Spatial Analysis Research Articles

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.

Spatial distribution of inadequate meal frequency and its associated factors among children aged 6-23 months in Ethiopia: Multilevel and spatial analysis.

More than two-third of global child death is occurred due to inappropriate feeding practice that happened during early childhood period. Evidence on meal frequency status among infant and young children at national level can be used to design appropriate interventions to improve the recommended feeding frequency. Therefore, this study was aimed to explore the spatial distribution and identify associated factors of inadequate meal frequency among children aged 6-23 months in Ethiopia. Secondary data analysis was conducted using the 2019 mini Ethiopian Demographic and Health Survey data. A total weighted sample of 1,532 children aged 6-23 months were included. To identify significant factors associated with of inadequate meal frequency, multilevel binary logistic regression model was fitted. Variables with p-value < 0.25 from the bi-variable model were exported to multivariable analysis. In the multivariable model, variables with p-value < 0.05 were declared as significantly associated factors and adjusted odds ratio (AOR) with its 95% confidence interval were reported. Multilevel models were compared using deviance and log-likelihood. Spatial analysis tools were utilized to visualize the distribution of inadequate meal frequency. Bernoulli model was fitted using SaTScan V.9.6 to identify most likely clusters and ArcGIS V.10.8 was used to map the hotspot areas. Ordinary least square and geographic weighted regression models were used and compared using information criteria and adjusted-R2. Local coefficients of factors associated with hotspots of inadequate meal frequency were mapped. The prevalence of inadequate meal frequency was 47.03% (95% CI: 44.54%, 49.53%) in Ethiopia. Age of the child, sex of the household head, timely initiation of breastfeeding, current breastfeeding status, number of antenatal care visit, maternal education, and region were significantly associated with inadequate meal frequency. The spatial distribution of inadequate meal frequency was showed significant variation across Ethiopia (Global Moran's I = 0.164, p-value <0.001). A total of 38 significant clusters were detected through SaTScan analysis, from these the 22 primary clusters were located in Somali and Harari. The prevalence of inadequate meal frequency was high in Ethiopia and had significant clustering patter. Significant hotspot clusters were located in Somali, northern Afar, Harari, Amhara, Gambela, and eastern South nation nationalities and peoples' region. Therefore, public health interventions which enhance breastfeeding practice, optimal number of antenatal care visits, educational empowerments should target hotspot areas to decrease inadequate meal frequency practice.

Space-time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador.

Intestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables. In Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran's index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined. During the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services. This study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

Spatial variations of runoff thresholds associated with changes in land use over the period 1990-2018 in the Mediterranean side of Andalusia

The runoff threshold is the amount of precipitation that exceeds the infiltration capacity of the soil and from which water begins to flow over the land surface. Its spatial variation will determine the appearance of a series of processes and risks such as soil erosion, sediment transport, water pollution or flooding, all of which are of great interest in the field of land planning and management. This article provides an update of the runoff threshold in the territorial scope of the Demarcación Hidrográfica de las Cuencas Mediterráneas Andaluzas and evaluates the spatial changes occurred between 1990 and 2018 because of land use dynamics. For this purpose, the methodology described in Regulation 5.2-IC related to surface drainage, of the Instrucción de Carreteras (Ministerio de Obras Públicas y Urbanismo [MOPU], 1990; Ministerio de Fomento, 2016, 2019) was used. The results obtained show very contrasting runoff threshold values, with maximums in the western sector and minimums in the eastern sector. On the other hand, the dynamics of land use changes have led to a significant increase in impermeable surfaces and sclerophyllous vegetation, which has generated a clear reduction in runoff threshold values in different areas, mainly in the eastern region. Specifically, the former forest areas, currently occupied by sclerophyllous vegetation, stand out, with a reduction of 17 mm over an area of approximately 229.1 km2. The conversion of agricultural lands into urban areas is also noteworthy, with a decrease in their retention capacity by up to 14 mm of precipitation, covering a total area of 97.1 km2.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Evaluation of landscape ecological security pattern via the “pattern-function-stability” framework in the Guanzhong Plain Urban Agglomeration of China

Most studies have assessed and constructed landscape ecological security patterns (LESPs) primarily from the perspective of landscape morphology by employing morphological spatial pattern analysis, with little attention paid to fundamental ecosystem attributes (such as ecosystem functions and stability). Thus, this study proposed a comprehensive evaluation and optimization framework for LESPs from the perspective of “mode function stability” by integrating assessment landscape pattern, ecological function, and landscape stability. A comprehensive evaluation and optimization of the LESPs was conducted using the Minimum Cumulative Resistance (MCR) model in the Guanzhong Plain Urban Agglomeration (GPUA) as the research area. The results showed that: (1) Generally, the landscape quality in urban agglomerations was relatively low because of the large area of agricultural vegetation, high human interference, and landscape fragmentation, which caused low levels of landscape pattern, function, and stability. (2) LESPs were mainly determined by landscape stability in urban agglomerations due to low-quality ecosystems, rapid landscape transformation, and severe anthropogenic disturbances. (3) There was a large variation in the landscape function and stability of a certain landscape type due to their different structures and human interference across space in urban agglomerations. According to assessment by “pattern-function-stability” framework, the ecological sources and corridors covered with natural forest were the crucial cornerstone to constructing LESPs, and the rivers in crop planting areas were the inevitably selected corridors, but their ecological function and stability needed further improvement in construction. (4) The framework can assess spatial patterns and ecological functions to further the stability and sustainability of the landscape in maintaining ecological security and facilitating the identification of improvement objectives or measures of individual ecological sources or corridors in regional ecological infrastructure construction practices.

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.

A machine learning-based framework for spatio-temporal extension and filling of SMOS surface soil moisture observations over Canada

ABSTRACT Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products.

Unraveling the Tourism–Environment–Equity Nexus: A Neighborhood-Scale Analysis of Texas Urban Centers

This study explores the complex interplay between air pollution, the socioeconomic conditions, and the tourism density within Texas’s urban landscapes, focusing on Dallas, Houston, San Antonio, and Austin. Despite extensive research on environmental justice and urban tourism separately, few studies have integrated these fields to examine how tourism development intersects with environmental and socioeconomic disparities at a neighborhood level. This research addresses this gap by employing advanced geospatial analyses and multi-criteria decision analysis to reveal the pronounced clustering of stressed communities on urban peripheries, often removed from tourism’s economic benefits. The study uniquely quantifies the spatial mismatches between tourist hotspots and areas of environmental stress, a dimension often overlooked in the environmental justice literature. Local spatial statistics and cumulative impact analysis uncover statistically significant correlations between high poverty levels and elevated air pollution in specific locales. The results show varying patterns across cities, with Austin presenting the lowest inequality levels and San Antonio exhibiting significant disparities. This granular, neighborhood-centric approach provides novel insights into the tourism–environment–equity nexus, addressing the lack of comprehensive studies linking these factors in rapidly growing Texan metropolitan areas. The findings underscore the critical need for targeted policy interventions and neighborhood-specific approaches in diagnosing urban environmental disparities and crafting equitable urban development policies that consider tourism’s impact on local communities.

Spatial analysis of soil micronutrients in an area of extensive pasture in the southeast of the State of Tocantins

Spatial analysis of soil micronutrients in an area of extensive pasture in the southeast of the State of Tocantins

Assessing Africa's child survival gains and prospects for attaining SDG target on child mortality.

This study assessed Africa's child survival gains and prospects for attaining Sustainable Development Goals (SDG) target 3.2. We analysed multiple country-level secondary datasets of 54 African countries and presented spatial analysis. Results showed that only 8 out of the 54 African countries have achieved substantial reductions in under-5 mortality with an under-five mortality rate (U5MR) of 25 deaths per 1,000 live births or less. Many countries are far from achieving this target. Results of the predictions using supervised machine learning on the Bayesian network reveal that the probability of achieving the SDG target 3.2 (i.e., having U5MR of 25 deaths per 1000 live births or less) increases (from 21.6% to 100%) when the contraceptive prevalence increases from 49.8% to 78.5%; and the use of skilled birth attendants increases from 44.8% to 86.3%; and percentage of secondary school completion of female increases from 42.5 to 74.0%. Our results from Local indicator of spatial autocorrelation (LISA) cluster maps show that 7 countries (mainly in West/Central Africa) formed the high-high clusters (hotspots for U5M) and may not achieve the SDG target 3.2 unless urgent and appropriate investments are deployed. As 2030 approaches, there is a need to address the problem of limited access to quality health care, female illiteracy, limited access to safe water, and poor access to quality family planning services, particularly across many sub-Saharan African countries.

Spatial Analysis of Advanced Air Mobility in Rural Healthcare Logistics

The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions.

The Evolution of Forest Landscape Connectivity and Ecological Network Construction: A Case Study of Zhejiang’s Ecological Corridors

As main components of terrestrial ecosystems, forests play irreplaceable roles in maintaining ecological balance and protecting the basic environment for human survival and development. In this study, the dynamic changes in the forest landscape connectivity in Zhejiang province in 2000, 2010, and 2020 were detected by identifying ecological sources and evaluating connectivity indexes based on morphological spatial analysis (MSPA) and a minimum cumulative resistance (MCR) model. The results are as follows: (1) The forest area of Zhejiang increased by 64.88% from 2000 to 2020, indicating that the overall habitat quality has improved and that ecological risks have decreased, which are attributed to Zhejiang’s adherence to comprehensive environmental management. (2) Regions with low connectivity are distributed mainly in the north, where human activities are intensive. The overall pattern of high connectivity in the middle of the region and low connectivity elsewhere demonstrates the uneven distribution of forest landscape connectivity across the province. (3) The extracted ecological corridors have a mesh-like structure that is dense in the middle and slack in the north. Important corridors have disappeared over time, indicating potential issues in maintaining connectivity for species migration. (4) These results can provide optimization strategies for ecological infrastructure planning in Zhejiang province and offer a theoretical reference for the optimization of the ecological network system.

PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics.

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network's advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.

Epidemiological Characteristics and Spatiotemporal Patterns of Visceral Leishmaniasis in Xinjiang, China, during 2004-2021.

The spread of visceral leishmaniasis (VL), a serious global zoonotic parasitic disease, is mostly under control; however, several cases have been reported in recent decades in Xinjiang, China. This study aimed to analyze the epidemiological status and spatiotemporal clustering characteristics of VL in Xinjiang, China, between 2004 and 2021 to provide a basis for the development and implementation of surveillance and response measures. Data on VL incidence during 2004-2021 were collected from the National Diseases Reporting Information System of China. Global spatial autocorrelation analysis, identification of local indicators of spatial association, and spatial-temporal clustering analysis were conducted to identify the distribution and high-risk areas. A total of 2034 VL cases were reported, with a mean annual incidence of 0.50 per 100,000. There was a general decreasing trend in the incidence of VL during our study period. The majority of the cases were reported from October to February of the following year, and fewer cases were reported from April to July. Spatial autocorrelation analysis revealed that the incidence of VL was spatially clustered within a few counties. Significant differences were observed during the study period (Moran's I = 0.74, Z = 4.900, p < 0.05). The male-to-female ratio was 1.37:1, and most patients were in the age group 0-3 years. Cases were primarily distributed in seven regions and two autonomous prefectures, and Kashgar reported the highest number of cases (1688, 82.98%). Spatial analysis revealed that the aggregation of VL was predominantly observed in southwest Xinjiang. This was in alignment with the high-risk areas identified by spatiotemporal clustering analysis. The H-H clustering region was primarily observed in Gashi, Atushi, Shufu, Injisha, Kashgar, Yepuhu, and Bachu. These findings indicate that integrated control measures must be taken in different endemic areas to strengthen the VL control program in Xinjiang, China.

Mapping urban large‐area advertising structures using drone imagery and deep learning‐based spatial data analysis

AbstractThe problem of visual pollution is a growing concern in urban areas, characterized by intrusive visual elements that can lead to overstimulation and distraction, obstructing views and causing distractions for drivers. Large‐area advertising structures, such as billboards, while being effective advertisement mediums, are significant contributors to visual pollution. Illegally placed or huge billboards can also exacerbate those issues and pose safety hazards. Therefore, there is a pressing need for effective and efficient methods to identify and manage advertising structures in urban areas. This article proposes a deep‐learning‐based system for automatically detecting billboards using consumer‐grade unmanned aerial vehicles. Thanks to the geospatial information from the drone's sensors, the position of billboards can be estimated. Side by side with the system, we share the very first dataset for billboard detection from a drone view. It contains 1361 images supplemented with spatial metadata, together with 5210 annotations.

Ergatic approach to reconstruction and modeling of the spatial future of Ukraine

ABSTRACT The article expresses the idea of ergaticity and the ergatic approach (behavioral model) in decision-making on the spatial development of Ukraine, which considers the state and its space as a ‘system-process’ that is characterized by behavior, the ability to self-organize and self-develop, and the choice of effective behavior in changing realities and situations. Foreign and domestic scientific sources, statistical compilations, project materials, strategies, and programs for the development of Ukraine, its regions, and cities served as source data. The authors use long-term monitoring of processes at different hierarchical levels of the space of the state, their dynamics, and changes. The study resulted in the substantiation of the conceptual provisions of the ergatic approach to planning and modeling the spatial future of Ukraine. The suggested spatial changes relate to functional and urban zoning of the territories of the state; development of connections and understanding of the multidimensional dynamics of processes in the territory; placement of new elements and investments in the existing structure of natural landscape and urban planning conditions; spatial, temporal, structural, and parametric coordination of processes in the space of the state and creation of systemic effects of using the capacity of ergaticity in the spatial development of Ukraine.

IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data.

Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of 32 discrete immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel and different species by applying IMmuneCite to data from murine liver tissue. This approach enabled deep characterization of different functional states in each immune compartment, uncovering key features of the immune microenvironment in clinical liver transplantation and murine hepatocellular carcinoma. In conclusion, we demonstrated that IMmuneCite is a user-friendly, integrated computational platform that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.

Quantile delta-mapped spatial disaggregation analysis for climate variations over the Yangtze River Basin

Abstract Significant increases in temperature and precipitation due to global warming affect socio-economics. Accurate analysis is needed for future temperature and precipitation changes in the Yangtze River Basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time over the YRB. The evaluation results show that while QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, it shows improvement in reproducing precipitation. Projections indicate the annual mean temperature will increase from 2020 to 2080 under SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest ranges of differences in summer. Conversely, under SSP2-4.5, annual mean temperatures significantly decrease from 2081 to 2100. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual mean precipitation will increase from 2020 to 2080 but decreases from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policy-making to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry).

Retraction Note: The spillover of tourism development on CO2 emissions: a spatial econometric analysis.

Retraction Note: The spillover of tourism development on CO2 emissions: a spatial econometric analysis.