Spatial Regression Models Research Articles

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models. This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data. From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases. The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

Comparing Spatial Ecological Regression Models in Breast Cancer Incidence in Iran

Comparing Spatial Ecological Regression Models in Breast Cancer Incidence in Iran

Spatial Analysis of Risk Factors for Tuberculosis Incidence in South Jakarta City in 2022

Introduction: South Jakarta City, within DKI Jakarta, bears a substantial burden of TB cases, yet case detection rates and treatment success remain low. Factors such as population density, gender disparity, socio-economic conditions, and healthcare accessibility influence TB transmission. The city's high population density presents challenges in controlling TB spread. Additionally, males and low-income families face higher TB risks. Objective: This study aims to analyze TB case distribution across 10 districts in South Jakarta, assess TB case clustering, and understand the spatial regression model of TB risk factors. Method: The methodology of this research utilizes a quantitative approach with an ecological study design and spatial methodology, secondary data from various sources, including the national TB control reporting application. Data processing involves GeoDa v1.22, QGIS v.3.32.3, SPSS v.22, and GWR v4.0 applications for spatial analysis. Result: Findings reveal that spatial autocorrelation tests using Moran's Index on TB cases in South Jakarta City are not statistically significant, indicating no spatial autocorrelation. The LISA test identifies Mampang Prapatan District as a cold spot in Quadrant III (Low-Low). GWR regression analysis highlights three spatially influential risk factor variables affecting TB cases: gender ratio, number of health centers, and population density. The first two variables affect all districts in South Jakarta City, whereas population density only impacts the Setiabudi District. Conclusion: In conclusion, Mampang Prapatan district in South Jakarta City exhibits low TB transmission risk, considering population density and other factors. Notably, three spatial risk factors affect TB transmission in South Jakarta City, warranting attention from the health department in TB elimination efforts.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Neighborhood-level socioeconomic disparities in Radon testing in North Carolina from 2010 to 2020

Radon is a naturally occurring radioactive gas that poses significant health risks to humans, including increased risk of lung cancer. This study investigates the association of neighborhood-level socioeconomic variables with radon testing and radon exposure levels in North Carolina between 2010 and 2020. Our analysis of the two largest commercial household radon tests reveals that 67% of census tracts had testing rates below 10 tests per 1000 population, indicating low testing prevalence. Low radon levels (<2 pCi/L) were detected in 74.1% of the tracts (n = 1626), while medium levels of 2–4 pCi/L and ≥4 pCi/L were observed in 17.2% (n = 378) and 1.6% (n = 36) of the tracts. A generalized spatial regression model was employed to analyze the association between neighborhood-level socioeconomic variables and radon testing rates (per 1000 households), controlling for median radon testing results. The results show a positive correlation (P-value <0.001) of testing rate with various indicators of neighborhood affluence including education level, income, and occupation. In contrast, neighborhood disadvantage, including poverty, unemployment, and public assistance, was associated with a lower radon-testing rate (P-value <0.001). These findings highlight the need for targeted interventions to address socioeconomic disparities in radon testing and promote awareness and access to testing resources in lower socio-economic neighborhoods. Improving testing rates can effectively address radon-related health risks in North Carolina and across the U.S.

Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study.

Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services. This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India. We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators. Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions. Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

A district-level geospatial analysis of the availability of improved water and sanitation among tribal households in India

ABSTRACT Despite progress in Sustainable Development Goal 6, which envisages clean water and sanitation for all, enormous disparities in access to water and sanitation services between and within countries continue to be a significant challenge. Evidence on the spatial heterogeneity of water, sanitation, and hygiene practices among the Scheduled Tribe (ST) population is limited in India. This study estimates the spatial heterogeneity in ST people's access to improved water and sanitation facilities and its correlates at the district level. Geospatial techniques like Moran's I, univariate and bivariate local indicator of spatial association, and spatial regression models were carried out for 707 districts covered in the fifth round of the National Family Health Survey, 2019–21. Stata was used for descriptive analysis, and ArcMap and GeoDA were used for spatial analysis. Only about half of ST households had access to improved water and sanitation facilities in India. Spatial heterogeneity across districts was evident, with 130 districts from Uttarakhand, Himachal Pradesh, Punjab, Haryana, Delhi, Sikkim, Arunachal Pradesh, Nagaland, Mizoram, and some portion of Karnataka forming the hot spots. Gender and age of the household head, family type, and region were significantly associated with improved water and sanitation facilities among ST households.

Spatio-temporal distributions and determinants of diarrhea among under-five children in Ethiopia.

The purpose of this study was to evaluate the spatio-temporal pattern of Ethiopia's childhood diarrheal disease and identify its contributing factors. We conducted analyses on secondary data from four Ethiopian Demographic and Health Surveys conducted between 2000 and 2016. Moran's I was used to determine spatial dependence and spatial models were used to evaluate variables associated with diarrhea in under-five children at the zonal level. Childhood diarrhea showed spatial clustering in Ethiopia (Moran's I; p < 0.05). The spatial regression model revealed significant factors at the zonal level: children born at home ( = 1.355, 95% CI: 1.052-1.544, p < 0.001), low birth weight ( = 1.18, 95% CI: 1.017-1.691, p < 0.05), and unimproved source of water ( = 0.8568, 95% CI: 0.671-1.086, p < 0.01). The prevalence of diarrhea among under-five children varied over time by zone, with the Assosa, Hundene, and Dire Diwa zones having the highest rates. Home births and low birth weight contributed to the prevalence of childhood diarrhea. In high-risk zones of Ethiopia, reducing childhood diarrhea requires integrated child health interventions and raising awareness about the potential hazards associated with unimproved water sources.

Public attitudes toward medical waste: Experiences from 141 countries.

Medical Waste (MW), conceptualized as waste generated in the diagnosis, treatment, or immunization of human beings or animals, posing massive threat to public health. Environment-friendly public attitudes promotes the shaping of pro-environmental behavior. However, the public attitudes of MW and the potential determinants remained scarce. The present study aims to reveal globally public attitudes towards MW and captured the determinants. We integrated the crawler technology with sentiment analysis to captured the public attitudes toward MW across 141 specific countries from 3,789,764 related tweets. Multiple cross-national databases were integrated to assess characteristics including risk, resistance, environment, and development. The spatial regression model was taken to counterbalence the potential statistical bias. Overall, the global public attitudes towards MW were positive, and varied significantly across countries. Resilience (β = 0.78, SD = 0.14, P < 0.01) and development (β = 1.66, SD = 0.13, P < 0.01) posed positive influence on public attitudes towards MW, meanwhile, risk (β = -0.1, SD = 0.12, P > 0.05) and environment (β = 0.09, SD = 0.09, P > 0.05) were irrelated to the shaping of positive MW public attitudes. Several positive moderating influences was also captured. Additionally, the cross-national disparities of the determiants were also captured, more specific, public attitudes towards MW in extremely poor areas were more likely to be negatively affected by risks, resilience and development. This study focused mainly on the public attitudes as well as captured the potential determinants. Public attitudes towards MW were generally positive, but there were large cross-national disparities. Stakeholders would need to designate targeted strategies to enhance public satisfaction with MW management.

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

Local car markets in an emerging economy: exploring the dichotomous nature of car ownership growth

The intensive motorization growth observed in emerging and developing economies has attracted increased academic attention. However, many existing studies frequently investigate the car ownership determinants that are typical of Western countries and use aggregate measures that mask the role of imported used cars. This implies that there is an important research gap concerning the role of the second-hand vehicles as a source of car ownership growth in emerging and developing countries. This paper aims to reveal the dichotomous character of car ownership growth in an emerging economy and identify the determinants of local primary (new cars) and secondary (imported used cars) car markets. Using data from the Polish Central Vehicle Register containing entries for more than 20 million cars registered and applying the spatial regression models, we disclose that in addition to well-known determinants of car ownership growth, such as income, population density, and housing types, there may be other factors specific to emerging economies driving this process. Specifically, we test the influence of geographical distance on the source of the car supply and the number of companies and entrepreneurs importing and repairing used cars. The findings suggest that future investigations of motorization processes concerning developing and emerging economies should consider the scale of second-hand car imports and its impact on car ownership and seek country-specific determinants of the phenomenon.

Modeling the impact of land use/land cover (LULC) factors on diurnal and nocturnal Urban Heat Island (UHI) intensities using spatial regression models

This paper models the impact of different land use/land cover (LULC) factors on diurnal and nocturnal Urban Heat Island (UHI) intensities using spatial regression models. This research first developed an extensive dataset of 17 LULC factors as the independent variables and the diurnal and nocturnal UHI intensities as the dependent variables. Second, ordinary least-squared (OLS) regression was implemented to check for spatial autocorrelation. The results showed that spatial autocorrelation exists for diurnal and nocturnal UHI intensities and thus spatial regression models are needed. Hence, the following spatial regression models were implemented: the spatial error model (SEM) and the spatial lag model (SLM). The findings showed that the SEM is the best in modeling the diurnal UHI intensity while the SLM is the best in modeling the nocturnal UHI intensity. The results also showed that the diurnal UHI differs from the nocturnal UHI by its intensity, geographical distribution, and the contributing LULC factors or causes. The outcomes of this research can be used to inform different UHI-related decisions such as identifying and modeling the impacts of the most significant LULC factors on the diurnal and nocturnal UHI intensities as well as determining the most suitable geographical locations for implementing UHI mitigation initiatives.

Does 2D and 3D built environment matter for the spreading of COVID-19 at early stages in Wuhan, China: A spatial and multi-scale analysis

Evidence for the impacts of urban physical attributes on the spread of COVID-19 remains weak due to few investigations into the modifiable areal unit problem (MAUP) and spatial autocorrelation, as well as the lack of fine-grained COVID-19 case data. This study presents a spatial and multi-scale approach to examine the relationships between two-dimensional (2D) and three-dimensional (3D) urban parameters and accumulative confirmed infection cases. The spatial distributions of COVID-19 infections are mapped using anonymized neighborhood-level report data with nearly 50,000 confirmed cases at the early stages of the epidemic in Wuhan, China, and the 2D and 3D built environment factors at multiple spatial scales are characterized. Next, geographical detector model is used to examine the MAUP in the spread of COVID-19 and effects of urban parameters, and spatial regression models are then used to address spatial autocorrelation in the results at the identified appropriate spatial scales. Results reveal that the influences of most urban parameters are sensitive to the scale and areal unit used, and 3D urban parameters, such as building height, have a greater influence than 2D parameters. The estimates from both spatial and non-spatial models suggest the presence of spatial dependence and unobserved autocorrelated factors, and the overestimation of the effects of most urban parameters.

Technical efficiency of teff farms in Ethiopia: Do neighborhood effects matter? A copula stochastic frontier and spatial Durbin regression

Teff is essential to most Ethiopians, but its production is hampered by farmers' characteristics and spatially related neighborhood variables. This study analyzes the neighborhood effect on the technical efficiency of teff farms in Ethiopia using panel data from the Ethiopian socioeconomic survey. The spatial Durbin regression models (SDM) and Copula stochastic frontier were used with 858 teff-growing farmers. The mean value of teff's technical efficiency was found to be 53 %, meaning that farmers had a 47 % likelihood of improving teff farm efficiency. The results from the SDM indicate a significant contribution of neighborhood effects for improving technical efficiency in teff farms. Thus, policymakers could explore implementing localized interventions and knowledge-sharing initiatives to disseminate best practices, innovative technologies, and agronomic knowledge within specific spatial clusters. By doing so, they can leverage the observed influence of neighborhood dynamics on teff farm efficiency.

Spatiotemporal Evolution and Influencing Factors of Urban Industry in Modern China (1840–1949): A Case Study of Nanjing

In modern China, industrialization has formed a critical foundation for the transition to modernization. However, the spatiotemporal evolution patterns and driving mechanisms of urban industrial development in Nanjing from 1840 to 1949 remain unclear. Based on textual historical sources, this study examined the spatiotemporal patterns of urban industrial development in Nanjing from 1840 to 1949 by using spatial analysis methods, GeoDetector, regression models and industrial structure indices. The results reveal the following: (1) The overall spatial distribution pattern of the industry in modern Nanjing exhibited a “one main, one secondary” dual-center “ladle-shaped” arrangement. Over time, industry has expanded from the urban center toward the east and north. (2) The modernization level of different industries was uneven, exhibiting a “center-periphery” spatial pattern. (3) At the micro level, transportation and population density were the primary influencing factors for industrial location, whereas at the macro level, government intervention mainly affected the industrialization pattern. (4) The industrial development pattern in modern Nanjing, in alignment with the “pole-axis” spatial system, serves as a microcosm of China’s urban modernization transition. This study represents the application of GIS methods in the humanities and provides valuable insights for urban planning and development.

Factors affecting the demographic situation in the regions of the Far Eastern Federal District

Subject. The article considers the multifactorial regression modeling and evaluation of indicators affecting the improvement of demographic situation in the regions of the Far Eastern Federal District. Objectives. The aim is to identify factors influencing the population growth in the regions of the Far Eastern Federal District. Methods. The study employs theoretical, descriptive, comparative, and statistical methods, demographic analysis, and multivariate regression modeling. Results. We found that the demographic situation in the Central, Northwestern, Southern, North Caucasian, and Ural Federal Districts is generally stabilizing. The situation is extremely negative in the Volga, Siberian, and Far Eastern federal districts. The spatial regression modeling identified 13 out of 47 factors that affect the population growth of the regions of the Far Eastern Federal District. The development of small business contributes to solving socio-economic problems and has a positive impact on living standards. The number of single-industry towns located in the said Federal District has a positive effect on population growth. Conclusions. There is a decrease in the population in the regions of the Far Eastern Federal District. The development of small and medium-sized businesses is good for demographic situation. This is confirmed by positive correlation of the number of small and medium-sized enterprises and their average number with the population of the region. The number of single-industry towns has a positive effect on population growth. Mining and processing enterprises have a negative environmental impact, however, they are not negative indicators that affect the deterioration of the demographic situation.

Spillover impacts of financial development and globalization on environmental quality in ASEAN countries

In the globalization era, the economic policy of a specific country might be influenced by the development of neighboring countries. Thus, this study aims to probe the direct and spillover effects of financial development, economic growth, and globalization on environmental sustainability in ASEAN countries during the period of 1992-2021. By applying three spatial regression models, the results are summarized: (1) There are positive spillover effects of financial development in neighboring countries on ecological footprint in a particular country; (2) Economic growth has a positive impact on ecological deficits in both the host country and neighboring countries in the short-run; (3) The expansion of globalization in neighboring countries has a negative spillover effect on the ecological footprint in a particular country and vice versa. Based on these findings, the study recommends that when a country formulates its economic policies, it is necessary to calculate the impact of that policy on neighboring countries and vice versa. Encouraging economic growth and expanding the money supply ought to go hand in hand with fostering greater integration. This integration is essential to counterbalance the potential adverse effects of these macroeconomic variables on environmental quality and ecological balance.

Exploring farmland price determinants in Northern Italy using a spatial regression analysis

Using spatial regression models, we detect determinants of farmland’s prices in a rural area located in the upper Treviso plain (Veneto region, Italy). Econometric analysis is based on a Spatial linear regression model able to account for spatial lags in the data. Estimates show which intrinsic and extrinsic characteristics have the greatest influence on price, and how buyers and sellers’ profiles also matter on the price determination. Our application fosters spatial regression models in rural real estate market analysis and appraisal, and highlights that in the area under study the farmland’s prices are significantly affected by factors that are rarely considered in the literature, such as sellers and buyers’ profiles, the land use in the context where the sold plot is located matters, the hydraulic risk of the area and the presence of large infrastructures.

Unveiling the Impact of Urbanization on Net Primary Productivity: Insights from the Yangtze River Delta Urban Agglomeration

Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran’s I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems.

Impact of transport superiority on ecosystem health in arid regions: A case study of southern Xinjiang, China

The increasingly improved transportation infrastructure network is the prerequisite and foundation for driving regional development. Scientifically investigating the mechanisms by which the transport superiority degree (TSD) in arid regions affects the health of ecosystems holds significant practical value and importance for ecosystem protection and regional sustainable development. This study investigates the impact of the TSD on the Ecosystem Health Levels (EHL) in Southern Xinjiang, China, an area characterized by its arid climate and ecological vulnerability. Arid regions, such as Southern Xinjiang, often face unique ecological challenges, including water scarcity, soil erosion, and biodiversity loss, which can be exacerbated by human activities including transportation infrastructure development. By leveraging multi-source remote sensing data, this research aims to uncover the intricate dynamics between TSD and EHL in such a fragile ecological context. The findings of this study are critical for understanding the balance between infrastructural development and environmental conservation in arid regions and provide valuable insights for sustainable planning and policy-making in similar ecological settings. The research results show the following: (1) Over the study period, county-scale TSD in southern Xinjiang generally increased, while the conditions of EHL deteriorated, with higher EHL in the eastern counties than in the western counties. (2) Bivariate spatial autocorrelation analysis reveals a significant spatial dependency between county-scale TSD and EHL, with primary relationship types being LH (Low TSD − High EHL) and HL (High TSD − Low EHL). (3) Spatial regression model results indicate that an increase in TSD leads to the deterioration of county-scale EHL, and during the study period, it was observed that the interference of TSD on EHL gradually weakened.