMP2 as a therapeutic intervention.

Cloud-based application for spatial and statistical analysis about environmental fragility and land cover

The MapBiomas project developed a free and open-source platform to monitor Brazilian land use land cover (LULC) changes since 1985. To increase its analytic power, we developed the Environmental Fragility Mapping System (EFMS), using Google Earth Engine (GEE). Environmental fragility (EF) analysis has its origins in ecodynamics, and it is composed of potential environmental fragility (PEF) and emergent environmental fragility (EEF). The advantage of EF analysis over LULC analysis is its flexibility in evaluating multiple scenarios for different aspects of environmental challenges. EFMS calculates three environmental fragility indexes: potential fragility index (PFI), land cover fragility index (LCFI), and emergent fragility index (EFI). Combined with LULC change analysis from the MapBiomas, EFMS can provide spatial analysis and generate maps and data for environmental management. In this article we describe the EFMS method, provide the code and present a case study to generate fragility scenarios for selecting priority areas for environmental recovery.

New Perspective to Evaluate the Carbon Offsetting by Urban Blue-Green Infrastructure: Direct Carbon Sequestration and Indirect Carbon Reduction.

Urban blue-green infrastructure (BGI) offers a multitude of ecological advantages to residents, thereby playing a pivotal role in fortifying urban resilience and fostering the development of climate-resilient cities. Nonetheless, current research falls short of a comprehensive analysis of BGI's overall potential for carbon reduction and its indirect carbon reduction impact. To fill this research gap, we utilized the integrated valuation of ecosystem services and trade-offs model and remote sensing estimation algorithm to quantify the direct carbon sequestration and resultant indirect carbon reduction facilitated by the BGI within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (China). To identify the regions that made noteworthy contributions to carbon offsets and outliers, spatial autocorrelation analysis was also employed. The findings of this study reveal that in 2019, the BGI within the study area contributed an overall carbon offset of 1.5 × 108 t·C/yr, of which 3.5 × 107 and 11.0 × 107 t·C/yr were the result of direct carbon sequestration and indirect carbon reduction, respectively. The GBA's total CO2 emissions were 1.1 × 108 t in 2019. While the direct carbon sequestration offset 32.0% of carbon emissions, the indirect carbon reduction mitigated 49.9% of potential carbon emissions. These results highlight the critical importance of evaluating BGI's indirect contribution to carbon reduction. The findings of this study provide a valuable reference for shaping management policies that prioritize the protection and restoration of specific areas, thereby facilitating the harmonized development of carbon offset capabilities within urban agglomerations.

Assessment of the piroxicam-incited model of synchronized colitis in T-cell receptor alpha chain-deficient mice.

A multitude of mouse models are utilized to emulate and study intestinal inflammation. T-cell receptor alpha chain (TCRα)-deficient mice are used as a model of spontaneous colitis that has similarities with human ulcerative colitis. However, colitis is triggered late in the life of the mouse (age: 4-5 months), and inflammation does not develop at the same time in different mice. A previously conducted study reported that the administration of the drug piroxicam triggered predictable and early colitis in TCRα-deficient mice at the age of 6-8 weeks. However, a detailed characterization of ensuing inflammation was not provided. We conducted an in-depth examination of piroxicam-triggered colitis in TCRα-deficient mice, with emphasis on spatial histopathologic changes and analysis of expression of inflammatory markers. Furthermore, we tested amelioration of colitis with dexamethasone. We confirmed that piroxicam induced a time-prescribed colitis and did so in the proximal colon as well as the cecum of TCRα-deficient mice. Piroxicam administration was observed to induce epithelial hyperplasia, goblet cell loss, and leukocyte infiltration with occasional ulceration. A Swiss roll technique was used to examine the colon and cecum in its entirety. Importantly, we observed that inflammation was multifocal segmental, with areas of tissue damage in between healthy tissue. In addition, we observed variability in the severity of inflammation among replicate animals and treatments, and that the administration of dexamethasone only partially ameliorated inflammation in the proximal colon. Piroxicam consistently induced multifocal segmental colitis in the proximal colon and cecum, although the degree of inflammation was reduced in the latter. Importantly, spatial variability in inflammation in the large intestine and the inter-replicate variation in the severity of inflammation must be taken into consideration when utilizing this murine model of synchronized colitis.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study.

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia. Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed. Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment. The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Synergistic effects of social determinants of health and race-ethnicity on 30-day all-cause readmission disparities: a retrospective cohort study

ObjectiveThe objective of this study is to assess the effects of social determinants of health (SDOH) and race-ethnicity on readmission and to investigate the potential for geospatial clustering of patients...

Exploring factors influencing the agglomeration of implicit consumption space: A comparison with explicit consumption space

The implicit development of consumption space in buildings relying on the O2O business mode is a new form of urban renewal under experience economy and e-commerce. The concept of implicit consumption space emerged as a result. There is a lack of research on implicit consumption space and this study aims to fill this gap. Using POI data and spatial analysis methods, the paper studies the clustering characteristics and influencing factors of the explicit and implicit consumption space in the Old City of Nanjing to provide practical implications for urban renewal, and finds that the implicit consumption space grows fast and has strong clustering, and its layout basically follows the traditional commercial location theory. Commercial districts, office buildings and traffic accessibility are the common influencing factors for the distribution of explicit and implicit consumption spaces. Explicit and implicit consumption space influence each other in layout, while implicit consumption space has a stronger dependence on explicit consumption space. The influencing factors for the agglomeration of explicit and implicit consumption space have spatial variations. The implicit consumption space is an important path for the bottom-up renewal of the old city, and also provides an opportunity for urban function optimization, innovation, and entrepreneurship.

Cell atlas of the regenerating human liver after portal vein embolization

The liver has the remarkable capacity to regenerate. In the clinic, regeneration is induced by portal vein embolization, which redirects portal blood flow, resulting in liver hypertrophy in locations with increased blood supply, and atrophy of embolized segments. Here, we apply single-cell and single-nucleus transcriptomics on healthy, hypertrophied, and atrophied patient-derived liver samples to explore cell states in the regenerating liver. Our data unveils pervasive upregulation of genes associated with developmental processes, cellular adhesion, and inflammation in post-portal vein embolization liver, disrupted portal-central hepatocyte zonation, and altered cell subtype composition of endothelial and immune cells. Interlineage crosstalk analysis reveals mesenchymal cells as an interaction hub between immune and endothelial cells, and highlights the importance of extracellular matrix proteins in liver regeneration. Moreover, we establish tissue-scale iterative indirect immunofluorescence imaging for high-dimensional spatial analysis of perivascular microenvironments, uncovering changes to tissue architecture in regenerating liver lobules. Altogether, our data is a rich resource revealing cellular and histological changes in human liver regeneration.