Framework For Quantitative Analysis Research Articles

Continuous-Time Reinforcement Learning Control: A Review of Theoretical Results, Insights on Performance, and Needs for New Designs.

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of affine nonlinear systems. We review four seminal methods that are the centerpieces of the most recent results on CT-RL control. We survey the theoretical results of the four methods, highlighting their fundamental importance and successes by including discussions on problem formulation, key assumptions, algorithm procedures, and theoretical guarantees. Subsequently, we evaluate the performance of the control designs to provide analyses and insights on the feasibility of these design methods for applications from a control designer's point of view. Through systematic evaluations, we point out when theory diverges from practical controller synthesis. We, furthermore, introduce a new quantitative analytical framework to diagnose the observed discrepancies. Based on the analyses and the insights gained through quantitative evaluations, we point out potential future research directions to unleash the potential of CT-RL control algorithms in addressing the identified challenges.

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Evaluation of secretome biomarkers in glioblastoma cancer stem cells: A bioinformatics analysis.

Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer. This study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis. Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions. Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.

Mechanistic Insights into Plasma Oxidation of Ag Nanofilms: Experimental and Theoretical Studies.

Plasma oxidation of metals has been studied extensively to fabricate nanoporous oxides with the merits of room temperature treatment and facile control of the oxidation rate. Plasma oxidation of Ag, motivated by studies on atomic oxygen corrosion of Ag, is one of the most studied systems. However, several important questions remain unaddressed and even overlooked traditionally: the critical role played by atomic O in promoting oxidation, evolution of microstructures during plasma exposure, and a sound framework for quantitative oxidation kinetic analyses. In this paper, the O2 plasma oxidation behavior of Ag films deposited on Si substrates was systematically studied both experimentally and theoretically. The effects of plasma pressure and power on the microstructural evolution and oxidation kinetics of Ag films of various thicknesses were investigated using comprehensive characterization, as well as numerical analysis of plasma chemistry for deriving atomic O concentration. The findings here provide a full picture and deep mechanistic insights into the morphology and microstructure evolution of Ag films and the growth of dense or porous Ag2O and AgO oxide layers by plasma oxidation, revealing the intricate interplay between atomic O, vacancy creation, Ag ion diffusion, Kirkendall effect, formation of pores, and interfacial void coalescence. The methodology developed here can be easily transferred to help understand the plasma oxidation behavior of other metals.

A quantitative causal analysis and optimization framework for inclusions of steel products

Inclusions generated during steelmaking and continuous casting processes could seriously reduce the mechanical properties of steel products and greatly increase the risk of corrosion failure. Much attention has been paid to perceiving the origin and classification of inclusions based on the process knowledge and mechanism model, while the value of industrial big data is underestimated. Machine learning (ML) methods have demonstrated potential in the processing of nonlinear and strong coupling relationships in industrial data. However, existing ML models are frequently unable to analyze causal relationships between manufacturing process, which is essential for the quality optimization. To address the limitation, this paper proposes a quantitative causal analysis and optimization framework. This framework starts with a feature selection module using propensity score matching (PSM) to measure the quantitative and reliable causal relationship between the process parameters and inclusions. Subsequently, a high-performance AutoML method, AutoGluon-Tabular (AGT), is applied to quantify the optimization range and verify the optimization effect. Specifically, AGT is used to estimate the inclusion level in each window of the process parameter to be optimized, choose the best window of which to determine adjustment strategies, and eventually verify the change of inclusion levels after optimization. The comparative experiments based on the real data collected from a steel company demonstrate that the proposed framework reduces the inclusion rate by 8.85%, which is effective, efficient and has guiding significance for the inclusions control of steel products.

RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences

AbstractAnalyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

Multidimensional evolutionary analysis of China’s BIM technology policy based on quantitative mapping

ABSTRACT In the wave of the digital era, Building Information Modelling (BIM) has emerged as the primary tool for intelligent construction and digital transformation in the Architecture, Engineering, and Construction (AEC) industry. China's focus on policy guidance for the dissemination and application of BIM technology has promoted high-quality development and technological innovation in the AEC industry. However, few studies have described the multi-dimensional evolution picture of BIM technology policy in China, and lack of effective grasp and prediction of policy evolution law and future direction. An improved policy quantitative analysis framework is built using natural language processing (NLP) based on deep learning in this paper. The dynamic characteristics of 242 Chinese BIM technology policy documents from 1998 to 2022 in the PKULAW database are quantitatively analyzed by the framework, and the four-stage integrated evolution of Chinese BIM technology policy is quantitatively mapped in a visual form through network analysis and dynamic topic model (DTM). Furthermore, this research conducts a comprehensive discussion from three dimensions, providing valuable insights for policy makers and industry participants in China. The findings contribute to the deep integration of policy optimization and the construction industry, while also aiming to share China's experience in BIM technology governance with other countries and regions. Ultimately, this study seeks to support the global AEC industry in its digital transformation and development by offering valuable knowledge and perspectives.

Does the sticky relationships of global value chains help stabilize employment? Evidence from China

Global production beyond borders is becoming an invisible force for stabilizing employment, and the behaviour of firms featuring transnational corporations has become critical to the development of production. In this paper, we construct a systematic quantitative analytical framework of measuring global value chain (GVC) sticky relationships and analyze the steady growth of employment from the perspective of inter-firm interactions firstly, furthermore, provide the latest evidence from China. Our findings show that: multiple interactions between firms for that participate GVC contribute to the development of the GVC sticky relationships and promotion of employment growth, the GVC sticky relationship accounts for approximately 17.32 % of the total employment in China, and domestic GVC sticky relationship activities have become an important force in stabilizing and promoting employment growth in China, it surpasses cross-border GVC employment since 2008. Furthermore, we characterize the shape of GVC sticky relationships and find that the circle-structure relationship effect has greater potential to stabilize employment in China, particularly in the sectors of ICT, wholesale and retailor. Therefore, mitigating the transaction costs of participating in GVC by reducing institutional barriers in the process of firms' interactions is significant for maintaining the employment stability.

Coupled mode theory for plasmonic couplers

Photonic integrated circuits play an increasingly important role in several emerging technologies. Their functionality arises from a combination of integrated components, e.g., couplers, splitters, polarization rotators, and wavelength selective filters. Efficient and accurate simulation of these components is crucial for circuit design and optimization. In dielectric systems, design procedures typically rely on coupled-mode theory (CMT) methods, which then guide subsequent refined full-wave calculations. Miniaturization to deep sub-wavelength scales requires the inclusion of lossy plasmonic (metal) components, making optimization more complicated by the interplay between coupling and absorption. Even though CMT is well developed, there is no consensus as to how to rigorously and quantitatively implement it for lossy systems. Here we present an intuitive coupled-mode theory framework for quantitative analysis of dielectric–plasmonic directional and adiabatic couplers, whose large-scale implementation in 3D is prohibitively slow with full-wave methods. This framework relies on adapting existing coupled mode theory approaches by including loss as a perturbation. This approach will be useful in designing dielectric–plasmonic circuits, providing a first reference point for anyone using techniques such as inverse design and deep learning optimization methods.

The selection of reform models for provincial rural credit cooperatives unions in China using an extended CPT-TODIM method based on novel type-2 fuzzy numbers

The selection of a suitable reform model for provincial rural credit cooperatives unions (PRCCUs) is a pivotal aspect of China’s ongoing financial reform endeavors. This involves optimizing internal processes, enhancing efficiency, minimizing operational costs, elevating service quality, strengthening financial innovation capabilities, and promoting the healthy growth of rural financial markets. However, current research on this matter primarily concentrates on qualitative analysis. As of yet, a scientifically grounded decision-making framework from a quantitative standpoint has not been developed. This absence of quantitative analysis results in inaccuracies in research findings, thereby hindering the practical applicability of outcomes. To tackle this formidable challenge, we propose an innovative quantitative analysis framework employing the multi-criteria group decision-making method. This framework, which builds upon the cumulative prospect theory (CPT), the TODIM method, and the most recent advances in type-2 fuzzy numbers (T2FNs), is referred to as the T2FNs-CPT-TODIM. Initially, we define a novel score function, followed by the introduction of a fresh method for computing the distance between the T2FNs. Subsequently, our model pioneers an exclusive weight allocation strategy for decision-makers, grounded in the principles of Dice Similarity. Furthermore, the objective weights of criteria can be computed utilizing the Distance Correlation-based CRITIC approach. We then optimize the rankings by applying the advantage of gains and losses preference in the CPT-TODIM. Ultimately, we apply our proposed model to the practical task of selecting the optimal reform model for a specific PRCCU. Thorough comprehensive sensitivity and comparative analyses, the results validate the theoretical robustness and practical efficacy of our proposed method.

Analysis of Small Traders' Preferences When Deciding to Owe Loans Using Behavioral Planning Theory

Objective: The aim of this research is to analyze the factors that influence small traders' decisions in borrowing money from loan sharks. Method: This research uses Behavioral Planning Theory (TPB) as a quantitative analysis framework. Results and Discussion: The results of data processing show that the financial behavior of small traders is influenced by factors such as ease of loans, disbursement without time constraints, social communication, payment flexibility, and other social dimensions. The research results provide insight into the complex dynamics of small traders' financial behavior and provide a basis for finding relevant and sustainable solutions in increasing fairer financial access at the community level. Research Implications: This research investigates the financial behavior of small traders who interact with moneylenders in the context of communal finance. Amid intense market competition, small traders face financial differentiation, creating complexity in their financial behavior. Limited access to finance and the complex role of moneylenders makes it difficult for small traders to manage debt. Originality/Value: This research can help design better policies to support the economic sustainability of small traders and overcome the financial challenges they face.

Exploratory factor analysis of sustainable craft theory among Finnish craft hobbyists

This study explores how Finnish craft hobbyists view their craft practice in a sustainable craft theory context. We collected the data for this study through an online survey in February and March 2021. We analyse the survey questionnaire data (N = 929) using exploratory factor analysis. The findings of the study show detailed information about craft hobbyists’ perceptions towards sustainable craft and reveal how craft hobbyists find sustainable crafts to be well-being-related activities that enhance meaning-making in their leisure time. This study also provides a sustainable craft measure framework for quantitative examination and analysis. The findings of the study can be utilized to understand sustainable behaviour through hobby crafts in the green transition.

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences

Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

Visualization of translation reorganization upon persistent ribosome collision stress in mammalian cells

Aberrantly slow ribosomes incur collisions, a sentinel of stress that triggers quality control, signaling, and translation attenuation. Although each collision response has been studied in isolation, the net consequences of their collective actions in reshaping translation in cells is poorly understood. Here, we apply cryoelectron tomography to visualize the translation machinery in mammalian cells during persistent collision stress. We find that polysomes are compressed, with up to 30% of ribosomes in helical polysomes or collided disomes, some of which are bound to the stress effector GCN1. The native collision interface extends beyond the invitro-characterized 40S and includes the L1 stalk and eEF2, possibly contributing to translocation inhibition. The accumulation of unresolved tRNA-bound 80S and 60S and aberrant 40S configurations identifies potentially limiting steps in collision responses. Our work provides a global view of the translation machinery in response to persistent collisions and a framework for quantitative analysis of translation dynamics in situ.

Fluidity in Reporting Gender Identity Labels in a Sample of Transgender and Gender Diverse Adolescents and Young Adults, Los Angeles, California, and New Orleans, Louisiana, 2017-2019.

Treating gender identity as a fixed characteristic may contribute to considerable misclassification and hinder accurate characterization of health inequities and the design of effective preventive interventions for transgender and gender diverse (TGD) adolescents and young adults. We examined changes in how an ethnically and racially diverse sample of TGD adolescents and young adults reported their gender identity over time, the implications of this fluidity on public health, and the potential effects of misclassification of gender identity. We recruited 235 TGD adolescents and young adults (aged 15-24 y) in Los Angeles, California, and New Orleans, Louisiana, from May 2017 through August 2019 to participate in an HIV intervention study. We asked participants to self-report their gender identity and sex assigned at birth every 4 months for 24 months. We used a quantitative content analysis framework to catalog changes in responses over time and classified the changes into 3 main patterns: consistent, fluctuating, and moving in 1 direction. We then calculated the distribution of gender identity labels at baseline (initial assessment) and 12 and 24 months and described the overall sample by age, race, ethnicity, and study site. Of 235 TGD participants, 162 (69%) were from Los Angeles, 89 (38%) were Latinx, and 80 (34%) were non-Latinx Black or African American. Changes in self-reported gender identity were common (n = 181; 77%); in fact, 39 (17%) changed gender identities more than twice. More than 50% (n = 131; 56%) showed a fluctuating pattern. Gender identity labels varied over time, suggesting that misclassification may occur if data from a single time point are used to define gender identity. Our study lays the foundation for launching studies to elucidate the associations between shifting gender identities and health outcomes.

DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-Cell Transcriptomics Data

Abstract Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for studying cellular heterogeneity in various fields, particularly in virological research. By studying the viral and cellular transcriptomes, the dynamics of viral infection can be investigated at a single-cell resolution. However, limited studies have been conducted to investigate whether RNA transcripts from clinical samples contain substantial amounts of viral RNAs, and a specific computational framework for efficiently detecting viral reads based on scRNA-seq data has not been developed. Hence, we introduce DVsc, an open-source framework for precise quantitative analysis of viral infection from single-cell transcriptomics data. When applied to approximately 200 diverse clinical samples that were infected by more than 10 different viruses, DVsc demonstrated high accuracy in systematically detecting viral infection across a wide array of cell types. This innovative bioinformatics pipeline could be crucial for addressing the potential effects of surreptitiously invading viruses on certain illnesses, as well as for designing novel medicines to target viruses in specific host cell subsets and evaluating the efficacy of treatment. DVsc supports the FASTQ format as an input and is compatible with multiple single-cell sequencing platforms. Moreover, it could also be applied to sequences from bulk RNA-sequencing data. DVsc is available at http://62.234.32.33:5000/DVsc.

A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network

This study introduces an innovative probabilistic network designed to integrate workflow, operations, and safety barriers involved in operational risk propagation, based on which a generic risk model is proposed to quantify the impact of safety barriers on operational risk. For this purpose, a structural barrier module is defined to describe the interaction mechanisms between operations and risk. This module encompasses scenarios such as risk mitigation based on barriers, risk escalation facilitated due to barrier performance degradation, and natural risk attenuation. The barrier modules are integrated into the probabilistic network, which incorporates both workflow and risk propagation, thereby providing detailed insights into risk propagation under the impact of barriers and operations. To evaluate the impact of barriers, a quantitative analysis framework is presented based on probability theory, including the network Green's function and path search algorithms. Furthermore, a sensitivity analysis is employed based on Green's function to illustrate the optimization of possible intervention strategies. Finally, the impact and sensitivity of the barriers are combined in a two-dimensional space to classify the barriers, leading to recommendations or countermeasures for safety barrier management. The methodologies proposed in this study shed light on the cooperative mechanisms of barriers for operational risk control.

Quantitative Analysis Village Spatial Morphology Using “SPSS + GIS” Approach: A Case Study of Linxia Hui Autonomous Prefecture

This research comprehensively analyzes the spatial morphology of 177 traditional villages within Linxia Hui Autonomous Prefecture, Gansu Province. The study delineates these characteristics utilizing a combination of five quantitative measured indices—ratio, boundary, saturation, building density, and dispersion coefficients. Leveraging sophisticated analytical techniques facilitated by “SPSS + GIS” integration, the investigation systematically explores the intricate details of village spatial form. Their overarching distribution patterns, and the determinant factors influencing them, provide insights across both granular and broad-scale dimensions. The aim is to establish a robust quantitative data analysis framework, facilitating a precise description of traditional villages’ spatial dynamics. The findings categorize the spatial morphology of Linxia’s traditional villages into three distinct types: linear multi-point concentration, dense clustering, and irregular dispersion. Common traits among these categories include widespread dispersal, small settlements, and a mix of dwellings. Spatial distribution patterns vary, with dense clusters forming an “olive-shaped” trend in the southeast–northwest direction, while irregularly dispersed villages develop along mountains and valleys, exhibiting multi-core structures. Additionally, linear multi-point concentrated villages display a random, multi-point distribution interspersed with dense clusters. The survival strategies of these commercial, subsistence, and resource-based villages are shaped by a confluence of factors such as elevation, river proximity, ancient road networks, and the interplay between Han Chinese and Tibetan cultural influences. The implications of this study are significant for understanding traditional village dynamics, promoting sustainable development, and refining quantitative methods for rural studies.