Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Verification and Validation of High-Resolution Inviscid and Viscous Conical Nozzle Flows

Capturing elaborated flow structures and phenomena is required for well-solved numerical flows.The finite difference methods allow simple discretization of mesh and model equations.However,they need simpler meshes,e.g.,rectangular.The inverse Lax-Wendroff(ILW)procedure can handle complex geometries for rectangular meshes.High-resolution and high-order methods can capture elaborated flow structures and phenomena.They also have strong mathematical and physical backgrounds,such as positivity-preserving,jump conditions,and wave propagation concepts.We perceive an effort toward direct numerical simulation,for instance,regarding weighted essentially non-oscillatory(WENO)schemes.Thus,we propose to solve a challenging engineering application without turbulence models.We aim to verify and validate recent high-resolution and high-order methods.To check the solver accuracy,we solved vortex and Couette flows.Then,we solved inviscid and viscous nozzle flows for a conical profile.We employed the finite difference method,positivity-preserving Lax-Friedrichs splitting,high-resolution viscous terms discretization,fifth-order multi-resolution WENO,ILW,and third-order strong stability preserving Runge-Kutta.We showed the solver is high-order and captured elaborated flow structures and phenomena.One can see oblique shocks in both nozzle flows.In the viscous flow,we also captured a free-shock separation,recirculation,entrainment region,Mach disk,and the diamond-shaped pattern of nozzle flows.

Accurate Source-Receiver Positioning Method for a High-Resolution Deep-Towed Multichannel Seismic Exploration System

The near-seabed multichannel seismic exploration systems have yielded remarkable successes in marine geological disaster assessment,marine gas hydrate investigation,and deep-sea mineral exploration owing to their high vertical and horizontal resolution.However,the quality of deep-towed seismic imaging hinges on accurate source-receiver positioning information.In light of existing technical problems,we propose a novel array geometry inversion method tailored for high-resolution deep-towed multichannel seismic exploration systems.This method is independent of the attitude and depth sensors along a deep-towed seismic streamer,accounting for variations in seawater velocity and seabed slope angle.Our approach decomposes the towed line array into multiline segments and characterizes its geometric shape using the line segment distance and pitch angle.Introducing optimization parameters for seawater velocity and seabed slope angle,we establish an objective function based on the model,yielding results that align with objective reality.Employing the particle swarm optimization algorithm enables synchronous acquisition of optimized inversion results for array geometry and seawater velocity.Experimental validation using theoretical models and practical data verifies that our approach effectively enhances source and receiver positioning inversion accuracy.The algorithm exhibits robust stability and reliability,addressing uncertainties in seismic traveltime picking and complex seabed topography conditions.

Investigating the relationship between intracranial atherosclerotic plaque remodelling and diabetes using high-resolution vessel wall imaging

BACKGROUND Intracranial atherosclerosis,a leading cause of stroke,involves arterial plaque formation.This study explores the link between plaque remodelling patterns and diabetes using high-resolution vessel wall imaging(HR-VWI).AIM To investigate the factors of intracranial atherosclerotic remodelling patterns and the relationship between intracranial atherosclerotic remodelling and diabetes mellitus using HR-VWI.METHODS Ninety-four patients diagnosed with middle cerebral artery or basilar artery INTRODUCTION Intracranial atherosclerotic disease is one of the main causes of ischaemic stroke in the world,accounting for approx-imately 10%of transient ischaemic attacks and 30%-50%of ischaemic strokes[1].It is the most common factor among Asian people[2].The adaptive changes in the structure and function of blood vessels that can adapt to changes in the internal and external environment are called vascular remodelling,which is a common and important pathological mechanism in atherosclerotic diseases,and the remodelling mode of atherosclerotic plaques is closely related to the occurrence of stroke.Positive remodelling(PR)is an outwards compensatory remodelling where the arterial wall grows outwards in an attempt to maintain a constant lumen diameter.For a long time,it was believed that the degree of stenosis can accurately reflect the risk of ischaemic stroke[3-5].Previous studies have revealed that lesions without significant luminal stenosis can also lead to acute events[6,7],as summarized in a recent meta-analysis study in which approximately 50%of acute/subacute ischaemic events were due to this type of lesion[6].Research[8,9]has pointed out that the PR of plaques is more dangerous and more likely to cause acute ischaemic stroke.Previous studies[10-13]have found that there are specific vascular remodelling phenomena in the coronary and carotid arteries of diabetic patients.However,due to the deep location and small lumen of intracranial arteries and limitations of imaging techniques,the relationship between intracranial arterial remodelling and diabetes is still unclear.In recent years,with the development of magnetic resonance technology and the emergence of high-resolution(HR)vascular wall imaging,a clear and multidimensional display of the intracranial vascular wall has been achieved.Therefore,in this study,HR wall imaging(HR-VWI)was used to display the remodelling characteristics of bilateral middle cerebral arteries and basilar arteries and to explore the factors of intracranial vascular remodelling and its relationship with diabetes.

In vivo pilot study into superficial microcirculatory characteristics of colorectal adenomas using novel high-resolution magnifying endoscopy with blue laser imaging

BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a novel high-resolution magnification endoscopy with blue laser imaging(BLI),thus providing a new insight into the microcirculation of early colon tumors.AIM To observe the superficial microcirculation of colorectal adenomas using the novel magnifying colonoscope with BLI and quantitatively analyzed the changes in hemodynamic parameters.METHODS From October 2019 to January 2020,11 patients were screened for colon adenomas with the novel high-resolution magnification endoscope with BLI.Video images were recorded and processed with Adobe Premiere,Adobe Photoshop and Image-pro Plus software.Four microcirculation parameters:Microcirculation vessel density(MVD),mean vessel width(MVW)with width standard deviation(WSD),and blood flow velocity(BFV),were calculated for adenomas and the surrounding normal mucosa.RESULTS A total of 16 adenomas were identified.Compared with the normal surrounding mucosa,the superficial vessel density in the adenomas was decreased(MVD:0.95±0.18 vs 1.17±0.28μm/μm2,P<0.05).MVW(5.11±1.19 vs 4.16±0.76μm,P<0.05)and WSD(11.94±3.44 vs 9.04±3.74,P<0.05)were both increased.BFV slowed in the adenomas(709.74±213.28 vs 1256.51±383.31μm/s,P<0.05).CONCLUSION The novel high-resolution magnification endoscope with BLI can be used for in vivo study of adenoma superficial microcirculation.Superficial vessel density was decreased,more irregular,with slower blood flow.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

Scale variation is amajor challenge inmulti-person pose estimation.In scenes where persons are present at various distances,models tend to perform better on larger-scale persons,while the performance for smaller-scale persons often falls short of expectations.Therefore,effectively balancing the persons of different scales poses a significant challenge.So this paper proposes a newmulti-person pose estimation model called FSANet to improve themodel’s performance in complex scenes.Our model utilizes High-Resolution Network(HRNet)as the backbone and feeds the outputs of the last stage’s four branches into the DCB module.The dilated convolution-based(DCB)module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch.Subsequently,the attention operation-based(AOB)module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of noise.Finally,predictions are made using the heatmap representation.The model can recognize images with diverse scales and more complex semantic information.Experimental results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets,validating the effectiveness of our proposed approach.

High-Resolution Mass Spectroscopy for Revealing the Charge Storage Mechanism in Batteries: Oxamide Materials as an Example

The pursuit of high-performance electrode materials is highly desired to meet the demand of batteries with high energy and power density.However,a deep understanding of the charge storage mechanism is always challenging,which limits the development of advanced electrode materials.Herein,high-resolution mass spectroscopy(HR-MS)is employed to detect the evolution of organic electrode materials during the redox process and reveal the charge storage mechanism,by using small molecular oxamides as an example,which have ortho-carbonyls and are therefore potential electrochemical active materials for batteries.The HR-MS results adequately proved that the oxamides could reversibly store lithium ions in the voltage window of 1.5–3.8 V.Upon deeper reduction,the oxamides would decompose due to the cleavage of the C–N bonds in oxamide structures,which could be proved by the fragments detected by HR-MS,^(1)H NMR,and the generation of NH_(3)after the reduction of oxamide by Li.This work provides a strategy to deeply understand the charge storage mechanism of organic electrode materials and will stimulate the further development of characterization techniques to reveal the charge storage mechanism for developing high-performance electrode materials.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

A systematic printability study of direct ink writing towards high-resolution rapid manufacturing

Direct ink writing(DIW)holds enormous potential in fabricating multiscale and multi-functional architectures by virtue of its wide range of printable materials,simple operation,and ease of rapid prototyping.Although it is well known that ink rheology and processing parameters have a direct impact on the resolution and shape of the printed objects,the underlying mechanisms of these key factors on the printability and quality of DIW technique remain poorly understood.To tackle this issue,we systematically analyzed the printability and quality through extrusion mechanism modeling and experimental validating.Hybrid non-Newtonian fluid inks were first prepared,and their rheological properties were measured.Then,finite element analysis of the whole DIW process was conducted to reveal the flow dynamics of these inks.The obtained optimal process parameters(ink rheology,applied pressure,printing speed,etc)were also validated by experiments where high-resolution(<100μm)patterns were fabricated rapidly(>70 mm s^(-1)).Finally,as a process research demonstration,we printed a series of microstructures and circuit systems with hybrid inks and silver inks,showing the suitability of the printable process parameters.This study provides a strong quantitative illustration of the use of DIW for the high-speed preparation of high-resolution,high-precision samples.

On High-Resolution Entropy-Consistent Flux with Slope Limiter for Hyperbolic Conservation Laws

This paper proposes a new version of the high-resolution entropy-consistent(EC-Limited)flux for hyperbolic conservation laws based on a new minmod-type slope limiter.Firstly,we identify the numerical entropy production,a third-order differential term deduced from the previous work of Ismail and Roe[11].The corresponding dissipation term is added to the original Roe flux to achieve entropy consistency.The new,resultant entropy-consistent(EC)flux has a general and explicit analytical form without any corrective factor,making it easy to compute and a less-expensive method.The inequality constraints are imposed on the standard piece-wise quadratic reconstruction to enforce the pointwise values of bounded-type numerical solutions.We design the new minmod slope limiter as combining two separate limiters for left and right states.We propose the EC-Limited flux by adding this reconstruction data method to the primitive variables rather than to the conservative variables of the EC flux to preserve the equilibrium of the primitive variables.These resulting fluxes are easily applied to general hyperbolic conservation laws while having attractive features:entropy-stable,robust,and non-oscillatory.To illustrate the potential of these proposed fluxes,we show the applications to the Burgers equation and the Euler equations.

Evaluation of COVID-19 Cases and Vaccinations in the State of Georgia, United States: A Spatial Perspective

This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in the state of Georgia. Data on COVID-19 was acquired from usafact.org while the vaccination records were obtained from COVID-19 vaccination tracker. The spatial patterns across the counties were analyzed using spatial statistical techniques which include both global and local spatial autocorrelation. The study further evaluates the effect of vaccination and selected socio-economic predictors on COVID-19 cases across the study area. The result of hotspot analysis reveals that the epicenters of COVID-19 are distributed across Cobb, Fulton, Gwinnett, and DeKalb counties. It was also affirmed that the vaccination records followed the same pattern as COVID-19 cases’ epicenters. The result of the spatial error model performed well and accounted for a considerable percentage of the regression with an adjusted R squared of 0.68, Akaike Information Criterion (AIC) 387.682 and Breusch-Pagan of 9.8091. ESDA was employed to select the main explanatory variables. The selected variables include vaccination, population density, percentage of people that do not have health insurance, black race, Hispanic and these variables accounted for 68% of the number of COVID-19 cases in the state of Georgia during the study period. The study concludes that both COVID-19 cases and vaccinated individuals have spatial peculiarities across counties in Georgia state. Lastly, socio-economic variables and vaccination are very important to reduce the vulnerability of individuals to COVID-19 disease.

Spatial evolution and spatial production of traditional villages from "backward poverty villages" to "ecologically well-off villages": Experiences from the hinterland of national nature reserves in China

With the rapid urbanization process,the space of traditional villages in China is undergoing significant changes.Studying the spatial evolution of traditional villages is significant in promoting rural spatial transformation and realizing rural revitalization and sustainable rural development.Based on the traceability analysis of spatial production theory,this paper constructed an analytical framework for the spatial production evolution of traditional villages,analyzed the spatial evolution process and characteristics of traditional villages by using buffer analysis,spatial syntax,and other research methods,and revealed the characteristics of the spatial production evolution of traditional villages and the driving mechanism.The results show that:(1)The village spatial formation and development follow the village life cycle theory and usually develop from embryonic villages to diversified and integrated villages;(2)The evolution of village spatial production is characterized by the diversity of material space,the sublimation of daily life space,and the integration of social system space and generalization of emotional space;(3)The evolution of village spatial production from backward and poor village to ecologically well-off village is influenced by a combination of factors;(4)The village has formed a spatial structure of"people-land-scape-culture-industry",realized comprehensive reconstruction and spatial reproduction.The study results reflect the spatial evolution characteristics of traditional villages in mountainous areas in a more comprehensive way,which helps to promote the protection and development of traditional villages in mountainous areas and,to a certain extent,provides a reference for the development of rural revitalization.

Spatial transcriptomics:recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field.Although bulk RNA sequencing and single-cell RNA sequencing(scRNA-seq)have provided insights into cell types and heterogeneity in the respiratory system,the relevant specific spatial localization and cellular interactions have not been clearly elucidated.Spatial transcriptomics(ST)has filled this gap and has been widely used in respiratory studies.This review focuses on the latest iterative technology of ST in recent years,summarizing how ST can be applied to the physiological and pathological processes of the respiratory system,with emphasis on the lungs.Finally,the current challenges and potential development directions are proposed,including high-throughput full-length transcriptome,integration of multi-omics,temporal and spatial omics,bioinformatics analysis,etc.These viewpoints are expected to advance the study of systematic mechanisms,including respiratory studies.

Assessing the Spatial Equality of COVID Testing Sites Maintaining Zero COVID Policy

Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

High-resolution simulation of upper-ocean submesoscale variability in the South China Sea:Spatial and seasonal dynamical regimes

Submesoscale processes in marginal seas usually have complex generating mechanisms,highly dependent on the local background flow and forcing.This numerical study investigates the spatial and seasonal differences of submesoscale activities in the upper ocean of the South China Sea(SCS)and the different dynamical regimes for sub-regions.The spatial and seasonal variations of vertical vorticity,horizontal convergence,lateral buoyancy gradient,and strain rate are analyzed to compare the submesoscale phenomenon within four sub-regions,the northern region near the Luzon Strait(R1),the middle ocean basin(R2),the western SCS(R3),and the southern SCS(R4).The results suggest that the SCS submesoscale processes are highly heterogeneous in space,with different seasonalities in each sub-region.The submesoscale activities in the northern sub-regions(R1,R2)are active in winter but weak in summer,while there appears an almost seasonal anti-phase in the western region(R3)compared to R1 and R2.Interestingly,no clear seasonality of submesoscale features is shown in the southern region(R4).Further analysis of Ertel potential vorticity reveals different generating mechanisms of submesoscale processes in different sub-regions.Correlation analyses also show the vertical extent of vertical velocity and the role of monsoon in generating submesoscale activities in the upper ocean of sub-regions.All these results suggest that the sub-regions have different regimes for submesoscale processes,e.g.,Kuroshio intrusion(R1),monsoon modulation(R2),frontal effects(R3),topography wakes(R4).

Analysis of spatial distribution characteristics and driving factors of ethnicminority villages in China using geospatial technology and statistical models

This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China.They are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization strategies.In this study,1652 EMV in China were selected as the research subjects.The Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages.Neural network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV.The results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest influence.As EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era.