Kernel Density Estimation Method Research Articles

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

Rural Settlement Optimization for Ecologically Sensitive Area Evaluations Based on Geo-Proximity and the Soil‒Water Conservation Capacity

Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.

Geospacialization and statistical analysis of the evolution of cases of sporotrichosis: Timbaúba and Grande Recife - Brasil

With a predominance in countries with tropical and subtropical climates, dermatozoonosis such as sporotrichosis occurs in several nations. Sporotrichosis is caused by species of the genus Sporothrix, whose dissemination between animals and humans is due to the traumatic inoculation of the fungus. The objective of the present study was to propose a management model based on geospatialization, with statistical methods applied to preventive and corrective care in order to control the evolution of cases of sporotrichosis in care at animal health establishments in Timbaúba and Grande Recife/PE - Brazil. Computational tools and statistical techniques were used, verifying the emphasis on places of occurrence of the disease by geospatialization with the Kernel density estimation method and descriptive statistical analysis. The research was approved by the CEP (CAAE: 44330921.2.0000.9547) in the period from 2011 to 2020. The results demonstrated the prevalence of the disease in adult felines and, lack of records in the units. It is concluded that the lack of information and knowledge contributes to dispersion and abandonment of treatment. In this sense, a management plan is proposed with the implementation of a computer program.

Dynamic production capacity assessment of aircraft overhaul shop based on digital twin

Aircraft overhaul is of the highest level in aircraft MRO (Maintenance, Repair, Overhaul), which is an important and necessary activity for ensuring safety and reliability of aircraft throughout its whole lifecycle. Production capacity of aircraft overhaul shop has huge strategic value and determines the cycle time and throughput of the overhauled aircraft. However, due to the inherent complexity and stochasticity, aircraft overhaul tasks are generally executed manually by skilled workers, whose stochastic task execution time (TET) makes it challenging to assess the production capacity of aircraft overhaul shop. Digital twin provides an up-to-date technical measure to simulate, evaluate, and predict the whole lifecycle process of its physical entity, which makes it possible to tackle the complexity and stochasticity of aircraft overhaul process. This paper proposes to apply digital twin to assess the production capacity of aircraft overhaul shop dynamically. Firstly, the aircraft overhaul process and its dynamic production capacity assessment are analyzed. A modeling framework of aircraft overhaul shop digital twin is proposed with a novel twin fusion mechanism. Then, an improved kernel density estimation (KDE) method and a quota hour density proportion (QHDP) method are proposed and verified to process stochastic TET for production capacity assessment. Finally, the digital twin of a typical aircraft avionics repair shop is constructed to assess its production capacity. Four assessment results are compared with real observations from the aircraft avionics repair shop, which verifies the feasibility and advantage of the proposed methodology. A bottleneck analysis is also conducted to optimize the production capacity, which provides managerial insights.

Strength analysis method of CMCs tip shroud structure considering random distribution of preform properties

This paper considers the impact of the random distribution of yarn properties on the mechanical and failure behavior of ceramic matrix composites (CMCs) structures and proposes an integrated analysis method based on preform-structure. Tensile tests of CMCs tip shroud specimens were carried out, and the digital image correlation technique was used to record the deformation of the specimens in real-time during loading. Based on the actual structure, a preform model of the CMCs tip shroud was established, and the kernel density estimation method was used to obtain the distribution of the yarn cross-sectional area. The random distribution of the cross-sectional area is equivalent to material properties, simulating the dispersion of the performance of CMCs. The progressive damage method simulates the failure mode during the specimen's loading process. The peak load error is 1.5 %, and the failure modes such as transverse shear damage, stitching yarn fracture, and interlaminar separation were successfully predicted.

Exploring the Spatiotemporal Dynamics and Simulating Heritage Corridors for Sustainable Development of Industrial Heritage in Foshan City, China

Industrial heritage serves as a testament to the historical and cultural legacy of industrialization, and its preservation and adaptive reuse are crucial for promoting sustainable urban development. This study explores the spatiotemporal dynamics of industrial heritage in Foshan City, China, and simulates potential heritage corridors to inform effective conservation and revitalization strategies. By employing Kernel Density Estimation (KDE) and Standard Deviational Ellipse (SDE) methods, the research investigates the spatial and temporal distribution patterns of industrial heritage across different historical periods and industrial types. An Analytic Hierarchical Process (AHP) is used to construct a hierarchical model of resistance factors, which serves as the basis for simulating potential heritage corridors using the Minimum Cumulative Resistance (MCR) model. The results unveil distinct spatiotemporal distribution patterns, with concentrations of industrial heritage in the central Chancheng District and southeastern Shunde District. Two primary potential heritage corridors are identified, and prioritized strategies for their adaptive reuse are proposed. The findings contribute to a comprehensive understanding of industrial heritage distribution in Foshan City and provide valuable insights for the conservation, planning, and sustainable development of these significant sites. The study highlights the importance of integrating spatiotemporal analysis and heritage corridor modeling in the decision-making process for industrial heritage revitalization, ensuring the preservation of invaluable industrial history and culture while fostering sustainable urban growth.

Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data

BackgroundReal-valued mutual information (MI) has been used in spatial functional network connectivity (FNC) to measure high-order and nonlinear dependence between spatial maps extracted from magnitude-only functional magnetic resonance imaging (fMRI). However, real-valued MI cannot fully capture the group differences in spatial FNC from complex-valued fMRI data with magnitude and phase dependence. MethodsWe propose a complete complex-valued MI method according to the chain rule of MI. We fully exploit the dependence among magnitudes and phases of two complex-valued signals using second and fourth-order joint entropies, and propose to use a Gaussian copula transformation with a lower bound property to avoid inaccurate estimation of joint probability density function when computing the joint entropies. ResultsThe proposed method achieves more accurate MI estimates than the two histogram-based (normal and symbolic approaches) and kernel density estimation methods for simulated signals, and enhances group differences in spatial functional network connectivity for experimental complex-valued fMRI data. Comparison with existing methodsCompared with the simplified complex-valued MI and real-valued MI, the proposed method yields higher MI estimation accuracy, leading to 17.4 % and 145.5 % wider MI ranges, and more significant connectivity differences between healthy controls and schizophrenia patients. A unique connection between executive control network (EC) and right frontal parietal areas, and three additional connections mainly related to EC are detected than the simplified complex-valued MI. ConclusionsWith capability in quantifying MI fully and accurately, the proposed complex-valued MI is promising in providing qualified FNC biomarkers for identifying mental disorders such as schizophrenia.

From super-Earths to sub-Neptunes: Observational constraints and connections to theoretical models

The growing number of well-characterized exoplanets smaller than Neptune enables us to conduct more detailed population studies. We have updated the PlanetS catalog of transiting planets with precise and robust mass and radius measurements and use this comprehensive catalog to explore mass-radius (M-R) diagrams. On the one hand, we propose new M-R relationships to separate exoplanets into three populations: rocky planets, volatile-rich planets, and giant planets. On the other hand, we explore the transition in radius and density between super-Earths and sub-Neptunes around M-dwarfs and compare them with those orbiting K- and FG-dwarfs. Using Kernel density estimation method with a re-sampling technique, we estimated the normalized density and radius distributions, revealing connections between observations and theories on composition, internal structure, formation, and evolution of these exoplanets orbiting different spectral types. First, the substantial 30<!PCT!> increase in the number of well-characterized exoplanets orbiting M-dwarfs compared with previous studies shows us that there is no clear gap in either composition or radius between super-Earths and sub-Neptunes. The "water-worlds" around M-dwarfs cannot correspond to a distinct population, their bulk density and equilibrium temperature can be interpreted by several different internal structures and compositions. The continuity in the fraction of volatiles in these planets suggests a formation scenario involving planetesimal or hybrid pebble-planetesimal accretion. Moreover, we find that the transition between super-Earths and sub-Neptunes appears to happen at different masses (and radii) depending on the spectral type of the star. The maximum mass of super-Earths seems to be close to 10 M$_ for all spectral types, but the minimum mass of sub-Neptunes increases with the star's mass, and is around 1.9 M$_ 3.4 M$_ and 4.3 M$_ for M-dwarfs, K-dwarfs, and FG-dwarfs, respectively. The precise value of this minimum mass may be affected by observational bias, but the trend appears to be reliable. This effect, attributed to planet migration, also contributes to the fading of the radius valley for M-planets compared to FGK-planets. While sub-Neptunes are less common around M-dwarfs, smaller ones (1.8 R$_ < R$_p$ < 2.8 R$_ exhibit lower density than their equivalents around FGK-dwarfs. Nonetheless, the sample of well-characterized small exoplanets remains limited, and each new discovery has the potential to reshape our understanding and interpretations of this population in the context of internal structure, composition, formation, and evolution models. Broader consensus is also needed for internal structure models and atmospheric compositions to enhance density interpretation and observable predictions for the atmospheres of these exoplanets.

From global to national: The role of urban agglomerations in China's new development paradigm.

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes.

Exploring Differences and Evolution of Coordination Level of the Industrial Structure, Economy and Ecological Environment Complex System in Beijing–Tianjin–Hebei Urban Agglomeration

Industrial structure (IS), economy (EC), and ecological environment (EE) could influence each other and constitute a complex system (IS-EC-EE). This study is to explore the differences and evolution of the coordination level of the IS-EC-EE complex system of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration by coupling coordination degree model, Markov chain, GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method to provide a reference for regional sustainable development. The results show that the comprehensive evaluation index of IS, EC, and EE had significant differences among the 13 cities in the BTH region. The IS-EC-EE system of the whole BTH region was in a highly coupled and low-coordinated development state. And the coordination degrees of the 13 cities in the BTH region had spatial differences, which were mainly due to inter-regional differences, and the differences became larger. Furthermore, the coordinated development of the 13 cities had the probability of keeping high, moderate, and low coordination. It was predicted that the coordination degree of the IS-EC-EE system in the whole BTH region in 2020–2030 was roughly similar to the trend in 2009–2019. The coordination levels of Beijing and Tianjin were higher than in Hebei Province, so optimizations need to be considered for regional overall sustainable development.

Research on Territorial Spatial Development Non-Equilibrium and Temporal–Spatial Patterns from a Conjugate Perspective: Evidence from Chinese Provincial Panel Data

Clarifying the intrinsic nature and formation mechanisms of the territorial spatial development non-equilibrium, optimizing the allocation of territorial resources, promoting regional balanced development, and alleviating regional development disparities have become common endeavors of all countries seeking to enhance development quality. This study, based on the land use and socio-economic data of 31 provinces and cities in China from 2006 to 2020, utilized the kernel density estimation method and ArcGIS spatial analysis to explore the spatiotemporal evolution characteristics of China’s territorial spatial development non-equilibrium. The research findings are as follows: (1) From 2006 to 2020, both the land development intensity and land supply capacity showed an increasing trend, with increases of 21.4% and 8.03%, respectively. However, their spatiotemporal evolutions significantly differed. (2) The state of the territorial spatial development non-equilibrium in China significantly improved, with a rising trend in the territorial spatial equilibrium degree. Regional disparities in the territorial spatial equilibrium degree decreased. The number of provinces and cities in a state of underdevelopment decreased from 23 to 8, while the number in an equilibrium state increased from 7 to 21. The central and western regions showed more significant improvements in the territorial spatial equilibrium degree and a greater reduction in regional disparity when compared to the eastern and northeastern regions. (3) China exhibited distinct regional disparities in the territorial spatial equilibrium degree. The overdeveloped areas were Beijing and Shanghai. The underdeveloped regions were primarily concentrated in western areas, including Sichuan, Yunnan, and Xinjiang. The high-level equilibrium regions were predominantly located in the southeastern coastal provinces and the central plains region, while the low-level equilibrium regions included Inner Mongolia, Ningxia, Qinghai, and the three northeastern provinces. (4) From the perspective of the number of agglomeration types, the order is HH (high–high) > LH (low–high) > LL (low–low) > HL (high–low), and China’s TSED exhibits a spatial proximity peer effect. The innovation of this study consists of the following aspects: (1) The clarification of the fact that the essence of equilibrium does not require an exact 1:1 match, as with “two horses”; rather, it entails the maintenance of a slight surplus of land supply capacity over land development intensity. This approach allows the possibility of future sustainable development by leaving room for expansion. (2) The differentiation between high-intensity conjugation (in terms of both quantity and quality) and low-intensity conjugation (quantity only) from a conjugate perspective. This categorization aids in a more comprehensive and in-depth understanding of the territorial spatial development non-equilibrium and its spatiotemporal patterns. It provides crucial information for the formulation of sustainable national territorial development plans and the promotion of a regional development equilibrium.

A data-driven robust optimization method based on scenario clustering for PVC production scheduling under uncertainty

In this paper, a data-driven robust optimization method based on scenario clustering is proposed for addressing energy consumption uncertainty characterized by correlation and multi-peaked distribution within the PVC production process. Firstly, principal component analysis (PCA) and kernel density estimation (KDE) methods are used to capture the correlation and distribution information effectively across multidimensional uncertain parameters; then a modified K-means clustering method based on density peaks is applied to cluster energy consumption scenarios and the flexible uncertainty subsets is established. A two-stage robust optimization model for the vinyl chloride production section is then proposed, and the column and constraint generation algorithm is applied to solved. Finally, the effectiveness of the proposed method is validated through a PVC production case study. Comparative results demonstrate that the proposed model reduces energy consumption under uncertainty and improves the robustness of PVC production scheduling.

Optimizing asphalt mix design through predicting volumetric properties using interpretable machine learning

Traditional asphalt mix design methods primarily rely on experience, consuming immense time and resources. Currently, advanced machine learning (ML) method can accelerate the asphalt mix design procedure. However, historical data on asphalt mix design exhibit high variability, and the outputs of ML models are often challenging to interpret. To address these issues, this study selectively utilized outlier detection algorithms for data preprocessing and employed interpretable ML to predict and explain the volumetric properties of the mixtures, achieving an automated and rapid mix design method. A database (1525 sets of data) of volumetric properties was established based on experimental data of the engineering site. Z-score, KDE (kernel density estimation) and LOF (local outlier factor) methods were employed to detect outliers of the database. Three ML models were trained to predict the volumetric properties of mixture (air void and voids in mineral aggregate), based on material proportion, gradation morphological and material performance features. The SHAP method was used to interpret the extreme gradient boosting (XGB) model, quantifying feature importance, and aiding in decision-making. Finally, an asphalt mix design framework was proposed. The results indicate that the combination of KDE and LOF methods for outlier detection enabled the machine learning model to achieve optimal performance. The XGB model had the best prediction effect on volumetric properties. According to the SHAP method results, the influence of the critical features on the results was explained by the physical laws. The method proposed effectively reducing the design time and costs. The feasibility of the proposed design method was proved by the case study.

Spatial and Temporal Distribution and Flow Characteristics of High-Quality Development of Modern Service Industry in Chinese Sub-provincial Cities

Based on the new development concept of the service industry, this paper constructs a comprehensive evaluation index system for the high-quality development of modern service industry from a total of 22 indicators in four dimensions, namely, development efficiency, structural optimization, innovation agglomeration and industrial scale. The composite index of 15 sub-provincial cities in China from 2013 to 2022 was measured by entropy weight method, describing the current development status and development differences of each city, and with the help of Kernel density estimation method and Dagum Gini coefficient method, the law of inter-city evolution was inscribed, which revealed the spatial differences of the four major geographic regions and their sources and contributions. The study shows that the overall high-quality development of the service industry in the 15 cities is at a relatively low level, with obvious differences, but all of them are on a slowly rising trend. According to the division of the four major geographic regions, the northeast region has the largest intra-regional differences, and the eastern region has the smallest intra-regional differences. Meanwhile, the spatial imbalance between regions is still aggravating, and there is a clear differentiation between the development level of the eastern and western regions.

International comparison of China's digitalization level and its enlightenment.

The global digital wave has flourished in recent years, and the digital technology revolution has emerged. Digitalization plays an undeniable role in promoting modern economic and social development in multiple aspects such as economy, society, innovation, public services and sustainable development, China's digitalization application is also developing rapidly. In order to better measure the current situation of China's digitalization development level, this paper constructs a comprehensive evaluation index system of digitalization development from four dimensions of talents in the digital field, digital infrastructure construction, digitalization innovation ability and international competitiveness, and tests the index system. The entropy method is used to measure the level of digitalization development between China and the United States, the United Kingdom, France and other major developed countries in the world, and the coefficient of variation method, kernel density estimation and Dagum Gini coefficient method are used to analyze the temporal and spatial characteristics and regional differences of digitalization development level of seven countries. This paper makes a comparative analysis between China and major developed countries from the historical perspective of the evolution of the techno-economic paradigm. With a view to summarizing and exploring from it and drawing on the advanced experiences accumulated by the developed countries over a long period of time, so as to provide China with useful insights and bases for further enhancing its digitalization development level.

Spatial Distribution Characteristics and Influencing Factors of Geographical Indication Agricultural Products in Shaanxi Province

Geographical indication agricultural products are of great significance to promote the high-quality development of rural revitalization due to their unique quality and brand value. Based on the geographical concentration index, the nearest neighbor index and the kernel density estimation method, this paper empirically studies the spatial distribution characteristics and influencing factors of 117 geographical indication agricultural products in Shaanxi Province. The results show that the spatial distribution of geographical indications in Shaanxi is extremely uneven, showing the characteristics of ' dense in the south and sparse in the north, regional agglomeration '. From the perspective of density distribution characteristics, the overall spatial distribution of geographical indications in Shaanxi shows a distribution pattern of ' three high and three times ', and the three high-density areas show a agglomeration distribution pattern of ' pin-shaped '. The difference between the north and the south is obvious, and the overall distribution pattern is ' cold in the north and hot in the south '. The spatial distribution pattern of geographical indications in Shaanxi is affected by many factors. Natural factors are the basis, social and economic factors strengthen the spatial distribution pattern of geographical indications, and government-led and policy factors play a regulatory and guiding role. It is suggested to optimize the spatial distribution pattern of geographical indications in Shaanxi, rely on local social resources, give full play to the leading role of the government, and actively promote the industrialization development of geographical indication products.

Reliability analysis of steel truss bars with imprecise statistical data

The article describes the probabilistic approach of reliability analysis for steel truss bars with imprecise statistical data. An approach to recovery the probability density of a random variable by the kernel density estimation method is illustrated for a small number of tests/measurements cases. The estimation of the distribution density parameters is presented in an interval form, which makes it possible to form a p-box for the random variable. Evaluation of the probability of non-failure allow to get a quantitative assessment for the safety of steel trusses, to identify the most unreliable trusses for monitoring their technical condition or to calculate the effect of strengthening the truss according to the reliability criterion. The probability of non-failure is estimated in the interval form. If the lower limit of the interval does not allow to get a conclusion about the truss safety level, then it can be increased by obtaining additional statistical data or by strengthening the truss or reducing the permissible load.

Robust maximum fairness consensus models with aggregation operator based on data-driven method

In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model.

Identifying impervious surface hot spots in Ho Chi Minh City using object-based classification and spatial statistics

Impervious surface is often a primary indicator for urbanization. The rapid expansion of impervious surface area (ISA) can lead to a variety of environmental issues such as the change in local climate, urban heat islands, urban flooding, biodiversity loss, and the depletion of air quality. In this paper, satellite data from Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 were employed to extract impervious areas in Ho Chi Minh City (HCMC) from 2009 to 2022 by integrating the object-based image analysis and Kernel density estimation method. Regarding spatial analysis, optimized hot spot analysis (OHSA) and emerging hot spot analysis (EHSA) were used to detect specific spatial distribution and spatio-temporal changes of ISA hot spots over the 12 years. Study results show the hot spots of ISA with 99% confidence interval increased considerably from 28,625 ha in 2009 to 44,400 ha in 2022. Through emerging hot spot analysis, many kinds of ISA hot spots including persistent, new, intensifying, sporadic, and potential hot spots were traced over time. New hot spots had a total area of 14,975 ha, located mainly in Cu Chi, Thu Duc city, whereas potential hot spots, which are likely to become new hot spots in the future, were over 28,000 ha, making up 19% of the study area. Thus, the results from this study can provide a preliminary prediction of ISA distribution for HCMC in the coming years.

Prediction of surface urban heat island in Ho Chi Minh City, Viet Nam using remote sensing and logistic regression model

Urban heat island is a concerning environmental issue in major cities, particularly in Ho Chi Minh City. With a high rate of urbanization, the city’s extensive impervious surface area contributes to land surface temperature increase, leading to the expansion of the urban heat island phenomenon. This study was conducted in the Ho Chi Minh city region from 2009 to 2020, images from Landsat 5 (TM) and Landsat 8 (OLI/TIRS) satellites were employed to estimate land surface temperature (LST), examine the spatial distribution of the urban heat island (SUHI), and analyze changes in impervious surface area (ISA). A logistic regression model was established to predict the relationship between SUHI and ISA, with the percentage of ISA calculated by the Kernel Density Estimation method (KDE). The results revealed that the impervious surface area in the city doubled from 2009 to 2020, accounting for 29.54% of the city’s area, which correlated with a significant expansion of SUHI from 37,000 ha to 53,000 ha. The SUHI predicting model exhibited good discrimination ability (AUC = 0.98) and total accuracy of 96% through Hosmer & Lemeshow Test in R. The probability of the predicted SUHI showed a high correlation (R2 = 0.885) with the actual SUHI. In areas where ISA increased from 60% to 80%, the probability of SUHI occurrence also increased from 52.85% to 98%. This study presented a novel approach, utilizing statistical modeling to accurately quantify SUHI based on changes in impervious surface density. This method can contribute to an effective evaluation of heat-related health risks, provide the government and urban planners with a precise orientation in management toward sustainable development.