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It is crucial for social sustainability that adolescents have access to social sports services fairly. However, there are few research studies on requirements for sports facilities and spatial accessibility of adolescents at a fine scale. Further, identifying the heterogeneity of the relationship between spatial accessibility and other factors and their scales simultaneously would be conducive to reveal the variations of spatial accessibility effectively under the potential scale effect. This research aims to explore the heterogeneity of spatial accessibility to sports facilities at a fine scale in Changsha, China. The Gaussian-based two-step floating catchment area model is first employed to evaluate spatial accessibility. Then, multiscale geographically weighted regression (MGWR) is applied to examine the relationship between spatial accessibility and its associated factors. The mean accessibility in Yuelu District (0.158) is the highest as well, and the standard deviation (0.236) is also the highest one. Both the accessibility (0.019) and its standard derivation (0.029) in Furong District are the lowest. The results show that there is a lack of balance of spatial accessibility for the clusters with different accessibility levels distributed in the study area. Some socio-economic factors, such as housing price and nighttime light intensity, have significant impacts on spatial accessibility for adolescents with spatial and scale heterogeneity by using MGWR. Based on heterogeneous distribution and association, suggestions for promoting spatial accessibility to sports facilities for adolescents are proposed.

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns. [ABSTRACT FROM AUTHOR]

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran's I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants. [ABSTRACT FROM AUTHOR]

Based on the dynamic monitoring data of floating population in China in 2017, this paper uses the multiscale geographically weighted regression model to analyze the spatial heterogeneity of the influencing factors of urban identification and perceived acceptance of floating population in China. It is found as follows. ➀ The spatial pattern of urban identification and perceived acceptance of floating population is similar, showing obvious spatial agglomeration, but overall, the perceived acceptance is lower than the urban identification. ➁ There are significant differences in the influencing factors of spatial heterogeneity of urban identification and perceived acceptance The influencing factors of urban identification are relatively single, and the main influencing factors (proportion of highly educated talents, proportion of marriage and inter-provincial mobility ratio) have a significant global impact. Perceived acceptance is influenced by more factors. Except for the main mobility factors (inter-provincial mobility ratio, source urban agglomeration ratio) have a global impact, other factors show significant local heterogeneity. ➂ The urban identification is affected by individual growth, source city, and labor level, while the perceived acceptance is affected by individual internal characteristics, floating status, and external economic industries. Based on the above conclusions, it can be seen that it is important to formulate targeted management and service policies for floating population according to local conditions. [ABSTRACT FROM AUTHOR]

BackgroundIn recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies.MethodsThis study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran’s I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective.ResultsThe spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence.ConclusionMeteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.

The built environment is a structural determinant of health. Here we reveal spatially heterogeneous associations of built environment indicators with objective health outcomes (morbidity) by combining a random forest (RF) approach and a multiscale geographically weighted (MGWR) regression method. Using data from six Japanese cities, we found that the ratio of morbidity has obvious spatial agglomerations. The mixed land-use diversity with 1000 m buffer, distance to hospital, proportion of park area with 300 m buffer, and house price with 2000 m buffer, negatively affect health outcomes at all locations. For most locations, high PM2.5 or high floor area ratio with 2000 m buffer are linked to a high ratio of morbidity. Our findings support the use of such data for long-term urban and health planning. We expect our study to be a starting point for further research on spatially heterogeneous associations of the built environment with comprehensive health outcomes. [ABSTRACT FROM AUTHOR]

With the deepening linkage between housing and finance, the financial attributes of housing have been increasing. Thus, housing financialization has become a worldwide phenomenon that is gradually emerging in China's real estate market and thus cannot be ignored. The amount of urban capital is an important manifestation of financialization, but only a few studies have considered the spatial heterogeneity of impact of urban capital amount—represented by loan balances (LOAN) on housing prices. To fill this gap, this study builds a dataset of housing prices and influencing factors for county-level units using 2109 counties in China and analyzes the spatial scope and heterogeneity of housing financialization. Results show that globally, LOAN has a significant positive effect on housing prices, and the impact direction is in line with theoretical expectations. Locally, spatial heterogeneity exists for the impact of LOAN on housing prices, and the phenomenon of housing financialization is mainly observed in China's eastern coastal area. This study can help enhance the understanding of the spatial constraints on the impact of LOAN on housing prices and the spatial heterogeneity of housing financialization in China. Moreover, it provides a theoretical basis for policymakers to formulate spatially differentiated housing policies.

Consistent urbanization and global warming escalates the summer temperatures of the urban, significantly impacting daily lives and endangering well-being. It is difficult to balance urban construction and increasing the blue-green space. Hence, understanding the impact of changes in the blue-green spatial patterns in different spaces on the urban thermal environment is beneficial to the rational layout of urban patterns. Drawing from the case study of Shanghai, by employing bivariate spatial autocorrelation and multiscale geographically weighted regression, the spatial interplay between changes in the blue-green spatial distribution and modifications in land surface temperature grades is scrutinized, thus unraveling the underlying mechanisms of their mutual influence. The findings reveal the following: (1) The transformation of the blue-green spatial pattern exhibited substantial discrepancies between the northern and southern sectors. (2) The alteration of the thermal environment in Shanghai varies significantly spatially and is characterized by a decrease in temperature grade in the southwestern suburbs, an increase in the east, and almost no change in the central urban region. (3) Furthermore, the correlation between the extent of the change in the blue-green spatial pattern and the change in land surface temperature manifested spatial unevenness. (4) Finally, the mechanism underlying the changes of alterations in the blue-green spatial pattern on the thermal environment of the city emanates primarily from the influence of heat exchange areas. The spatial instability of the influence of blue-green spatial pattern on land surface temperature can provide implications for urban planners.

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

The multiscale geographically weighted regression (MGWR) model is an important extension of the classical geographically weighted regression (GWR) model that can be used to explore the spatial nonstationarity of the regression relationship in spatial analysis, but also allows for different scales on conditional relationships between response and different predictors. A Bayesian version of the MGWR model is proposed to obtain estimates of the spatially varying coefficients and the bandwidths simultaneously. The hierarchical form of the Bayesian MGWR model has attractive features, including obtaining posterior estimates of the bandwidths and local parameters simultaneously, and their uncertainty can be easily measured. For Bayesian posterior inference, an efficient algorithm based on integrated nested Laplace approximation is introduced to provide a great alternative of the classical Markov chain Monte Carlo algorithm under the Bayesian framework. The performance of the proposed method is evaluated through simulation study, and it is shown that the proposed approach can correctly identify the differences between scales of parameter surfaces and also obtain precise posterior estimates. Finally, for illustration, this approach is used to analyze monthly housing cost data in the state of Georgia. [ABSTRACT FROM AUTHOR]

In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents' willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents' WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents' WTPEP improved during COVID-19, which indicates that residents' demand for an ecological environment is increasing; (2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP; (3) Environmental degradation, health, environmental quality, and education are WTPEP's significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future. [ABSTRACT FROM AUTHOR]

Spatial variations exist in migration patterns and the processes causing the observed patterns. Yet, far less attention has been given to the latter. Even if a few studies have engaged in spatial heterogeneity in the process of migration, the scale multiplicity is not emphasised. This research aims to address these gaps with evidence from Chinese cities. To facilitate international comparisons, we use data on intercity migration over a 5‐year interval instead of the floating population. Following an elaboration of highly imbalanced migration patterns at the city scale, we detect spatial heterogeneity in the processes underlying these patterns based on 17 potential determinants in four domains (namely labour market conditions, site‐specific amenities, agglomerative effects, and institutional dividends) by multiscale geographically weighted regression. The results indicate that cities' attractiveness to migrants depends on a broad spectrum of factors whose influences are location‐dependent and exhibit specific patterns, including east–west, south–north, and southwest–northeast gradient patterns, as well as discrete clustered and concentric patterns. The diagnostic analysis has confirmed that the provenance of spatial‐varying effects is irrelevant to the nonlinearity. These effects are also scale‐sensitive, showing that the influencing scales of the labour market status and agglomeration factors tend to be larger (i.e., their effects are spatially stationary) than those relevant to amenities and policies, which vary considerably (i.e., the impacts of some factors change significantly over space). These findings regarding spatial heterogeneity deepen our understanding of migration in China and highlight the fact that there is no 'one size fits all' approach for government policies designed to attract migrants. [ABSTRACT FROM AUTHOR]

Conventional public transport travel is an important part of the urban passenger transport system, and its travel intensity is obviously affected by the urban built environment. Previous modeling methods did not consider the multiscale spatial heterogeneity between the influencing factors, and the research results have certain limitations. Taking the main urban area of Qingdao as the research area, firstly, extract conventional public transport passenger flow from public transport GPS and IC card data were set as the dependent variables. Secondly, 14 indicators were obtained as independent variables through the open network map platform. Spatial data was used to analyze the spatial evolution characteristics of conventional public transport passenger flow of 303 research units. Finally, geographically weighted regression and multiscale geographically weighted regression model were introduced to quantitatively analyze the spatial heterogeneity of the impacts of built environmental variables on the passenger flow of conventional bus. The results show that the multiscale geographically weighted regression model is more explanatory to the geographically weighted regression model. The school density, hospital density, bus line network and parking lot density positively affect the bus traffic, shopping density, land use mixing and distance from bus station significantly negatively affect the conventional public transport passenger flow in morning and evening peak hours, and the bus line network and school density have obvious spatial heterogeneity and their influence degree shows a trend of diminishing spread from south to north. [ABSTRACT FROM AUTHOR]

In rapidly growing cities, on-street parking spaces consistently serve as a supplement to urban parking supply. However, prolonged curb occupancy remains a prevalent issue on roadways with limited parking management, leading to reduced traffic flow and safety concerns in non-motorized areas. To support the formulation of fine-grained management strategies based on on-street parking duration (OPD), we investigate the distribution of OPD across various roadway segments and explore the factors influencing it at different scales. Real data are obtained from a newly built-up area of Xi'an, China, and the spatial effects of factors influencing OPD are modeled using a multiscale geographically weighted regression (MGWR) method. Results show that the number of parking events, the number of parking spaces, employment buildings, the parking space location, and the distance to adjacent bus stops spatially significantly affect the OPD on both weekdays and weekends. The lowest parking fee level is positively associated with weekday OPD. The estimated coefficients of the variables for on-street parking lots are spatially heterogeneous, which is discussed with the substantial parking policy implications. This study can inform enhanced parking management policies in cities through spatial effects analysis. • On-street parking duration (OPD) is investigated in newly built-up urban areas. • The multiscale GWR is utilized to capture the spatial effects of OPD influences. • Policy implications for parking management are given with further discussion. [ABSTRACT FROM AUTHOR]

The built environment, as perceived and felt by human beings, can shape and affect residential satisfaction. From the perspective of municipal administrators, understanding the building environment and its relationship with people's residential satisfaction is crucial to improving people's living environment. This study examines the correlation between built environment elements and residential satisfaction using the consideration of spatial heterogeneity of such a correlation. Machine vision technology is introduced to quantify the design dimension of the built environment. The method of multiscale geographically weighted regression is used to evaluate the relationship between built environment and residential satisfaction and to analyze the spatial heterogeneity in the influencing effects. This empirical study draws on 399 collected samples from the residents of Zhengzhou, China. The results show that elements of the built environment, including street space design features, have a significant effect on people's residential satisfaction in Zhengzhou City. The factors of functional diversity and distance to the city center show spatial heterogeneity in influencing effects on residential satisfaction. The results of this study could help municipal managers to improve people's residential satisfaction in Zhengzhou City through the development of urban renewal policies. [ABSTRACT FROM AUTHOR]

BackgroundOpioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates.MethodsData are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016–2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely “level of influence” (the percentage of older Medicare beneficiaries affected by a certain determinant), “scalability” (the spatial process of a determinant as global, regional, or local), and “specificity” (the determinant that has the strongest association with the OUD rate).ResultsThe results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others.ConclusionsOur findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.

The reasonable layout of green sports space has positive significance for promoting residents’ outdoor sports. Existing studies rarely use large-scale trajectory data to analyze urban greenway physical activity; they mostly use on-site research and questionnaires to conduct research. This study, using central Beijing as a case study, identified the spatiotemporal relationships of greenway use intensity with the social, economic, and built environment. A multiscale geographically weighted regression (MGWR) model was used to explore the temporal heterogeneity of the factors, compensating for the lack of cross-sectional data in existing studies. The results demonstrated significant differences in the spatiotemporal patterns among the core, central and inner suburban areas, and they revealed residents’ preferences in different periods. The MGWR model indicated that there were obvious scale differences and spatial heterogeneity in the greenway use intensity among social, economic, and built environment factors. Housing prices, residential density, historical resource density, and greening quality of greenways contributed to greenway use intensity, which was weak in the core area and strong in the inner suburban area. The reason for the difference in the temporal dimension was related to commuting and the separation of workplaces and residences. The layout of the greenway should be combined with rivers, historical resources, and green space to improve its attractiveness. This paper improved the spatial awareness of greenway use and facilitated the ability of urban planners to generate appropriate strategies to improve land use efficiency in different regions and promote green spatial justice to meet the needs of green, healthy cities.

In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents’ willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents’ WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents’ WTPEP improved during COVID-19, which indicates that residents’ demand for an ecological environment is increasing; (2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP; (3) Environmental degradation, health, environmental quality, and education are WTPEP’s significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events. [ABSTRACT FROM AUTHOR]

The objective of the present study was to examine the effects of a confluence of demographic, socioeconomic, housing, and environmental factors that systematically contribute to heat-related morbidity in Maricopa County, Arizona, from theoretical, empirical, and spatial perspectives. The present study utilized ordinary least squares (OLS) regression and multiscale geographically weighted regression (MGWR) to analyze health data, U.S. census data, and remotely sensed data. The results suggested that the MGWR model showed a significant improvement in goodness of fit over the OLS regression model, which implies that spatial heterogeneity is an essential factor that influences the relationship between these factors. Populations of people aged 65+, Hispanic people, disabled people, people who do not own vehicles, and housing occupancy rate have much stronger local effects than other variables. These findings can be used to inform and educate local residents, communities, stakeholders, city managers, and urban planners in their ongoing and extensive efforts to mitigate the negative impacts of extreme heat on human health in Maricopa County. [ABSTRACT FROM AUTHOR]

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events.

• The impact of changes in blue-green spatial patterns on alterations in land surface temperature grades is subjected to spatial analysis. • Spatial disparities in the influence of distinct indicators of blue-green spatial patterns on shifts in land surface temperature grades are observed. • Land surface temperature grades can be effectively reduced through the expansion of heat exchange areas. • Guidance for mitigating urban thermal environments through the consideration of blue-green spatial patterns is provided. Consistent urbanization and global warming escalates the summer temperatures of the urban, significantly impacting daily lives and endangering well-being. It is difficult to balance urban construction and increasing the blue-green space. Hence, understanding the impact of changes in the blue-green spatial patterns in different spaces on the urban thermal environment is beneficial to the rational layout of urban patterns. Drawing from the case study of Shanghai, by employing bivariate spatial autocorrelation and multiscale geographically weighted regression, the spatial interplay between changes in the blue-green spatial distribution and modifications in land surface temperature grades is scrutinized, thus unraveling the underlying mechanisms of their mutual influence. The findings reveal the following: (1) The transformation of the blue-green spatial pattern exhibited substantial discrepancies between the northern and southern sectors. (2) The alteration of the thermal environment in Shanghai varies significantly spatially and is characterized by a decrease in temperature grade in the southwestern suburbs, an increase in the east, and almost no change in the central urban region. (3) Furthermore, the correlation between the extent of the change in the blue-green spatial pattern and the change in land surface temperature manifested spatial unevenness. (4) Finally, the mechanism underlying the changes of alterations in the blue-green spatial pattern on the thermal environment of the city emanates primarily from the influence of heat exchange areas. The spatial instability of the influence of blue-green spatial pattern on land surface temperature can provide implications for urban planners. [ABSTRACT FROM AUTHOR]

• CO2 Emissions from heavy-duty trucks reveals spatial non-stationarity. • Different factors influence the CO2 emissions at varying spatial scales. • Land use distribution exhibits the highest explanatory power at 35.7%. • Industrial agglomeration and freight hubs' specialization aid carbon reduction. Urban freight emissions play a vital role in the overall CO 2 emissions of the transportation sector. However, previous studies have mainly focused on the CO 2 emissions of the entire transportation sector. This study addresses this gap by employing a bottom-up emission estimation model and constructing a high spatiotemporal resolution road freight emission inventory using GPS trajectory data from over 17,000 heavy-duty trucks (HDTs) in Shenzhen. Spatiotemporal analysis reveals distinct patterns and spatial non-stationarity in CO 2 emissions from road freight transportation. A multiscale geographically weighted regression model is applied to analyze the potential factors that may influence CO 2 emissions at varying spatial scales. The findings demonstrate that land use distribution exhibits the highest relative explanatory power at 35.7%, followed by accessibility to freight hubs at 23.9%. Based on these findings, this study proposes four policy recommendations to reduce CO 2 emissions and enhance urban sustainability. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks have caused growing concern due to their negative environmental externalities. Existing studies have not adequately examined the heterogeneity in the spatial distribution of heavy-duty truck activities and identified major influencing factors behind the patterns, thus making effective traffic management difficult. This study extracts heavy-duty truck activities information from long-term trajectory data, and clarifies the mechanism in which heavy-duty truck activities are subject to various industrial, transportation, and land use factors by developing global and local regression models. The results of the Global Ordinary Least Squares and Multiscale Geographically Weighted Regression models indicate that heavy-duty truck activities are driven not only by the location of industrial and freight hubs, but also by the availability of truck service infrastructure such as road networks and parking spaces. Moreover, the way these factors impact on heavy-duty truck activities differ across places. Local authorities therefore should evaluate the dynamic relationship between land use development and heavy-duty truck activities and mitigate the negative impacts of such activities on urban life. Locally tailored traffic management policies on heavy-duty truck will be helpful. [ABSTRACT FROM AUTHOR]

Taxi plays a supplement role in sustainable development of urban public transport systems. However, the extent to which the built environment affects taxi ridership at various spatial scales deserves further exploration because understanding the true spatial heterogeneity across a varying scale could be valuable for both global and localized policy decision-makings. In this study, we attempt to analyze and discuss the spatial predictors of taxi ridership by utilizing an multiscale geographically weighted regression (MGWR) model and comparing the model's performance to that of ordinary least square (OLS) and geographically weighted regression (GWR) models. Using the taxi data of Xi'an city, we found that the MGWR model could explain 81.8% of the total taxi ridership fluctuations and allows localized and targeted policy makings to help taxi drivers search for passengers and to improve passengers' taxi-hailing experiences in specific districts. [ABSTRACT FROM AUTHOR]

Ecosystem service value (ESV) is increasingly influenced by land use/land cover (LULC). At the same time, China's extensive urbanization over the past 40 years has reduced ESV, highlighting a pressing need for spatial studies. However, few studies on the spatial relationship between LULC, urbanization, and ESV, especially at the national level, represent a significant gap in this field. In this research aims, we aim to fill this gap by examining their spatial relationships by studying 279 prefecture-level cities in China. Furthermore, to understand the changing dynamics, we studied the temporal and spatial heterogeneity of the effects of LULC and urbanization on ESV in 2005, 2010, 2015, and 2020 using a multiscale geographically weighted regression model. Regarding LULC, the results show that the impact of land use intensity on ESV is significantly negative nationwide, and the trend continued to strengthen from 2005 to 2020. There was significant spatial and temporal heterogeneity in the effects of land use diversity on ESV, with negative effects in the north and a positive impact in the Beijing-Tianjin-Hebei region. Concerning urbanization, the association between ESV and different types of urbanization varied greatly over space and time. Our findings suggest that policymakers should pay attention to compact land development, optimize land use structure, and promote the mixed use of land on the basis of rational planning in urban layouts, to mitigate the negative impacts of urbanization and LULC changes on ESV. [Display omitted] • Complex temporal and spatial relationships between Urbanization, Land Use/Cover, and ESV in China. • The association between ESV and different types of urbanization varied greatly over space and time. • Land use intensity negatively impacts ESV nationwide, trend strengthens 2005-2020. • Land use diversity effects on ESV show spatio-temporal heterogeneity. [ABSTRACT FROM AUTHOR]