Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis

Background

Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab.

Purpose

We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab.

Methods

118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features.

Results

71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation).

Conclusion

Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Spatial Heterogeneity of Nontuberculous Mycobacterial Pulmonary Disease in Shanghai: Insights from a Ten-Year Population-Based Study

OBJECTIVE

To investigate the spatial heterogeneity of nontuberculous mycobacterial pulmonary disease (NTM-PD) in Shanghai.

METHODS

A population-based retrospective study was conducted using presumptive pulmonary tuberculosis surveillance data of Shanghai between 2010 and 2019. The study described the spatial distribution of NTM-PD notification rates, employing hierarchical Bayesian mapping for high-risk areas and the Getis-Ord Gi* statistic to identify hot spots and explore associated factors.

RESULTS

Of 1652 NTM-PD cases, the most common species was Mycobacterium kansasii complex (MKC) (41.9%), followed by Mycobacterium avium complex (MAC) (27.1%) and Mycobacterium abscessus complex (MABC) (16.2%). MKC-PD patients were generally younger males with a higher incidence of pulmonary cavities, while MAC-PD patients were more often farmers or had a history of tuberculosis treatment. MKC-PD hot spots were primarily located in the areas alongside the Huangpu River, while MAC-PD hot spots were mainly in the western agricultural areas. Patients with MKC-PD and MAC-PD exhibited a higher risk of spatial clustering compared to those with MABC-PD.

CONCLUSIONS

Different types of NTM-PD exhibit distinct patterns of spatial clustering and are associated with various factors. These findings underscore the importance of environmental and host factors in the epidemic of NTM-PD.

Evaluating multifaceted effects of watershed properties and human activities on drought propagation in the Wei River Basin with an integrated framework

Understanding the factors driving propagation from meteorological to hydrological drought is crucial for drought mitigation. In this study, an integrated framework based on the Soil and Water Assessment Tool model, standardised drought indices and Geographical Detector were used to investigate how and to what extent watershed properties and human activities affect the spatial heterogeneity of drought propagation in the Wei River Basin, a typical arid and semi-arid region in China. Results indicated that (1) spatially, the propagation times increased from southwest to northeast. Seasonally, the propagation was shorter and stronger in summer and autumn. (2) The aridity index significantly affected the spatial distribution of drought propagation time for the entire basin, especially in summer, while human activities primarily drove spatial distribution in the sub-basins. The explanatory power of any two independent factors was non-linearly enhanced after the interaction. (3) Watershed properties potentially impacted the anthropogenic driving factor of drought propagation. Strong anthropogenic effects on drought propagation often occurred in watersheds with moderate drought levels, steep slopes, low elevations, and small areas, and the key factors varied seasonally. These findings help elucidate the multifaceted effects of watershed properties and human activities on drought propagation. The proposed framework and the results of this study provide valuable guidance for formulating precise drought control strategies in the Wei River Basin and worldwide.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.

Transport accessibility and hospital attributes: A nonlinear analysis of their impact on Women's prenatal care seeking behavior

Ensuring women receive vital prenatal care is crucial for maternal and newborn health. Limited research explores factors influencing prenatal care-seeking from a geospatial perspective. This study, based on a substantial Wuhan dataset (23,947 samples), investigates factors influencing prenatal care-seeking, focusing on transport accessibility and hospital attributes. Findings indicate a nuanced relationship: (1) A non-linear trend, resembling an inverted "U," reveals the complex interplay between transport accessibility, hospital attributes, and prenatal care visits. Hospital attributes have a more pronounced impact than transport accessibility. (2) Interaction analysis underscores that lower prenatal care visits relate to low-income and education levels, despite reasonable public transport accessibility. (3) Spatial disparities are significant, with suburban areas facing increased obstacles compared to urban areas, particularly for those in suburban rural areas. This study enhances understanding by emphasizing threshold effects and spatial heterogeneity, offering valuable perspectives for refining prenatal care policies and practices.

Space-specific flux estimation of atmospheric chemicals from point sources to soil

Predicting chemical flux to soil from industrial point sources accurately at a regional scale has been a significant challenge due to high uncertainty in spatial heterogeneity and quantification. To address this challenge, we developed an innovative approach by combining California Air Resources Board Puff (CALPUFF) and mass balance models, leveraging their complementary strengths in quantitative accuracy and spatial precision. Specifically, CALPUFF was used to predict the polycyclic aromatic hydrocarbons (PAHs) flux to soil due to industrial sources. Additionally, the spatial distribution coefficient of PAHs flux (e.g., si for spatial unit i) was calculated by neural network and combined with the mass balance model to obtain the results of total PAHs fluxes, which were then combined with the results predicted by CALPUFF to effectively estimate the contribution of industrial sources to soil PAHs flux. Taking a petrochemical industry region located in Zhejiang province, China as a case study, results showed the input Phenanthrene (Phe) and Benzo(a)pyrene (BaP) fluxes predicted by CALPUFF were generally lower than those by the mass balance model, with slightly different distribution patterns. CALPUFF results, based on 36 industrial sources, partially represent those of the mass balance model, which includes all sources and pathways. It was suggested that industrial sources contributed 49%-89% and 65%-100% of soil Phe and BaP, respectively across the study area. The average Phe flux from point sources by deposition averaged 2.68 mg m-2∙a-1 in 2021, accounting for approximately 60% of the total Phe flux to soil. The average BaP flux from point sources by deposition averaged 0.0755 mg m-2∙a-1, accounting for only 0.1%-3.65% of the total BaP flux to soil. Thereby, our approach fills up a gap between the relevance to point sources and the accuracy of deposition quantification in estimating chemical flux from specific point sources to soil at a regional scale.

Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive whenR 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case whenR 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

The stromal microenvironment endows pancreatic neuroendocrine tumors with spatially specific invasive and metastatic phenotypes

Cancer-associated fibroblasts (CAFs) play an important role in a variety of cancers. However, the role of tumor stroma in nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs) is often neglected. Profiling the heterogeneity of CAFs can reveal the causes of malignant phenotypes in NF-PanNETs. Here, we found that patients with high stromal proportion had poor prognosis, especially for that with infiltrating stroma (stroma and tumor cells that presented an infiltrative growth pattern and no regular boundary). In addition, myofibroblastic CAFs (myCAFs), characterized by FAP+ and α-SMAhigh, were spatially closer to tumor cells and promoted the EMT and tumor growth. Intriguingly, only tumor cells which were spatially closer to myCAFs underwent EMT. We further elucidated that myCAFs stimulate TGF-β expression in nearby tumor cells. Then, TGF-β promoted the EMT in adjacent tumor cells and promoted the expression of myCAFs marker genes in tumor cells, resulting in distant metastasis. Our results indicate that myCAFs cause spatial heterogeneity of EMT, which accounts for liver metastasis of NF-PanNETs. The findings of this study might provide possible targets for the prevention of liver metastasis.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban-rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8-18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6-38.0%) was shown to carry a higher proportion of the load (70.0-93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6-49.0%, load: 37-90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.

Digital financial inclusion and upgrading of consumption structure: Evidence from rural China

Based on the perspective of spatial economy, this paper focuses on the primary effects and spatial characteristics of Digital Financial Inclusion (DFI) on the upgrading of rural consumption structure (URCS) in China, conducting a literature review and theoretical analysis. It then uses statistical data collected over the years and the Digital Financial Inclusion Index (DFII) of Peking University to prepare panel data for 31 provinces in China (aside from Hong Kong, Macao, and Taiwan) from 2011 to 2020 for empirical testing. The results are as follows: DFI can considerably boost URCS, and there is a strong spatial neighbor impact, that is, it is affected by random shocks in surrounding provinces via its spatial effect; DFI has nonlinear characteristics in the process of fostering URCS, with the threshold variables of income level and family sizes; the impact of DFI on URCS is spatially heterogeneous, and the promotion of the eastern region is better than other zones. These results can inform policymakers about rural development and provide valuable references to push forward rural vitalization.

Spatial variation in stability of wheat (Triticum aestivum L.) straw phytolith-occluded carbon in China

Global climate warming, driven by human activities emitting greenhouse gases like CO2, results in adverse effects, posing significant challenges to human health and food security. In response to this challenge, it is imperative to enhance long-term carbon sequestration, including phytolith-occluded carbon (PhytOC). Currently, there is a dearth of research on the assessment and distribution of the stability of PhytOC. Additionally, the intricate relationships and effects between the stability and environmental factors such as climate and soil remain insufficiently elucidated. Our study provided a composite assessment index for PhytOC stability based on a rapid solubility assay and principal component analysis. The machine learning models that we developed in this study, utilize experimentally and publicly accessible environmental data on large spatial scales, facilitating the prediction and spatial distribution mapping of the PhytOC stability using simple kriging interpolation in wheat ecosystems across China. We compared and evaluated 10 common classification machine learning models at 10-fold cross-validation. Based on the overall performance, the Stochastic Gradient Boosting model (GBM) was selected as predictive model. The stability is influenced by dynamic and complex environments with climate having a more significant impact. It was evident that light and temperature had a significant positive direct relationship with the stability, while the other factors showed indirect effects on the stability. PhytOC stability exhibited obvious zonal difference and spatial heterogeneity, with the distribution trend gradually decreasing from the southeast to the northwest in China. Overall, our research contributed to reducing greenhouse gas emissions and achieving global climate targets, working towards a more sustainable and climate-resilient future.

Exploring the spatial and seasonal heterogeneity of cooling effect of an urban river on a landscape scale

Urban water bodies can effectively mitigate the urban heat island effect and thus enhance the climate resilience of urban areas. The cooling effect of different water bodies varies, however, the cooling heterogeneity of different sections of a single watercourse or river network is rarely considered. Based on various satellite images, geospatial approaches and statistical analyses, our study confirmed the cooling heterogeneity from spatial and seasonal perspectives of the Suzhou Outer-city River in detail in the urban area of Suzhou, China. The cooling effect of the river was observed in the daytime in four seasons, and it is strongest in summer, followed by spring and autumn, and weakest in winter. The combination of the width of the river reach, the width and the NDVI value of the adjacent green space can explain a significant part of the cooling heterogeneity of the different river sections in different seasons. Land surface temperature (LST) variations along the river are more related to the width of the river reach, but the variations of the cooling distance are more related to the adjacent green space. The cooling effect of a river reach could be enhanced if it is accompanied by green spaces. In addition, the cooling effect of a looping river is stronger on the inside area than on the outside. The methodology and results of this study could help orient scientific landscape strategies in urban planning for cooler cities.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

Understanding the spatial disparity in socio-economic recovery of coastal communities following typhoon disasters

The increasing risk of climate change in the Anthropocene underscores the importance and urgency of enhancing resilience to climate-related disasters. However, the assessment of resilience to disasters with traditional statistical data is spatially inexplicit and timeliness inadequate, and the determinants of resilience remain unclear. In this study, we employed spatially detailed daily nighttime light images to assess socio-economic disturbance and track near real-time recovery of coastal communities in Southeast China following super typhoon Meranti. Furthermore, we constructed a "exposure-sensitivity-adaptive capacity" framework to explore the role of key factors in shaping spatiotemporal patterns of recovery. Our case study showed a significant spatial disparity in socio-economic recovery in the post-typhoon period. Low-urbanized areas recovered relatively rapidly with the weakest socio-economic disturbance they suffered, and middle-urbanized areas experienced the slowest recovery despite the disruption being moderate. Remarkably, high-urbanized areas were the most severely impacted by the typhoon but recovered fast. The exposure to hazard, socio-economic sensitivity, and adaptive capacity in communities explained well the spatial disparity of resilience to the typhoon. Maximum wind speed, percentage of the elderly, and percentage of low-income population significantly negatively correlated with resilience, whereas commercial activity intensity, spatial accessibility of hospitals, drainage capacity, and percentage of green open space showed significantly positive relationships with resilience. Notably, the effects of key factors on resilience were spatially heterogeneous. For instance, maximum wind speed exhibited the strongest influence on resilience in middle-urbanized areas, while the effect of commercial activity intensity was most pronounced in low-urbanized areas. Conversely, spatial accessibility of hospitals and drainage capacity showed the strongest influence in high-urbanized areas. Our study highlights the necessity of linking post-disaster recovery with intensity of hazard, socio-economic sensitivity, and adaptive capacity to understand community resilience for better disaster risk reduction.

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

With the deepening linkage between housing and finance, the financial attributes of housing have been increasing. Thus, housing financialization has become a worldwide phenomenon that is gradually emerging in China's real estate market and thus cannot be ignored. The amount of urban capital is an important manifestation of financialization, but only a few studies have considered the spatial heterogeneity of impact of urban capital amount-represented by loan balances (LOAN) on housing prices. To fill this gap, this study builds a dataset of housing prices and influencing factors for county-level units using 2109 counties in China and analyzes the spatial scope and heterogeneity of housing financialization. Results show that globally, LOAN has a significant positive effect on housing prices, and the impact direction is in line with theoretical expectations. Locally, spatial heterogeneity exists for the impact of LOAN on housing prices, and the phenomenon of housing financialization is mainly observed in China's eastern coastal area. This study can help enhance the understanding of the spatial constraints on the impact of LOAN on housing prices and the spatial heterogeneity of housing financialization in China. Moreover, it provides a theoretical basis for policymakers to formulate spatially differentiated housing policies.

An inexpensive "do-it-yourself" device for rapid generation of uniform tumor spheroids

Three-dimensional (3D) cancer cell culture models such as tumor spheroids better recapitulate in vivo tumors than conventional two-dimensional (2D) models. However, two major challenges limit the routine use of 3D tumor spheroids. Firstly, most existing methods of generating tumor spheroids are not high-throughput. Secondly, tumor spheroids generated using current methods are highly variable in dimension. Here, we describe a simple 'Do-It-Yourself (DIY)' device that can be assembled for less than $7 of parts and generate uniform tumor spheroids in a high-throughput manner. We used a simple phone coin vibrating motor to superimpose the vibration for breaking a laminar jet of cell-loaded alginate solution into equally sized spherical beads. We generated 3,970 tumor spheroids/min, which exhibited a hypoxic core recapitulating in vivo tumors and could be used to test the diffusion efficacy of anticancer drugs. Such low-cost, easy-to-fabricate, simple-to-operate systems with high-throughput outcomes are essential to democratize and standardize cancer research.

Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model

HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15-54 years and women aged 15-49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations.

Unveiling spatial disparities in basic medical and health services: insights from China's provincial analysis

Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2012 to 2019, this paper constructs the evaluation index system of basic medical and health services in China from seven dimensions: medical and health facilities, health expenditure, medical services, traditional Chinese medicine hospital services, maternal and child health care, people's health and medical security, disease control and public health. The entropy method was used to measure the level of basic medical and health services in China, and its spatial differences and convergence characteristics were further investigated. In this study, we employ the entropy weight method, σ convergence, and β convergence as our primary methodologies. The entropy weight method is used to evaluate the variability of each indicator, determine the weights of indicators, and quantify the information content of the data. σ convergence illustrates the process by which the variance of a sample decreases over time. β convergence refers to the gradual approach of variables within an economic system towards their long-term equilibrium level over time. The results show that: (1) The scores of basic medical and health services in China's four major regions (including Northeast, East, Central and West) remain in a relatively stable state, with small fluctuations and great room for improvement; (2) There are significant regional differences in the level of basic medical and health services in China, and the intra-regional differences are much greater than the inter-regional differences; (3) There is no significant σ convergence observed in China and its four major regions; however, there is a notable presence of β convergence.

Determinants and their spatial heterogeneity of carbon emissions in resource-based cities, China

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of "northeast high, southwest low" characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences.

Transport dynamics of watershed discharged diffuse phosphorus pollution load to the lake in middle of Yangtze River Basin

Diffuse pollution, including that in the lower and middle reaches of the Yangtze River, is the primary source of pollution in several agricultural watersheds globally. As the largest river basin in China, the Yangtze River Basin has suffered from total phosphorus (TP) pollution in the past decade owing to diffuse pollution and aquatic ecology destruction, especially in the midstream tributaries and mid-lower reaches of the lakes. However, the transport dynamics of diffuse pollutants, such as phosphorus (P) from land to water bodies have not been well evaluated, which is of great significance for quantifying nutrient loss and its impact on water bodies. In this study, diffuse pollution estimation with remote sensing (DPeRS) model coupled with Soil and Water Assessment Tools (SWAT) was utilized to simulate the transport dynamics of P, investigate the spatial heterogeneity and P sources in the Poyang Lake Basin. Additionally, the P transport mechanism from land to water and the migration process in water bodies were considered to investigate the impact of each loss unit on the water body and evaluate the load generated by diverse pollution types. The estimated diffuse TP loss was 6016 t P·yr-1, and the load to inflow rivers and to Poyang Lake were 11,619 and 9812 t P·yr-1, respectively. Gan River Basin (51.09%) contributed most TP to Poyang Lake among five inflow rivers, while waterfront area demonstrated the highest TP load per unit area with 0.057 t km-2·yr-1. Our study also identified P sources in the sub-basins and emphasized agricultural diffuse sources, especially planting, as the most significant factor contributing to TP pollution. Additionally, to improve the aquatic environment and water ecological conditions, further nutrient management should be applied using a comprehensive approach that encompasses the entire process, from source transportation to the water body.

Spatial distribution of rare earth elements and their impact factors in an area with a high abundance of regolith-hosted deposits

Despite the widespread occurrence of regolith-hosted rare earth elements (REEs) across South China, their spatial distribution characteristics in soils and their impact factors remain largely uncertain. This knowledge gap impedes the exploration of regolith-hosted REE deposits and the assessment of the environmental risks associated with REEs. To address this issue, 180 soil samples were collected from Meizhou City, Guangdong Province, a region known for its high abundance of regolith-hosted REEs. Subsequently, the correlations between REE enrichment/fractionation and various factors, i.e., topography, climate conditions, land use, and landform were analysed using the geo-detector method. The results revealed a highly uneven spatial distribution of REEs and their fractionation features with some regions displaying distinct spatial patterns. Elevation was the dominant factor influencing this distribution, and showed strong correlations with the concentrations of REEs, light REEs (LREEs) and heavy REEs (HREEs); the LREE/HREE ratio; and the positive Ce anomaly (δCe). The negative Eu anomaly (δEu) showed a good correlation with rock type. The enrichment and fractionation of REEs indicated a coupling among the abovementioned factors. For REE enrichment, areas with elevations of 138-148 m, precipitation levels of 1553-1574 mm, annual average land surface temperatures of 30.4-30.5 °C, leaf area index values of 22-29 and surface cutting degree of 21.5-29.9 m showed the highest average abundance within each type (scope) of the predominant factors. These findings highlight the key factors affecting REE distribution, thereby aiding the efficient utilization of regolith-hosted REE resources and the evaluation of their environmental risks.

The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.

Mapping spatial heterogeneity in gastric cancer microenvironment

Gastric cancer (GC) is difficult to characterize due to its heterogeneity, and the complicated heterogeneity leads to the difficulty of precisely targeted therapy. The spatially heterogeneous composition plays a crucial role in GC onset, progression, treatment efficacy, and drug resistance. In recent years, the technological advancements in spatial omics has shifted our understanding of the tumor microenvironment (TME) from cancer-centered model to a dynamic and variant whole. In this review, we concentrated on the spatial heterogeneity within the primary lesions and between the primary and metastatic lesions of GC through the TME heterogeneity including the tertiary lymphoid structures (TLSs), the uniquely spatial organization. Meanwhile, the immune phenotype based on spatial distribution was also outlined. Furthermore, we recapitulated the clinical treatment in mediating spatial heterogeneity in GC, hoping to provide a systematic view of how spatial information could be integrated into anti-cancer immunity.

Runoff and soil loss are drastically decreased in a rubber plantation combining the spreading of logging residues with a legume cover

Soil erosion on agricultural land is a major threat for food and raw materials production. It has become a major concern in rubber (Hevea brasiliensis) plantations introduced on sloping ground. Alternative agroecological crop management practices must be investigated. One aim of our study was to assess the ability of logging residues (i.e., trunks, branches, leaves and stumps of a clearcut plantation) and of legume cover (Pueraria phaseoloides) to mitigate N, P and K losses through runoff and soil detachment in a young rubber plantation. The other aim was to investigate the relationships of these nutrient losses with soil structure and soil macrofauna diversity. Runoff and soil loss were monitored for 3 years using 1-m2 plots under different practices as regards the management of logging residues and the use or not of a legume. The monitoring started when rubber trees were one-year-old. The planting row, where soil was bare, was the hotspot of soil erosion, with an average runoff of 832 mm y-1 and soil loss of 3.2 kg m-2 y-1. Sowing a legume in the inter-row reduced runoff and soil loss by 88 % and 98 % respectively, compared to bare soil. Spreading logging residues as well as growing a legume cover almost eliminated runoff and soil detachment (19 mm y-1 and 4 g m-2 y-1 respectively). Nutrient losses were negligible as long as the soil surface was covered by a legume crop, with or without logging residues. Total N loss from soil detachment ranged from 0.02 to 0.2 g m-2 y-1, for example. Spreading logging residues in the inter-rows significantly improved soil structure and soil macrofauna diversity compared to bare soil. Nutrient losses from runoff and soil detachment were negatively correlated with improved soil structure and soil macrofauna diversity. We recommend investigating alternative ways to manage planting rows.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

Landscape and land use affect composition and nutritional values of bees' food

Bees are primary pollinators across various terrestrial biomes and rely heavily on floral resources for sustenance. The composition of landscapes can influence bee foraging behavior, while human activities can directly affect both the composition and nutritional value of bee food. We aimed to assess how landscape structure and land use practices can impact the composition and nutritional value of food sources for two generalist social bee species, Apis mellifera and Scaptotrigona postica. Food samples were collected from twenty-five colonies of A. mellifera and thirteen of S. postica to examine how food composition and nutritional value may vary based on the extent of human land use and the composition of landscapes surrounding beekeeping sites. The pollen composition and nutritional value of A. mellifera were influenced by both land use practices and landscape heterogeneity. The number of patches determined total sugar and lipid content. Landscape heterogeneity affected pollen composition in S. postica, primarily due to the number of patches, while total sugar was affected by landscape diversity. Pollen nutritional value in S. postica was linked to land use, mainly meadow and vegetation, which influenced total sugar and dry matter. S. postica showed a higher sensitivity to land use changes compared to A. mellifera, which was more affected by landscape heterogeneity. Assuring landscape heterogeneity by preserving remaining forest patches around apiaries and meliponaries is crucial. Thoughtful land use planning is essential to support beekeeping activities and ensure an adequate quantity and quality of bee food resources.

Scale effects and spatial heterogeneity of driving factors in ecosystem services value interactions within the Tibet autonomous region

Widespread land development, deforestation, and wetland degradation have disrupted the physical integrity and functional capacity of ecosystems, leading to a reduction in ecosystem service values (ESV). However, comprehensive research addressing ESV interactions that represent various ecosystem services from multifaceted angles is limited. Moreover, the relative significance and spatiotemporal diversity of natural and socio-economic variables influencing ESV demand further investigation. This study conducts both quantitative and qualitative assessments of the spatiotemporal dynamics and interrelationships of ESV in the Tibet autonomous region from 2000 to 2020. Geographical detector and geographically weighted regression models are applied to ascertain the relative importance and spatial heterogeneity of diverse ESV determinants. The findings reveal the following key insights: (1) Barren lands experienced the most substantial expansion from 2000 to 2020, indicating an exacerbation of desertification in the Tibet autonomous region. (2) Over the two decades, ESV exhibited an overall upward trajectory, with regulation of water flows, water bodies, and forests making the most significant contributions to ESV and its growth. (3) The quantitative and qualitative assessment of ESV interrelations has identified the number of trade-offs and synergies, along with spatial occurrences, offering a detailed foundation for the scientific management of ecosystems. Specifically, quantitative results portray ESV correlations as positive or negative, qualitative spatial mapping elucidates intricate local interactions among ESV. (4) The primary driver of ESV in the Tibet autonomous region is NDVI (0.072), with elevation following closely behind, underscoring the predominant influence of natural factors relative to socio-economic variables. This research serves as a scientific underpinning for the development of ecological conservation policies and the execution of ecological restoration initiatives.

The spatial relationship between long-term vacant housing and non-communicable diseases in U.S. shrinking and growing metropolitan areas

The rising prevalence of non-communicable diseases (NCDs) has led to increased attention on understanding how built environments affect NCD risks. However, there's a significant gap in the literature regarding the relationship between housing vacancy duration and NCDs in metropolitan areas with varying development rates. Our research addresses this gap by examining the association between housing vacancy duration and NCDs across all U.S. metropolitan areas, considering growing, shrinking, and fluctuating counties. We used a Multiscale Geographically Weighted Regression (MGWR) model to analyze this relationship, finding that longer-term vacant housing (over 3 years) is more positively associated with NCDs compared to short-term vacancies. We also discovered that this association is non-uniform across metropolitan counties, except for cancer and stroke outcomes. Shrinking counties in the Northeast are particularly affected, emphasizing the need for targeted public health interventions in these areas. This study underscores the importance of revitalizing vacant homes, especially those vacant for over 3 years, in both shrinking and growing regions to improve public health. Policymakers should adopt tailored strategies, engage public health experts, and invest in healthcare infrastructure to effectively address the health risks linked to vacant housing.

Tracking household carbon inequality in China: Composition effect or coefficient effect?

Reasonable allocation of carbon emission reduction tasks requires addressing household carbon inequality. This study aims to track characteristics of household carbon inequality in China using the recentered influence function (RIF) based on the Household Tracking Survey data in 2018 and the multi-regional input-output table. The Oaxaca-Binder decomposition based on RIF further decomposes household carbon inequality based on spatial heterogeneity into composition and coefficient effects. The results indicate that (1) household carbon inequality is widespread in China, generally close to the 60/30 distribution, favouring high-income families. Furthermore, (2) increases in income, wealth and economic burden and declining marriage rate promote household carbon inequality, which is suppressed by the development of education and the Internet and the increase in car ownership. Additionally, (3) the carbon inequality of urban households is smaller than that of rural households, which is contributed by the composition effects of family size, education, car ownership, Internet development and the coefficient effect of income and housing. Finally, (4) under the composition effect of family size and the coefficient effect of income, the household carbon inequality in the eastern region is smaller than in the central and western regions.

Identifying conservation and restoration priorities for degraded coastal wetland vegetations: Integrating species distribution model and GeoDetector

The ongoing degradation of seagrass and seaweed is of global concern. Comprehending the spatial distribution of these wetland vegetation types and the threats they face becomes critical for effective conservation and restoration efforts. In this study, we combined a species distribution model and geographical detector to propose a novel framework for mapping the distribution and disturbance of degraded coastal wetland vegetation in sparsely recorded areas and identifying conservation and restoration priorities. Guangxi is a province in China known for its extensive coastal wetland vegetation. In our study of Guangxi, habitats suitable for two degraded vegetation types, i.e., seagrass and seaweed, were mapped using the maximum entropy model; 669.44 km2 of seagrass habitat and 929.69 km2 of seaweed habitat were identified. The geographical detector model was used to analyze anthropogenic disturbance caused by four local disturbance factors: shoreline development, fisheries, waterways, and ports and anchorages. Shoreline development was identified as the disturbance factor with the strongest impact on potential habitats of both vegetation types. According to these findings, 48.40 %-64.23 % of the vegetation habitats suffered from high anthropogenic disturbance. Preexisting nature reserves had not effectively protected wetland vegetation from human disturbance. Based on the spatial pattern of vegetation habitat and comprehensive anthropogenic disturbance, conservation and restoration priorities for seagrasses and seaweeds covering an area of 302.26 km2 were further mapped. Our results thus help improve wetland vegetation conservation by providing basic information, and they provide a tool to support site planning for seagrass and seaweed conservation and restoration.

City-level decoupling between livestock and crop production and its effect on fertilizer usage: Evidence from China

China is one of the largest producers of livestock production and also with tremendous fertilizer consumption in crop production, regional decoupling between livestock and crop production often results in fertilizer overuse and environmental pollution. However, city-level coupling analysis between livestock and crop production is rare, and its impact on fertilizer usage also remains unclear. Here, we evaluated the nitrogen (N) nutrient supply from the livestock breeding sector and the N nutrient demand of cropland during the 2007-2020 period in a typical agricultural region in China. The city-level coupling degree of livestock and crop production and the effect on fertilizer usage were explored with spatial analysis and regression methods. Our results show that the province level has a relatively high coupling degree. However, significant spatial heterogeneity was found at the city level, especially in western Sichuan Province due to the highly unbalanced distribution of livestock and crop production, and this decoupling phenomenon may hinder fertilizer reduction. Furthermore, we reveal that technological development is not an effective way to achieve sustainable agriculture without other policy instruments, such as livestock spatial relocation, which must be considered when formulating crop-livestock integration policies. The findings expand city-level knowledge of the livestock-crop system and provide important implications for adjusting agricultural practices to realize sustainable agricultural development.

What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in the Northeast China (NEC) is essential for establishing suitable and sustainable cropping patterns that are adapted to local conditions, and for promoting the optimal use of black soil resources. Here, we classified the major grain crop cover types and investigated their spatiotemporal dynamic in the NEC by combining multi-source remote sensing imagery and phenological information based on the Google Earth Engine (GEE) platform. A number of typical cropping patterns from 2017 to 2021 were defined and extracted, and the characteristics of their spatial heterogeneity were analyzed. Driving mechanisms for the spatial heterogeneity of cropping patterns were revealed using Geodetector. The results concluded that over the past five years (2017-2021), there has been a shift from soybean to maize in the NEC, while rice has remained stable in terms of spatiotemporal dynamics. Seven dominant cropping patterns showed high spatial heterogeneity and positive spatial agglomeration. The center of gravity of the cropping pattern shifted southwards as the frequency of maize planting increased, while the center of gravity shifted northwards as the frequency of soybean planting increased, while the rice cropping pattern remained stable. The interaction between black-soil productivity index (BPI) and total grain income trend (TGIT) exhibits the most pronounced impact on the spatial heterogeneity of cropping patterns, with a q statistic of 0.523. Following closely are the interactions of soybean subsidies trend (SST), rice subsidies trend (RST), and maize subsidies trend (MST) with TGIT, with q statistics of 0.481, 0.472, and 0.452, respectively. Among the seven dominant cropping patterns, the soybean-based cropping pattern had the highest level of TGIT and BPI, followed by the maize-based cropping pattern, while the rice-based cropping pattern had the lowest level. All of the natural environmental, agri-economic and policy factors have a synergistic effect in contributing to the spatial heterogeneity of cropping patterns. Natural environmental factors determine the overall spatial distribution of cropping patterns in the NEC, while economic and policy factors combine to influence farmers' decisions, resulting in diverse regional cropping patterns. It is recommended that maize-soybean rotations such as Maize-Soybean Alternate Cropping (MSAC) and Maize-Soybean Rotational Cropping (MSRC) should be promoted, especially in the central and southern regions of the NEC, to meet agricultural market demand and stabilize soil productivity.

Carbon emissions trading scheme and regional total factor carbon productivity: based on temporal-spatial dual perspectives

The carbon emissions trading scheme (CETS) in China is an important market-based environmental policy mechanism for decreasing carbon emissions. This paper calculates the total factor carbon productivity (TFCP) based on data from 275 cities in China from 2007 to 2020 using the DEA method and investigates the impact of the CETS on regional TFCP using the differences-in-differences (DID) method, all against the backdrop of carbon peaking and carbon neutrality. The research findings reveal that CETS has consistently improved TFCP in pilot cities, and this conclusion has held up following a number of robustness tests. Temporal heterogeneity experiments demonstrate that as implementation time increases, the enhancing effect takes on an inverted "U-shaped" structure with a 7-year effective lifetime. Spatial heterogeneity studies reveal that as one moves away from the pilot cities, the policy effect on surrounding cities' TFCP is inhibited, followed by facilitation. CETS policies can influence regional TFCP through the effects of green innovation and industry upgrading, according to mediation mechanism testing. We present policy recommendations based on the research findings for meeting the "dual" carbon goals and strengthening the carbon trading mechanism.

Responses of biodiversity to microhabitat heterogeneity in debris flow gullies: Assessing the impact of hydrological disturbance

Rivers play a vital role in the maintenance of the biosphere and human society, since they participate in the global water cycle and provide varied habitats to support biodiversity. Microhabitat heterogeneity is regarded as a key factor driving biodiversity and it plays an active ecological role in different types of mountain rivers. Whether river microhabitat heterogeneity exhibits the same ecological patterns across hydrological periods remains unclear. Here, we analyzed the changes in macroinvertebrate community composition, functional traits, and multi-faceted α-diversity in five debris flow gullies in the Xiaojiang River Basin (southwestern China) between two different hydrological periods. We explored the responses of biodiversity to river microhabitat heterogeneity and its driving factors before and after hydrological disturbance. The results indicated that river microhabitat heterogeneity and three facets of macroinvertebrate α-diversity decreased after hydrological disturbance, with macroinvertebrate state traits becoming more unbalanced. Macroinvertebrate taxonomic diversity increased with increasing river microhabitat heterogeneity across hydrological periods, and this pattern was more prominent before hydrological disturbance. A high correlation emerged between macroinvertebrate phylogenetic diversity and river microhabitat heterogeneity only before hydrological disturbance. Hydrogeomorphic parameters prominently affected macroinvertebrate communities before hydrological disturbance. Water environmental parameters worked together with hydrogeomorphic parameters to shape macroinvertebrate communities in hydrologically disturbed debris flow gullies, indicating a reduced ecological role of river microhabitat heterogeneity. The ecological health of debris flow gullies can be improved by increasing vegetation coverage on river bank slopes to increase slope stability and mitigate hydrological disturbances, as well as placing large rocks into river channels to enhance riverbed stability and create habitats for more biological groups.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model. It was demonstrated that regional spatial information improved the prediction performance of the model (R2 > 0.7). On the basis of the prediction results, the status of HM pollution in the Pearl River Delta (PRD) region was evaluated: cadmium (Cd) and copper (Cu) were the most serious pollutants in the PRD (the point overstandard rates are 18.77% and 12.95%, respectively). The analysis of index importance and bivariate local indicators of spatial association (LISA) shows that the key factors affecting the spatial distribution of heavy metals are geographical and climatic conditions [namely, altitude, humidity index, and normalized vegetation difference index (NDVI)] and some industrial activities (such as metal processing, printing and dyeing, and electronics industry). This study develops a novel approach to improve existing spatial interpolation techniques, which will enable more precise and scientific soil environmental management.

Comprehensive evaluation of community human settlement resilience and spatial characteristics based on the supply-demand mismatch between health activities and environment: a case study of downtown Shanghai, China

INTRODUCTION

Under globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment.

METHODS

Using data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience.

RESULTS

i) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement.

CONCLUSION

This paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment

Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil. Of the samples with Pb, Cd and Hg, 84.12 %, 68.03 % and 41.57 % exceeded the background values, and 5.36 %, 6.42 % and 5.43 % were moderately contaminated according to the geoaccmulation index, respectively. Industrial activities, with relative importance values of 17.82 %, 31.54 % and 26.51 % for Cd, Hg and Pb, respectively, were the predominant source of these metals especially, in their high-content cluster areas (central urban areas). Soil available phosphorus was another important factor (relative importance values of 13.03 %, 13.41 % and 25.55 % for Cd, Hg and Pb, respectively), and agricultural activities (especially the overuse of phosphoric fertilizers) were identified as an anthropogenic source of these toxic metals. Soil parent material had the greatest influence on As and Cr, with relative importance values of 19.88 % and 19.09 %, respectively, especially in their high-content accumulation area (the eastern coastal area), indicating that these toxic metals mainly come from natural sources. Slope had important impacts on toxic metal accumulation (relative importance values of 17.48 %, 21.22 %, 12.40 % and 16.13 % for Cd, Hg, Cr and As, respectively) by influencing industrial distribution and pollutant migration. By changing the soil adsorption capacity, soil organic matter (explaining 13.01 % of Pb) and soil pH (explaining 14.58 % of As and 12.40 % of Cr) strongly influenced toxic metal accumulation. This study highlights the benefits of the integrated Catreg-GWR model for analyzing multiple spatially heterogeneous environmental data types (numerical and categorical variables), providing a potential foundation for local pollution prevention.

Study on the structural characteristics of China's high-speed railway network and its coordination with economic growth based on Fractal theory

As one of the modern transportation modes, the high-speed railway network system has been a robust part of the comprehensive transportation system in China. An important topic emerges the exploration and optimization of its structural organization and coordinated relationship with the regional development, including urban form, land use, and economy. Therefore, supported by the integration of geographical information system (GIS) and fractal theory, this paper aims to carry out an investigation and discussion on the structural characteristics, including intensity (density), complexity, nonstationarity, and heterogeneity of the high-speed railway network in China (HSRNC) from the perspective of the whole country and specific regions, i.e., urban agglomerations. Moreover, based on the time-series data of network mileage expansion and economic output analysis, this study aims to evaluate and characterize the coordinated relationships between network development and economic growth in the context of the nationwide area and urban agglomerations. This study aims to explore and promote the spatial structural organization and morphology of the high-speed railway network in China, thus improving the coordinated development with the regional economic growth, for giving a new perspective to the future planning and evolution of the high-speed railway network in China.

Spatial differentiation characteristics of regional self-driving tourism flow: A case study of central Yunnan urban agglomeration

The aim of this study was investigate the spatial effects of A-class scenic spots and the spatial distribution of highway networks on the influence of self-driving tour behavioral patterns in China at the urban agglomeration scale, based on big data of road traffic during three holidays. A spatial analysis method and a geographically weighted regression model were used to analyze the spatial distribution differences and influencing factors of self-driving tourism flows in the central Yunnan urban agglomeration. The results showed that holiday self-driving tourism in the central Yunnan urban agglomeration presented a typical core-edge spatial pattern. The mean value of the spatial autocorrelation coefficient was 0.54, indicating significant spatial autocorrelation. The influence of tourism resources and traffic conditions on self-driving tourism flow showed a decreasing trend from the center of the high positive value to the periphery of the main urban area of Kunming. This study reveals the spatial differentiation characteristics of self-driving tourism flows in urban agglomerations and lays a theoretical foundation for understanding flow pattern.

Spatiotemporal dynamics and influencing factors of carbon productivity in counties of Shandong Province, China

In the context of the increasing global greenhouse effect, the Chinese government has proposed a "dual carbon" target. As a major carbon-emitting province in China, Shandong Province needs to improve its carbon productivity to coordinate carbon emission reductions and sustainable economic growth. This study analyzes the spatial and temporal evolution of carbon productivity at the county scale and the factors influencing it in Shandong Province from 2000 to 2017. The study uses the Dagum Gini coefficient, kernel density analysis, spatial autocorrelation model, and geographically and temporally weighted regression model. The results indicate that the carbon productivity in Shandong Province nearly doubled during the study period, revealing a spatial distribution characteristic of "high in the east and low in the west," together with a significant positive spatial autocorrelation. Intra-regional differences, the most important source of development differences among the three economic circles, rose to 32.11% during the study period, whereas inter-regional differences declined to 26.6%. Gross domestic product per capita and population density play a significant positive role in the development of carbon productivity. The balance of deposits in financial institutions at the end of the year has a weak positive effect, and the local average public finance expenditure and secondary industry structure on carbon productivity are negative in general. Shandong Province should identify specific regions with weak carbon productivity levels and understand the key factors to improve carbon productivity to promote the achievement of the "dual carbon" goal.

Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.

Impact of built environment on urban surface temperature based on multi-source data at the community level in Beilin District, Xi'an, China

With the global warming and rapid urbanization in China, the urban built environment has undergone rapid changes, and the land surface temperatures (LSTs) of urban communities have obvious spatial heterogeneity. To explore the key driving factors of community LSTs, the multi-source data and spatial statistical methods being jointly used to analyze the spatial characteristics and main influencing factors of LST at the community level in the Beilin District of Xi'an City, China. The results are as follows: (1) Compared with communities dominated by construction land, communities with large area of green space and water bodies have lower LST. (2) According to the Akaike's information criterion (AICc) and maximum of adjusted R2, and other parameters, the No.1236 model was selected as the optimal model to analyze the influencing factors of community LST by exploratory data analysis, including building density (BD), building height standard deviation (BHS), percentage of public administration and public services land (PASL), percentage of green space and square land (PGSL), population density (POPD), normalized difference impervious surface index (NDISI), and perimeter-area fractal dimension (PAFRAC). (3) For each increase of one unit in NDISI and BHS when other factors remain unchanged, the LST will increase by 0.569 °C and decrease by 0.478 °C, respectively. (4) From the spatial stability and distribution of Local-R2, the warming factors of community LST are mainly NDISI, PAFRAC, BD, and PASL, while the cooling factors are BHS and PGSL. The spatial heterogeneity of community LST is not only related to the change of underlying surface properties but is also affected by intra-urban architectural morphology. Therefore, reasonable planning of urban built environment is of great significance for mitigating heat islands.

Shifts bidirectional dependency between vegetation greening and soil moisture over the past four decades in China

Soil moisture (SM) has changed significantly over the past 40 years in China, while NDVI has varied dramatically, leading to increasing regional conflict between vegetation growth and water resource use. Quantifying the bidirectional dependency between SM and NDVI is essential for understanding the balance between land vegetation and water resources. However, few studies have reported their mutual feedback and spatiotemporal bidirectional dependency. This paper aims to reveal the bidirectional dependency between SM and NDVI using Granger causality test to show spatiotemporal tendency coupling patterns through trend coupling analysis, wavelet transform, and lag correlation. The Results indicated that a coupling relationship existed between SM and NDVI over most of China. The unidirectional Granger effect between SM on NDVI was 58 %, the unidirectional Granger effect of NDVI on SM was 26 %, and the bidirectional Granger relationship between SM and NDVI was 16 %. The Granger relationship is different for different soil layers or land cover types. SM and NDVI increased together in 36 % of the land cover areas, but SM increased and NDVI decreased in 12 %, and the SM decreased and NDVI increased in 27 %. The trend coupling between SM and NDVI has spatial heterogeneity. There is no change rule of coupling relationship with drought variation, but SM and NDVI increased together with more overlapping ecological restoration projects. SM decreased with the increase of NDVI from 1982 to 2010 but has reversed since 2011. NDVI and SM co-increased significantly with the implementation of ecological restoration projects during 2011-2022. The coupling relationship has a time lag effect of 1-3 months, and the time lag of NDVI to SM of deep soil layers mainly occurred in Southern China. This study illustrated the coupling framework and feedback analysis between SM and vegetation greening, which is helpful for the scientific implementing ecological restoration projects and the management of ecosystem carbon and water cycles.

Effects of environmental factors on avian communities in urban parks in small- to medium-sized city: a case study of Fuyang City, Anhui, China

With a worldwide expansion of urbanization, the conservation of urban biodiversity is attracting growing attention; it is important to study the relationship between wildlife and urban green spaces. In this study, we selected 31 parks in the urban area of Fuyang City in the North China Plain. A total of 8795 individual birds from 69 species were recorded. The study found that (a) at the local level, tree diversity and heights are the most important factors contributing to each level of bird diversity, followed by the coverage of shrubs and herbs, and (b) at the landscape level, the proportion of woodland has a strong positive correlation with the multidimensional diversity of birds, followed by the patch diversity and percent of grassland. Our results showed that artificial greenland can effectively increase bird diversity. While considering urban planning and human well-being, the proportion of vegetation and landscape in urban parks should be properly planned, providing more habitats to enrich bird diversity.

Determinants of the embodied CO2 transfers through electricity trade within China

There is unequal spatial distribution of resource endowment, population density, industrial structure, and economic development with diverse differences in labor, energy, and capital productivities in China. However, previous studies paid little attention to the determinants of CO2 transfers embodied in electricity trade. In this study, we use both the absolute and comparative advantage theories to reveal the determinants of embodied CO2 transfers through electricity trade within China. Results show that China's electricity sector has higher labor productivity but lower asset efficiency and energy productivity than that of mining and manufacturing sectors. The large-scale electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing to the less-developed regions, reduces the unequal spatial distribution of coal and natural gas reserves, and changes CO2 flow embodied in power grid. Econometric analysis shows that coal reserve contributes to the increase of embodied CO2 emission, while natural gas reduces the embodied CO2 emission. The regional differences in the opportunity cost of labor productivity of non-electricity sector are the dominant factor of the embodied CO2 transfers through electricity trade within China, while asset efficiency and energy productivity are not significant in the regressions. Our findings could provide details about China's power grid expansion when confronting climate mitigation in the future.

Measures of spatial heterogeneity in the liver tissue micro-environment as predictive factors for fibrosis score

The organization and interaction between hepatocytes and other hepatic non-parenchymal cells plays a pivotal role in maintaining normal liver function and structure. Although spatial heterogeneity within the tumor micro-environment has been proven to be a fundamental feature in cancer progression, the role of liver tissue topology and micro-environmental factors in the context of liver damage in chronic infection has not been widely studied yet. We obtained images from 110 core needle biopsies from a cohort of chronic hepatitis B patients with different fibrosis stages according to METAVIR score. The tissue sections were immunofluorescently stained and imaged to determine the locations of CD45 positive immune cells and HBsAg-negative and HBsAg-positive hepatocytes within the tissue. We applied several descriptive techniques adopted from ecology, including Getis-Ord, the Shannon Index and the Morisita-Horn Index, to quantify the extent to which immune cells and different types of liver cells co-localize in the tissue biopsies. Additionally, we modeled the spatial distribution of the different cell types using a joint log-Gaussian Cox process and proposed several features to quantify spatial heterogeneity. We then related these measures to the patient fibrosis stage by using a linear discriminant analysis approach. Our analysis revealed that the co-localization of HBsAg-negative hepatocytes with immune cells and the co-localization of HBsAg-positive hepatocytes with immune cells are equally important factors for explaining the METAVIR score in chronic hepatitis B patients. Moreover, we found that if we allow for an error of 1 on the METAVIR score, we are able to reach an accuracy of around 80%. With this study we demonstrate how methods adopted from ecology and applied to the liver tissue micro-environment can be used to quantify heterogeneity and how these approaches can be valuable in biomarker analyses for liver topology.