Geographically Weighted Regression Research Articles

The Evolution of the Spatial–Temporal Pattern of Tourism Development and Its Influencing Factors: Evidence from China (2010–2022)

The Chinese economy has shifted towards a stage of high-quality development, and promoting high-quality development of tourism is of great significance for its sustainable development. Based on the new development concept, using Geographic Information System (GIS) spatial analysis, Moran’s I Index, and the Geographically Weighted Regression (GWR) model, this paper analyzes the spatial–temporal evolution of tourism development in 31 provinces in China during 2010–2022. The findings are as follows: (1) the overall tourism development level of China from 2010 to 2022 was at a medium-low level, showing a spatial distribution pattern of “higher in southeast and lower in northwest”; (2) from the perspective of the Quality–Quantity relationship, the quality of tourism development in China has gradually improved, the regional gap between the east and the west and the internal gap among western regions/provinces have gradually expanded; and (3) tourism innovation and internationalization level are the main influencing factors of high-quality tourism development in China’s provinces, providing practical decision-making references for advancing the high-quality development of tourism.

Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Heilongjiang Province in the Past 20 years

PM2.5 is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM2.5 remote sensing data from 2000 to 2021, the Theil-Sen Median trend analysis, Mann-Kendall significant inspection, and spatial auto correlation were used to analyze the characteristics of space-time changes in PM2.5 concentration, and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM2.5 spatial differences. The results showed that： ① The average PM2.5 value of Heilongjiang Province was between 22.01 and 41 μg·m-3 from 2000 to 2021. From 2008 to 2015, the average PM2.5 value was higher than the secondary concentration limit （35 μg·m-3） of the "Environmental Air Quality Standard." The turning point of the PM2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM2.5 pollution. The PM2.5 concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin, Daqing City, and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM2.5 spatial differences. The remaining key driver factors were in turn： high-end, population density, average annual wind speed, land use, night lights, annual years precipitation, slope, annual relative humidity, and NDVI. Interactive detection showed that the interpretation of PM2.5 points after interaction was higher than a single factor after interaction, indicating that affecting PM2.5 spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM2.5 had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM2.5 pollution and NDVI and land use played an inhibitory effect on PM2.5 pollution. PM2.5 was significantly different from the action role of various influencing factors and the average annual temperature, annual average wind speed, and NDVI impact scale were the smallest, with a variable bandwidth of 43； population density and land use impact scale were the largest, with a variable bandwidth of 140.

Coupling Coordination and Influencing Mechanism of Ecosystem Services Using Remote Sensing: A Case Study of Food Provision and Soil Conservation

Understanding the trade-offs and synergies between ecosystem services is essential for effective ecological management. We selected food provisioning and soil conservation services to explore their intrinsic link and trade-offs. We evaluated these services in Minnesota from 1998 to 2018 using multi-source remote sensing data. The coupling coordination degree model (CCDM) was employed to quantify the relationship between these services. The CCDM evaluates the degree of coordination between systems by measuring their interactions. In addition, we used the geographically weighted regression (GWR) model to identify factors influencing this relationship. Our findings reveal that, while Minnesota’s food provision services have shown a significant overall upward trajectory, distinct declines occurred in 2008 and 2018. In contrast, soil conservation services showed considerable variability from year to year, without a clear trend. Over time, the relationship between food provision and soil conservation services evolved from uncoordinated and transitional to more coordinated development. Our analysis indicates that climate–soil indicators (Z1) exert the most significant influence on the coupling coordination degree (CCD), followed by topography (Z3), vegetation quality (Z4), and socio-economic indicators (Z2). This suggests that natural environmental factors have a greater impact than socio-economic factors. Spatial analysis highlights that topography exhibits significant spatial heterogeneity and serves as the primary spatial driving factor. This study explores the trade-offs between food provision and soil conservation ecosystem services in Minnesota, enhancing the understanding of trade-offs among different ecosystem services and providing insights for global sustainable agricultural development.

Associations Between the Legalization and Implementation of Medical Aid in Dying and Suicide Rates in the United States

Background Some have hypothesized that changing attitudes toward medical aid in dying (MAID) contribute to increased suicide rates, perhaps by increasing interest in dying or the perceived acceptability of suicide. This would represent a strong criticism of MAID policies. We sought to evaluate the association between the legalization and implementation of MAID across the U.S. and changing suicide rates. Methods We evaluated state-level monthly suicide death rates from 1995 to 2021. Because suicide rates vary by state, we constructed geographically-weighted regression models controlling for annualized state-level sociodemographic factors, such as racial distribution (percent Caucasian), average age, income levels, unemployment rates, rates of spiritual engagement, firearm ownership rates, gender ratios, and education levels. We applied a difference-in-difference analysis within our geographically-weighted models. Results 927,929 Suicide deaths were represented in the study. Ten states and the District of Columbia had legalized MAID within the study period. In an univariable analysis, states that legalized MAID differed significantly from non-MAID states with respect to mean monthly suicide rate (non-MAID States: 1.46; MAID states: 1.78; p < 0.0001), as well as several covariates. Monthly suicide death rates were spatially autocorrelated (Moran’s I = 0.607, p < 0.0001). In separate geographically-weighted difference-in-difference analyses, changes in suicide rates were not significantly associated with MAID legalization (β = 0.042, p = 0.33) or with later MAID implementation (β = 0.030, p = 0.63), with differences in suicide rates in MAID and non-MAID states being attributable to baseline between-state differences. Conclusions Our study failed to find evidence that suicide rates were positively associated with MAID legalization or MAID implementation, when controlling for geographic variation and multiple sociodemographic factors associated with suicide risk. This finding contrasts with other studies that have reported a positive association between suicide rates and MAID, and so calls into question one argument against MAID legalization.

Spatial variation of bus stop crime response to changes in the surrounding environment and transit level of service

Increasing concerns over the significant number of crimes occurring at and around bus stops have motivated the analysis of intervening factors to propose solutions to this problem. However, models found so far overlook important features of crime data: spatial dependence and spatial heterogeneity. In addition, crime predictor data has shown to be multicollinear in previous studies. To tackle these issues, this paper analyzes mobile phone thefts and robberies at 19,329 bus stops in São Paulo (Brazil) based on Geographically Weighted Regression (GWR), using components retained from a Principal Component Analysis (PCA) as explanatory variables. A comparison is carried out between GWR and a non-spatial Transformed Linear Regression (TLR), and a Negative Binomial Regression (NBR) with uncorrelated predictors. Effects on crime from 9 PCs, representing central areas, bus transit level of service, transport infrastructure, land use and sociodemographic features, were proven to have high spatial variability. Changes in the surrounding environment can cause higher or lower increases in mobile phone thefts and robberies at stops according to their spatial location. Results showed that GWR performs better than NBR and TLR in predicting bus stop crime, thus compensating for a loss of information associated with PCA. In addition, GWR was able to completely incorporate the spatial dependence found in the non-spatial model, which covered the nearest 96 neighboring stops. The proposed method can effectively detect critical bus stops and regions, contributing to crime prevention. GWR coupled with PCA can also help identify the best locations to install new bus stops.

Modeling the spatially varying effects of biophysical factors on land surface temperature

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

Regional Factors Affecting Liver Cancer Mortality Using GWR

Objectives: This study aimed to identify the regional factors affecting liver cancer mortality using geographically weighted regression (GWR). Methods: Data from 2017 to 2019 were utilized, including health examination statistics and regional medical utilization statistics from the National Health Insurance Corporation, regional infectious disease statistics and community health surveys from the Korea Centers for Disease Control and Prevention, mortality statistics and population census data from Statistics Korea. Spatial data were obtained from the Statistical Geographic Information Service, covering 229 cities, counties, and districts. Statistical analysis was performed using R version 4.3.2, with significant variables identified through ordinary least squares (OLS) analysis, followed by GWR to assess model improvement. Results: The OLS analysis identified percent of population aged 40+, incidence of hepatitis C, high-risk drinking rate, and national health insurance premium as significant factors. Moran’s Index showed a positive spatial autocorrelation (0.373, &lt;i&gt;p&lt;/i&gt;&lt;0.001). GWR analysis increased the adjusted R² from 48% to 61% and reduced the AIC from 1,062.21 to 1,038.59, indicating an improvement in the model’s explanatory power and fit. In Jeollanam-do and Jeju-do, the explanatory power was high, and the percent of population aged 40+ had a strong influence in Gangwon-do. The incidence of hepatitis C had a strong influence in the southern coastal regions, the high-risk drinking rate was notably significant in the Yeongnam region, and the national health insurance premium had a strong influence in Busan. Conclusions: As significant factors vary by region, it is considered important to establish health care policies and projects that take into account the characteristics of each region, and through this, we can expect to reduce liver cancer mortality in the region and improve community health levels.

The Impact of Urbanization-Induced Land Use Change on Land Surface Temperature

Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region’s LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region.

Spatial Dynamics of Crime in North Western Nigeria: A Geospatial Analysis of Crime Patterns and Trends

This study examines the spatial dynamics of kidnapping crime patterns in North Western Nigeria using a Geographically Weighted Regression (GWR) model. The results show that poverty rate, unemployment rate, and population density are positively related to crime rate, while education level is negatively related to crime rate. The GWR model reveals significant spatial variations in the relationships between socioeconomic factors and crime rates across different locations in North Western Nigeria. The findings of this study have important implications for policymakers and practitioners seeking to reduce crime rates in North Western Nigeria, highlighting the need to address socioeconomic factors such as poverty, unemployment, and education.

Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.

Mortality due to ischemic heart disease (IHD) is heterogeneously distributed globally, and identifying the sites most affected by it is essential in developing strategies to mitigate the impact of the disease, despite the complexity resulting from the great diversity of variables involved. To analyze the predictability of IHD mortality using machine learning (ML) techniques in combination with geospatial analysis in southern Brazil. Ecological study using secondary and retrospective data on mortality due to ischemic heart disease (IHD) obtained from the Mortality Information Systems (SIM-DATASUS) de 2018 a 2022, covering 1,191 municipalities in the states of Paraná (399), Santa Catarina (295), and Rio Grande do Sul (497). Ordinary Least Squares Regression (OLS), Geographically Weighted Regression (GWR), Random Forest (RF), and Geographically Weighted Random Forest (GWRF) analyses were performed to verify the model with the best performance capable of identifying the most affected sites by the disease based on a set of predictors composed by variables of procedures and access to health. In the analyzed period, there were 59,093 deaths, 65% of which were men, 82.7% were white, and 72.8% occurred between 60 and 70 years of age. Ischemic heart disease presented the highest mortality rates in the northwest and north regions of the state of Paraná, and in the central-east, southwest and southeast regions of Rio Grande do Sul, the latter state accounting for 41% of total deaths. The GWRF presented the best performance with R2 = 0.983 and AICc = 2298.4, RMSE: 3.494 and the most important variables of the model in descending order were electrocardiograph rate, cardiac catheterization rate, access index to hemodynamics, access index of pre-hospital mobile units, cardiologists rate, myocardial scintigraphy rate, stress test rate, and stress echocardiogram rate. The GWRF identified spatial heterogeneity in the variation of geographic predictors, contrasting the limitation of linear regression models. The findings showed patterns of vulnerability in southern Brazil, suggesting the formulation of health policies to improve access to diagnostic and therapeutic resources, with the potential to reduce IHD mortality.

Spatial Distribution Characteristics and Driving Factors of 777 Traditional Villages in Yunnan Province: A Study Based on GWR Model and Geodetector

Traditional villages (TVs) are clusters for the conservation of farming civilization heritage with multiple values. Studying their spatial distribution and driving mechanisms is conducive to formulating conservation and development strategies. In this study, 777 TVs in Yunnan Province were examined. Spatial analysis methods such as the nearest neighbor index (NNI), kernel density estimation (KDE), and Moran’s I were used to investigate their spatial distribution patterns. Twelve driving factors were selected from natural, spatial, social, and cultural aspects. The driving mechanisms affecting the distribution of TVs were explored by employing the geographically weighted regression (GWR) model and Geodetector combined with mathematical and statistical methods to systematically study the spatial autocorrelation and heterogeneity of TVs in Yunnan. The results of this study indicate that: (1) The 777 TVs in Yunnan Province are evidently clustered in the overall distribution, presenting a contiguous and concentrated distribution pattern of “3 high-density areas + multiple medium-density belts”. However, the distribution of TVs is unbalanced. The spatial distribution is significantly positively correlated globally, and there exist three types of clustering in local areas. (2) The spatial layout of TVs in Yunnan Province is jointly influenced by multiple factors, with obvious spatial heterogeneity. Regional cultural factors are the key ones. TVs tend to be distributed in areas with medium–high elevation, flat slops, sunny slopes, moderate precipitation and temperatures, and a certain distance from water sources and roads. A large number of TVs are distributed in areas with a high proportion of ethnic minorities. (3) The main factors affecting the distribution of national-level TVs in Yunnan are intangible cultural heritage, cultural relic protection units, followed by factors such as transportation, slope, and elevation. The interaction between intangible cultural heritage (X11) and cultural relic protection units (X12) reveals the strongest interactive driving force. This study reveals the diverse characteristics of the spatial distribution of national-level TVs in Yunnan and their influencing mechanisms, which can provide a scientific decision-making basis for the future protection and development of TVs in Yunnan and guide the sustainable development of TV cultural heritage clusters.

Associating socioeconomic factors with access to public healthcare facilities using geographically weighted regression in the city of Tshwane, South Africa.

Access to healthcare is influenced by various socioeconomic factors such as income, population group, educational attainment and health insurance. This study used Geographically Weighted Regression (GWR) to investigate spatial variations in the association between socioeconomic factors and access to public healthcare facilities in the City of Tshwane, South Africa based on data from the Gauteng City-Region Observatory Quality of Life Survey (2020/2021). Socioeconomic predictors included population group, income, health insurance status and health satisfaction. The GWR model revealed that all socioeconomic factors combined explained the variation in access to healthcare facilities (R²=0.77). Deviance residuals, ranging from -2.67 to 1.83, demonstrated a good model fit, indicating the robustness of the GWR model in predicting access to healthcare facilities. Black African, low-income and uninsured populations had each a relatively strong association with access to healthcare facilities (R²=0.65). Additionally, spatial patterns revealed that socioeconomic relationships with access to health care facilities are not homogeneous, with significance of the relationships varying with space. This study highlights the need for a spatially nuanced approach to improving healthcare facilities access and emphasizes the need for targeted policy interventions that address local socio-environmental conditions.

Understanding Sociodemographic Determinants of Voluntary Cesarean Section Deliveries in India through Geospatial Modeling using Multiscale Geographically Weighted Regression (MGWR)

IntroductionOver the period of time, the increasing trend of voluntary cesarean section (CS) delivery has become a serious maternal health concern. Previous studies have shown wide geographic variations in the prevalence of voluntary CS delivery constraints by several sociodemographic determinants. However, none of them have attempted to disentangle this phenomenon by using spatially varying coefficient models to consider local factors. This study aims to identify the spatially heterogeneous relationships among voluntary CS deliveries against the backdrop of sociodemographic determinants in India.Data and MethodsThis study utilized data from the National Family Health Survey (NFHS-5), 2019-21. The ordinary least square (OLS) model was used as a base model, and multiscale geographically weighted regression (MGWR) was employed at the district level to consider the local factors associated with voluntary CS delivery. An appropriate model diagnostic check was also performed to make our model robust and reliable. Several maps were produced using ArcGIS Pro to visualize the distribution of coefficients of several factors.ResultsMore than half (58%) of the total CS deliveries were voluntary; however, choropleth maps have shown that southern peninsular and northeastern districts have a higher prevalence than the rest. The distribution of voluntary CS deliveries with a Moran’s Index of 0.22 was also spatially autocorrelated. Moreover, the values of Jarque–Bera statistics and Koenker (BP) Statistics show that the OLS model residuals were not normally distributed and reflected heteroskedasticity. The estimates from the MGWR show that four and above ANC visits and mass media exposure show a positive relationship with voluntary CS deliveries. The local R-squared map explains nearly half of the variation for voluntary CS deliveries for all northeastern districts. The model fit metric shows higher R square and lower corrected Akaike’s Information Criterion (AICc) values for the MGWR model, which validates that the MGWR model is more robust and parsimonious.ConclusionThe present study revealed that although voluntary CS deliveries are at an increasingly alarming pace, the geographic distribution of voluntary CS deliveries is, albeit unequally. Therefore, this investigation has made an effort to examine how spatial analysis can help identify the geographic patterns and factors that explain this variation in voluntary CS deliveries across space. These findings add value to our understanding of how geographic space and scale matter when health is examined. Public health practitioners can recognize such variations when devising targeted interventions for CS deliveries in India and other developing nations.

Understanding the value of retail accessibility in private housing markets: A study from Turin, Italy

Numerous urban studies have explored the relationship between urban amenities and real estate value. However, a notable gap exists in the literature concerning the specific impact of retail activities on housing values, particularly within European contexts. This study addresses this gap by examining the relationship between housing values and accessibility to different retail typologies, expanding the research stream to a European city by applying the Geographically Weighted Regression (GWR) model. The results indicate that accessibility to shopping retailers, bars and restaurants has a positive effect. In contrast, accessibility to convenience retailers and traditional markets has a negative impact. No significant relationship is observed between housing values and accessibility to supermarket retailers. Furthermore, this study shows that retail accessibility typologies impact differs in different urban areas. These findings provide useful implications for planners and policymakers, highlighting the need for strategic support for the retail sector in safeguarding and enhancing the investments of investors and families.

Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions.

Impact of Paddy Field Expansion on Ecosystem Services and Associated Trade-Offs and Synergies in Sanjiang Plain

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) between these services to achieve optimal resource allocation, has become an urgent issue to address. This study employs the InVEST model to map the spatial and temporal dynamics of five key ESs, while the Optimal Parameter Geodetector (OPGD) identifies primary drivers of these changes. Correlation analysis and Geographically Weighted Regression (GWR) reveal intricate TOS among ESs at multiple scales. Additionally, the Partial Least Squares-Structural Equation Model (PLS-SEM) elucidates the direct impacts of paddy field expansion on ESs. The main findings include the following: (1) The paddy field area in the Sanjiang Plain increased from 5775 km2 to 18,773.41 km2 from 1990 to 2020, an increase of 12,998.41 km2 in 40 years. And the area of other land use types has generally decreased. (2) Overall, ESs showed a recovery trend, with carbon storage (CS) and habitat quality (HQ) initially decreasing but later improving, and consistent increases were observed in soil conservation, water yield (WY), and food production (FP). Paddy fields, drylands, forests, and wetlands were the main ES providers, with soil type, topography, and NDVI emerging as the main influencing factors. (3) Distinct correlations among ESs, where CS shows synergies with HQ and SC, while trade-offs are noted between CS and both WY and FP. These TOS demonstrate significant spatial heterogeneity and scale effects across subregions. (4) Paddy field expansion enhances regional SC, WY, and FP, but negatively affects CS and HQ. These insights offer a scientific basis for harmonizing agricultural development with ecological conservation, enriching our understanding of ES interrelationships, and guiding sustainable ecosystem management and policymaking.

Towards resilient communities: Evaluating the nonlinear impact of the built environment on COVID-19 transmission risk in residential areas

Reflections on urban epidemics often drive improvements in the resilience of the built environment. However, the assessment regarding the nonlinear influence of the community environment on the spread of Corona Virus Disease 2019 (COVID-19) is inadequate. This study analyzed the influential mechanism of built environment factors on the epidemic risk in residential areas, using Shanghai as a case study. During the lockdown in April 2022, Shanghai reported daily data on COVID-19 outbreaks in residential areas, amounting to a total of 90,324 entries. Based on a GIS-based grid analysis approach, we employed a Random Forest (RF) model and a Multiscale Geographically Weighted Regression (MGWR) model to investigate the marginal effects and spatial heterogeneity of environmental factors on the mean count of COVID-19 outbreak days (MC) in residential areas within each grid zone. The results show that the value of MC forms a ring-mountain distribution surrounding the city's outer ring road. The RF model (R² = 0.57) demonstrates that the house price, population density, family number, and the standard deviation of building height (BH_SD) significantly correlated with MC, with the relative importance of 25 %, 13 %, 11 %, and 6 %, respectively. The MGWR model (R² = 0.63) highlights the spatial heterogeneity of family number, house age, house price, property fee, and delivery density. We also found that property fee and green rate were negatively correlated with the MC. These findings help improve responses to public health emergencies and create more resilient communities to cope with pandemics.

Quantitative analysis of impact of human activities on soil erosion using the RUSLE model in a typical karst area in Guizhou, China.

Human activities have significant influence on soil erosion in karst areas. The spatial and temporal evolution of soil erosion in Guizhou Province was evaluated using the Revised Universal Soil Loss Equation (RUSLE), which revealed an increasing trend in the initial data analysis for the soil erosion modulus. To disclose the impact of human activities on regional soil erosion, the soil erosion in 2000, 2010, and 2020 was analyzed. The results show the following: (1) The average values of the soil erosion modulus in the study area for 2000, 2010, and 2020 were 4.479, 4.945, and 5.806 t·hm-2·a-1, respectively; when considering human activities without the influence of rainfall erosivity, these values were 4.679, 4.963, and 4.799 t·hm-2·a-1. The influence of human activities on soil erosion is gradually becoming a positive force. (2) According to the Spearman regression analysis, the top four factors related to soil erosion in 2000 and 2010 were soil loss risk (E, 0.721 and 0.737), anti-erosion factors (Pr, - 0.236 and - 0.221), rock exposure rate (0.222 and 0.279), and altitude (0.210 and 0.195). In 2020, the top four factors were Pr (0.725), land surface temperature (LST, 0.268), NDVI (- 0.232), and E (0.186). In the first two stages, soil erosion is closely related to natural factors, while in 2020, soil erosion is more closely related to human activities. (3) The geographically weighted regression (GWR) showed the highest range of regression coefficients for Pr (150), followed by E and NDVI (25), rock exposure rate (10), and land surface temperature (LST) (1.5). The rainfall erosivity is increasing annually as a consequence of global climate change. This rise in rainfall erosivity has resulted in a corresponding increase in soil erosion in the study area, which obscures the positive impact of human activities in the reduction of soil erosion.

Revealing the dynamic effects of land cover change on land surface temperature in global major bay areas

Comparative studies across national boundaries on urban heat islands (UHI) are lacking, especially comparative analyses among globally important bay area urban agglomerations at different levels of urbanizations. Bay areas around the world are currently experiencing different stages of urbanization, and the transformation of urban land cover into thermal environments varies significantly. This study investigated the spatial-temporal changes of land cover change (LCC) and the land surface temperature (LST) in four major bay areas (including the San Francisco Bay Area (SFBA), the New York Bay Area (NYBA), the Tokyo Bay Area (TBA), and the Guangdong-Hong Kong-Macao Bay Area (GBA)) from 2000 to 2020. Utilizing correlation analysis and the multiscale geographical weighted regression model (MGWR), the goal was to understand how LCC impact the thermal environmental regionally. The results showed that: (1) The main shift in land cover involved a decrease in forested and cultivated area and an increase in impervious surfaces, with deforestation and agriculture conversion being major factors in this increase. The GBA gave a significant 112% rise in impervious surface during this period. (2) The LST trended towards higher, medium LST zones, closely linked with urban impervious surface expansion. (3) The MGWR model highlighted the direct non-stationary relationship between land cover alterations and LST effects. It showed that conversion to impervious surfaces, particularly from water and cultivated lands, markedly intensified LST, whereas water bodies and forests demonstrated a substantial capacity to mitigate LST. Urban planners were advised to mitigate LST elevations stemming from the transformation of forest lands and aquatic environments into impervious areas, a change notably prevalent in highly urbanized regions. This study provides valuable perspectives on balancing the management of urban thermal environments and urbanization processes.

Exploring the spatiotemporal distribution characteristics and driving factors of water erosion in mountain area based on RUSLE-SDR

Quantifying the contributions of driving factors and analyzing dynamic changes of water erosion in mountain areas are crucial for soil erosion control and sustainable soil resource utilization. In this study, the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) model were integrated, and the Geographically Weighted Regression (GWR) and path analysis models were used to explore the contributions and interactions of key influencing factors (precipitation, NDVI, slope, soil moisture) on water erosion in Longnan City. The results showed that the RUSLE-SDR model could simulate the water erosion process effectively in Longnan City from 2000 to 2020 (R2 = 0.821, NSE = 0.67). The spatial and seasonal distribution of water erosion intensity was consistent with precipitation, showing the characteristics of weak in northwest and strong in southeast, and summer is the most serious period of water erosion. The GWR and path analysis models revealed that vegetation and slope were the main influencing factors of water erosion, and they had a strong interaction. When NDVI was below 0.67, slope had a direct impact on water erosion; when NDVI was between 0.67 and 0.82, slope and vegetation jointly influenced water erosion; and when NDVI was above 0.82, vegetation became the dominant factor, while slope indirectly affected erosion by regulating vegetation cover. Precipitation was the main factor that influenced erosion when the rainfall was less than 550 mm, but when the rainfall exceeded 550 mm, it exhibited a strong inhibitory effect on erosion through vegetation. This study reveals water erosion’s driving mechanisms in mountain areas and provides soil erosion control measures’ implementation with a scientific basis and theoretical support.