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AbstractScale in multiscale geographically weighted regression (MGWR) directly impacts the accuracy of coefficient estimates and shapes the comprehensive evaluation of the intensity of spatially non-stationary relationships. Presently, MGWR primarily utilizes back-fitting for sequentially optimizing multiple scales (MGWR-BF). However, the set of individual optima obtained through sequential optimization may not necessarily represent the global optimum. To address this issue, this paper proposes a multi-scale cooperative optimization within MGWR (MGWR-GA) model. Specifically, MGWR-GA employs a genetic algorithm to simultaneously input potential scale combinations, each comprising P scales. Subsequently, it introduces a dedicated overall estimation algorithm designed for these P scales, ultimately determining the optimal scale combinations based on the AICc. Simulation experiments have shown that, at least for global stationarity, the scales obtained by MGWR-GA approximate the true values across twelve different test environments. Additionally, the coefficient estimation bias of MGWR-GA is lower than that of MGWR-BF, especially in low signal-to-noise ratio settings. Empirical experiments further confirm the effectiveness of MGWR-GA in identifying both globally stationary and locally non-stationary scales. Furthermore, MGWR-GA outperforms MGWR-BF in terms of goodness-of-fit, adjusted goodness-of-fit, AICc and spatial autocorrelation of residuals. These findings indicate that MGWR-GA can serve as a valuable tool for modeling spatially non-stationary relationships. [ABSTRACT FROM AUTHOR]

Landslides pose a significant threat to the safety of people and their property. Previous landslide susceptibility assessment efforts have often overlooked the impact of spatial variations in the distribution of driving factors on disaster occurrences. Geographically Weighted Regression (GWR) is the most commonly used method for spatially heterogeneous modeling. However, it uses a single bandwidth and cannot explain the spatially varying scaling parameters of each factor. This study focuses on Luhe as the research area and introduces the Multiscale Geographically Weighted Regression (MGWR) model. By considering different spatial scales, the spatial non-stationarity of the relationship between landslides and their driving factors was explored. The final results indicate that the MGWR model outperforms the global regression Ordinary Least Squares (OLS) model and the traditional GWR model. Within the study area, the influence of various factors on landslides is complex, exhibiting a multipolar pattern in space. Elevation emerges as the dominant driving factor in the research area, showing a negative correlation with landslides. By employing the concept of multiscale spatial local regression, one can better analyze the interaction patterns between factors and landslides, providing improved insights for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Abstract As Japan faces a growing crisis of vacant housing, this study investigates the underexplored relationship between vacant house distribution and neighbourhood characteristics in Toda City, a satellite city of the Tokyo metropolis located in Saitama Prefecture. We selected 10 community features, including living convenience, housing supply, and policy support, to explore their influence on vacancy rates. Using a comprehensive dataset primarily derived from the PLATEAU data, we employed a two-step method combining global and local Moran’s I analyses to assess the spatial distribution normality of vacant houses. We then compared traditional statistical data analysis with spatial data analysis through multiscale geographically weighted regression to evaluate their effectiveness and identify the factors most closely associated with vacancy rates. Our findings reveal that spatial data analysis provides superior insights compared to traditional statistical methods. Both approaches consistently indicate a significant positive correlation between the supermarket area ratio and vacancy rates, and a significant negative correlation between road adjacency and vacancy rates. This study demonstrates the feasibility of using spatial data to analyse neighbourhood characteristics in the context of declining vacancy rates in satellite cities. The insights gained offer theoretical support for selecting data and methodologies in future research and policymaking that are aimed at mitigating the growth of vacant houses.

Abstract Background Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

In the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS’s environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS’s environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS’s environmental benefits variably.

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. [ABSTRACT FROM AUTHOR]

Urban agglomerations (UAs) are the main battlefield of urbanization and the most concentrated areas of carbon emissions (CEs). Nevertheless, limited studies have examined the impact of urbanization level (UL) on CEs in UAs in China. This study aimed to identify the spatial relationship between UL and CEs in Chinese UAs and to conduct a comprehensive analysis of the differences in CEs caused by urbanization. The findings would provide scientific support for the China's dual-carbon goals and the achievement of green and low-carbon urban development. Spatial variations in UL and CEs in 19 Chinese UAs were assessed in 2000, 2010, and 2020 using distribution dynamics and spatial regression models. The results indicated that the UL of UAs in China evidently increased over time, and UAs contributed approximately 80% of the national CEs. Significant spatial dependence was identified between urbanization factors and CEs. The regression results indicated that an increase in UL promoted the growth of CEs, and the form of the urban land had a significant and highly variable impact on CEs. Our findings provide a valuable case study for exploring relationships between UL and CEs in other UAs worldwide. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health. [ABSTRACT FROM AUTHOR]

This article delves into the distinctive intra- and interregional geographical heterogeneity of Italy, emphasizing demographic and socio-economic variations and the role of foreign employment, considering the labour market as a fundamental driver for migration and local inclusion. The article identifies a gap in understanding the employed foreign population as a multiscale process in Lombardy and Campania, representative regions as case studies from the North and South divide using a MGWR approach. The results reveal contrasting effects of the Italian labour force's unemployment rate (URI). In Lombardy, a positive effect suggests working competition between labour force components while, in Campania, the relation is less clear. The analysis underscores significant local heterogeneity, emphasizing the importance and urgency of employing local scale analysis for accurate statistics. The study emphasizes the multiscale nature of the analysed process, demonstrating variable effects across different regional contexts. While the study is limited to two regions and cross-sectional data, it marks the first attempt in Italy to address the foreign presence as a multiscale process, highlighting the need for localized and multiscale approaches in understanding spatial processes related to demography and population issues. [ABSTRACT FROM AUTHOR]

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.

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China's Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF's spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries. [ABSTRACT FROM AUTHOR]

Against the deteriorating urban-rural gap background, digital financial inclusion has offered the chance for rural areas to emerge from the economic downturn. Based on the data from 1907 counties in China, this study uncovers the spatially diversified impact of digital financial inclusion on rural economic development through the multiscale geographically weighted regression model. The key conclusions of this study are as follows: first, digital financial inclusion has spatially heterogeneous impacts on rural economic development. This relationship with economic development is also reflected in multiple aspects of digital financial inclusion, such as usage, payment, investment, and credit. Second, digital financial inclusion favors economic development in more south coastal and northwestern regions than others. Third, credit services have a greater and more extensive effect in rural regions than other sub-indicators, which warrants a superior focus. Last, digital financial inclusion's effects diffuse from the capital city, underscoring the feasibility of leveraging the leadership of these regions to expand the impact breadth of digital financial inclusion. In summary, this study complements the spatial heterogeneity of digital financial inclusion into the research framework and offers an in-depth understanding of digital financial inclusion. [ABSTRACT FROM AUTHOR]

The Qinling Mountains serve as a vital ecological barrier for China, needing regular monitoring for sustainable progress. The present study analyzed the Qinling Mountains (sections in Shaanxi province) via the Google Earth Engine (GEE) and Landsat images, constructing a Remote Sensing Ecological Index (RSEI) for quantitatively analyzing the spatiotemporal evolution of the ecological environment quality from 1990 to 2020. Additionally, this study integrated geodetector and multiscale geographically weighted regression (MGWR) to discern the driving factors influencing ecological quality. The results indicated the following: (1) over the 30 years, the RSEI in the study area initially declined (1990–2005) and then began to improve (2005–2020), demonstrating an overall upward trend; (2) RSEI varied with topography: lower values corresponded to lower elevations (<1200 m) and gentle slopes (<10°), whereas higher values were linked to higher elevations (>1600 m) and steeper slopes (>20°); and (3) NDVI, DEM, land use, and the presence of protected areas emerged as the main driving forces of ecological quality. The interaction between ecological conservation policies and socioeconomic factors enhanced the explanatory strength for the differentiation in ecological environment quality. In conclusion, factors like increased vegetation cover and the establishment of protected areas have significantly promoted the improvement of ecological environment quality in the Qinling region, making this study a scientific reference for providing ecological environment protection and promoting high-quality development. [ABSTRACT FROM AUTHOR]

Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments' and enterprises' efforts to improve the cycling environment and ensure the efficient management of shared bikes. [ABSTRACT FROM AUTHOR]

This study aims at finding out the important determinants of prevalence rate of COVID-19 in the Asian continent using spatial analysis. The impact of climatic, socioeconomic, demographic, and health status variables on the prevalence rate of COVID-19 is seen through various spatial models such as Spatial lag, Spatial error, Geographically Weighted regression model, and Multiscale Geographically Weighted regression model. The performance of the models is compared under different comparison criteria. It is found that among all, Multiscale Geographically Weighted regression model outperformed other competitive models. Findings also indicate that cardiovascular health, prevalence of smoking habit, human development index, and net migration rate played significant role in defining the prevalence rate of COVID-19 in Asia. [ABSTRACT FROM AUTHOR]

In order to investigate the mechanism of environmental factors in soil salinization, this study focused on analyzing the temporal-spatial variation of soil salinity in the Ogan-Kuqa River Oasis in Xinjiang, China. The research aimed to predict soil salinity using a combination of satellite data, environmental covariates, and advanced modeling techniques. Firstly, Boruta and ReliefF algorithms were employed to select variables that significantly affect soil salinity from the Sentinel-2 satellite data and environmental covariates. Subsequently, a soil salinity inversion model was established using three advanced strategies: comprehensive variable analysis, a Boruta-based variable selection algorithm, and a ReliefF-based variable selection algorithm. Each strategy was modeled using a Light Gradient Boosting Machine (LightGBM), an Extreme Learning Machine (ELM), and a Support Vector Machine (SVM). Finally, the Boruta-LightGBM strategy was proven to be the most effective in predicting soil electrical conductivity (EC), with a coefficient of determination ( R 2 ) of 0.72 and a Root Mean Square Error (RMSE) of 12.49 ds/m. The experimental results show that the red-edge band index is the foremost variable in predicting soil salinity, succeeded by the salinity index and soil attribute data, while the topographic index has the least influence, which further demonstrates that proper variable selection could significantly improve model functionality and predictive precision. Furthermore, the Multiscale Geographically Weighted Regression (MGWR) model was utilized to reveal the influence and temporal-temporal-spatial heterogeneity of environmental factors such as soil organic carbon (SOC), precipitation (PRE), pH value, and temperature (TEM) on soil EC. This research offers not just a viable methodological framework for monitoring soil salinization but also new perspectives on the environmental drivers of soil salinity changes, which have implications for sustainable land management and provide valuable information for decision-making in soil salinity control and mitigation efforts. [ABSTRACT FROM AUTHOR]

Comprehensively and objectively revealing the spatial relationship between ecosystem services (ESs) and human activity intensity (HAI) is vital for achieving sustainable development goals. However, existing studies still have an incomplete understanding of how ESs change with HAI gradients. Therefore, taking Shenzhen City, China, as an example region experiencing rapid urbanization, the distribution of ESs and HAI in 2010 and 2020 were quantified using the InVEST model and the human footprint index method; at the same time, the gradient perspective was introduced and the spatial and temporal correlation characteristics of ESs along 10 HAI gradient bands, from weak to strong, were captured by applying multiscale geographically weighted regression (MGWR) and the bivariate spatial autocorrelation model. The findings showed that (1) the HAI demonstrated an increasing trend (20.63 (2010) and 23.36 (2020)), and the area with high values of HAI (the 10th gradient band) was distributed in the western part of the study area; meanwhile, the area with low HAI values (the 1st gradient band) was more distributed in the eastern part of the study area. (2) On the whole, the average levels of water conservation, soil conservation, carbon storage, and habitat quality decreased from 2010 to 2020; the spatial distribution characteristics of these parameters were similar. (3) In general, ESs were negatively correlated with HAI, and the negative correlation ratio was more than 65%. At the same time, the spatial and temporal correlations between ES patterns and HAI under different gradient bands were significant. These findings can effectively alleviate the pressure on the ecosystem caused by human activities, which is of great significance for the sustainable development of highly urbanized regions. [ABSTRACT FROM AUTHOR]

Opportunity youths (OY) are youths between 16 and 24 years of age who are neither in school nor working. Research suggests that youth disconnection can have negative consequences on youths' well-being, their communities, and the economy at large. The existing literature however neglects the underlying socio-demographic and economic factors that systematically contribute to the cause of youth disconnection in the United States. This study aims to explore the characteristics associated with youth disconnection and their spatially varying effects in different regions across the contiguous United States. Using data from the 2015 to 2019 5-year American Community Survey Public Use Microdata Sample, results suggest that areas with higher percentages of OY are located in the Sunbelt region (e.g., southern California, Arizona, New Mexico, Texas, Louisiana, Alabama, and Georgia). Moreover, a multiscale geographically weighted regression (MGWR) analysis illustrated spatially varying relationships with disconnection, demonstrating that factors have unique effects across different geographic areas. The potential impact of intersectionality for OY by race and ethnicity, ability, nationality, language, income, and parenting status is discussed. These results highlight the influence of local contexts in creating and reinforcing youth disconnection as well as informing responses to local support services for OY. [ABSTRACT FROM AUTHOR]

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]

Accurately identifying and obtaining changes in ecosystem drivers and the spatial heterogeneity of their impacts on ecosystem services can provide comprehensive support information for ecological governance. In this study, we investigate the changes in the relationship between human and natural factors and water-related ecosystem services (WESs) in different sub-watersheds across various time periods, focusing on four aspects: single-factor effect, nonlinear effect, interactive effects, and spatial characteristics. Taking the southern basins, which have complex topographic, climatic, and economic characteristics, as a study area, the study area was divided into four sub-basins with different characteristics. WESs of water yield, soil conservation, and water purification were quantified using the InVEST model for five periods from 2000 to 2020, and the OPGD and MGWR models were integrated to assess the impacts of 15 factors on WESs and their spatial characteristics. The results show the following: (1) After comparing the data over multiple time periods, climate factors such as precipitation (0.4033) are the primary factors affecting WESs in the southern basins, and human factors such as construction area (0.0688) have a weaker influence. The direct impact of human factors on WESs is not significant in the short term but increases over time. (2) Different sub-watersheds have different impacts on WESs. For instance, human activity intensity (0.3518) is a key factor affecting WESs in the Inward Flowing Area, while precipitation is the primary factor influencing WESs in other sub-watersheds. (3) Influencing factors and WES changes are often nonlinearly correlated; however, once a certain threshold is exceeded, they may have adverse impacts on WESs. (4) When a single factor interacts with other factors, its explanatory power tends to increase. (5) Compared to traditional methods, the estimation accuracy of MGWR is higher. Intense human activities can adversely affect WESs, while abundant precipitation creates favorable conditions for the formation of WESs. Therefore, integrating long-time-series multi-remote sensing data with OPGD and MGWR models is suitable for identifying and analyzing the driving mechanisms of human and natural factors that influence changes in WESs. Against the backdrop of global change, elucidating the driving factors of ecosystem services can provide crucial insights for developing practical policies and land management applications. [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]

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR framework's predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies. [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 Tibetan Plateau, characterized by complicated geological structures, is sensitive to climate change and human activities. The vulnerable environmental balance of the Tibetan Plateau has witnessed substantial alterations, emphasizing the urgency to comprehend and protect its ecosystem quality. In this study, we aim to quantify the ecosystem quality of the Tibetan Plateau, portray its spatiotemporal dynamics, and analyze the spatial heterogeneity of climatic and anthropogenic influences. We employed the entropy weight method (EWM) to construct a comprehensive index, termed the Ecosystem Quality Comprehensive Assessment Index (EQCAI). The historical spatiotemporal patterns and trends of EQCAI were subsequently identified. Our results revealed a gradient of decreasing ecosystem quality from the southeast to the northwest of the Tibetan Plateau, while the mitigation of ecosystem quality degradation displayed an improving trend from the southwest to the northeast. We further detected the spatial heterogeneity of factors influencing changes in ecosystem quality through the multiscale geographically weighted regression (MGWR) method. Precipitation emerged as the primary positive factor, exerting a profound influence on the ecosystem quality of the southeastern Tibetan Plateau. Conversely, evapotranspiration, elevation, and human activities negatively affected the western Tibetan Plateau. This study proposes targeted conservation efforts for forestry regions with high ecosystem quality in the southeastern Tibetan Plateau. Additionally, we suggest that the positive impact of human activities on the ecosystem quality of the Tibetan Plateau should be further strengthened. Our findings contribute valuable insights for informed decision-making in the sustainable management and preservation of the Tibetan Plateau's fragile ecosystem.

Abstract Background Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. Methods Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. Results Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p

Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

Identifying water quality parameter concentrations and their drivers is important for the prevention and control of water environment pollution. In this study, we constructed an inverse model of water quality parameters based on measured water quality parameters and remote sensing spectral data for this study area using artificial neural networks. We investigated the water environment response of the urban water network in the Pearl River Delta under the influence of typhoon rain events and explored their spatial heterogeneity using a multiscale geographically weighted regression model. The results indicate that in regions with a high level of urbanization, the dissolved oxygen (DO) concentration in river water is lower, and the ammonia nitrogen (NH3-N) concentration is higher. Under the influence of typhoon rain events of varying intensities, the response of water quality parameters in the urban water network of Zhongshan City varies. The intensity of rainfall determines the impact of typhoon rain events on water quality parameter concentrations. Our results are expected to improve the understanding of water quality trends under the influence of typhoon rain events and help policymakers and planners better develop water environment control strategies during typhoon transit. HIGHLIGHTS We first constructed a water quality parameter inversion model based on measured water quality parameters using artificial neural networks.; We analyzed the nonstationary spatial relationship between rainfall and water quality trends of typhoon rain events based on the multiscale geographic weighted regression model.; Our results are expected to improve the understanding of water quality trends under the influence of typhoon rain events.;

Energy justice is violated when particular customers and locations are excluded from a variety of urban energy service distribution. This study explores energy justice in terms of natural gas distribution by providing empirical evidence from 356 neighborhoods of Izmir Metropolitan Area (IMA). The aim is to reveal driving factors of natural gas investment and the spatial reflections of the relationships between investment, and socio-economic and physical characteristics. A global regression model, OLS, and two local spatial regression models, GWR and MGWR, are conducted. Population, income, employment and disadvantaged areas are the significant determinants of natural gas investments. Due to the presence of spatial autocorrelation in OLS residuals, GWR and MGWR are utilized to account for spatial variation in the response variable. Local models are superior to the global model according to AICc values, which are 607.3, 586.7, and 558.5, in OLS, GWR, and MGWR, respectively. MGWR further improves the overall fit with higher R-sq and lower AICc values. The local R-sq values indicate at least 70% variability is explained in 85% of the study area in MGWR, and in 73% of IMA in GWR. Parameters are slightly overestimated in GWR at the mean level. None of the local models are subject to multicollinearity according to local condition numbers, but local variance decomposition proportions indicate the effects of multicollinearity in some observations. Spatial modeling of investments helps to demonstrate local variations of energy injustice and to develop site-specific policies. Population and employment are related to potential customers, which lead to higher investments. Income depends on purchasing power, and there are economic barriers that need to be regulated through subsidies and incentives for the low-income households. Awareness raising policies can also be developed to better inform households about energy alternatives. Disadvantaged areas, either declared as urban transformation areas or currently under urban transformation, lack the natural gas service in general, while the investments can be considered in those areas along with the new development plans.

In 2020, Arizonans approved Proposition 207, the Smart and Safe Arizona Act, which legalized recreational marijuana sales. Previous research has typically used non‐spatial survey data to understand marijuana legalization voting patterns. However, voting behavior can, in part, be shaped by geographic context, or place, which is unaccounted for in aspatial survey data. We use multiscale geographically weighted regression to analyze how place shaped Proposition 207 voting behavior, independently of demographic variations across space. We find significant spatial variability in the sensitivity of voting for Proposition 207 to changes in several of the predictor variables of opposition and support for recreational marijuana legalization. We argue that local statistical modeling approaches provide a more in‐depth understanding of ballot measure voting behavior than the current use of global models. Related Articles: Branton, Regina, and Ronald J. McGauvran. 2018. "Mary Jane Rocks the Vote: The Impact of Climate Context on Support for Cannabis Initiatives." Politics & Policy 46(2): 209–32. https://doi.org/10.1111/polp.12248. Brekken, Katheryn C., and Vanessa M. Fenley. 2020. "Part of the Narrative: Generic News Frames in the U.S. Recreational Marijuana Policy Subsystem." Politics & Policy 49(1): 6–32. https://doi.org/10.1111/polp.12388. Fisk, Jonathan M., Joseph A. Vonasek, and Elvis Davis. 2018. "'Pot'reneurial Politics: The Budgetary Highs and Lows of Recreational Marijuana Policy Innovation." Politics & Policy 46(2): 189–208. https://doi.org/10.1111/polp.12246. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. Methods: Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. Results: Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access). Conclusions: The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations. Highlights: Diabetes-related hospitalization rates exhibited marked variation at the local level, which may be masked in investigations of larger geographic units. Hospitalization rates can be a useful indicator of diabetes outcomes at the local level, particularly in states or countries without population-level data from disease registries and/or spatially representative health surveys. This is the first study to use multiscale geographically weighted regression (MGWR) to investigate determinants of diabetes-related hospitalization rates at the local level. Strengths of associations between determinants and hospitalization rates varied based on geographic location within the study area. This information is useful to guide targeted resource allocation and needs-based health planning, and MGWR can be employed in other study areas to investigate spatially-variable determinants of diabetes-related outcomes. Associations between the identified determinants and diabetes-related hospitalization rates exhibited spatial heterogeneity at local, regional, and statewide levels. This information can serve policymakers and public health planners by suggesting the spatial scale at which a given intervention strategy should be implemented. Future ecological studies should consider spatial scale as well as geographic location when investigating determinants of diabetes and other chronic disease outcomes. [ABSTRACT FROM AUTHOR]

Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing. [ABSTRACT FROM AUTHOR]

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.

Voter turnout is an essential aspect of elections and often reflects the attitude of a country’s population towards democracy and politics. Therefore, examining the distribution of voter turnout and determining the factors that influence whether or not people will vote is crucial. This study aims to find significant factors that underlie the different levels of electoral participation across regions in Slovakia during the 2020 parliamentary elections. In this interpretation, special attention is paid to the ability of the main theories of voter turnout to explain the behaviour of Slovak voters. The primary analytical tool is multiscale geographically weighted regression, which represents an advanced local regression modelling variant. The results indicate that the multiscale geographically weighted regression is superior to the global ordinary least square model in virtually all aspects. Voter turnout is generally higher in economically and socially prosperous localities and regions, which is in line with the societal modernisation theory. Additionally, factors connected to mobilisation theory and the concept of ‘left behind places’ also proved to be valuable. However, in other cases, such as with the share of retirees and potential habitual voting, the outcomes were not overly convincing, and further research is required.

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities.

Urban agglomerations (UAs) are the main battlefield of urbanization and the most concentrated areas of carbon emissions (CEs). Nevertheless, limited studies have examined the impact of urbanization level (UL) on CEs in UAs in China. This study aimed to identify the spatial relationship between UL and CEs in Chinese UAs and to conduct a comprehensive analysis of the differences in CEs caused by urbanization. The findings would provide scientific support for the China’s dual-carbon goals and the achievement of green and low-carbon urban development. Spatial variations in UL and CEs in 19 Chinese UAs were assessed in 2000, 2010, and 2020 using distribution dynamics and spatial regression models. The results indicated that the UL of UAs in China evidently increased over time, and UAs contributed approximately 80% of the national CEs. Significant spatial dependence was identified between urbanization factors and CEs. The regression results indicated that an increase in UL promoted the growth of CEs, and the form of the urban land had a significant and highly variable impact on CEs. Our findings provide a valuable case study for exploring relationships between UL and CEs in other UAs worldwide.

Evaluating the potential impacts of land use and land cover change (LULCC) and climate change on air pollution is crucial to unravel the driving forces and mechanisms behind changes in air quality. A multi-faceted approach was adopted, including a land change model (LCM) and Mann–Kendall (MK) test, to evaluate the transition of land cover type, changes in climate, and atmospheric pollutants during 2004–2021 in Pakistan. Moreover, a multiscale geographically weighted regression (MGWR) model and a mathematical model were used to assess the potential contribution of LULCC and climate dynamics to atmospheric pollution. It was revealed that during 2004, croplands covered an area of 9.72 × 104 mile2, accounting for 38% of the total area. However, the area of the croplands increased to 10.1 × 104 mile2, accounting for 40% of the total area in 2021. The MK test showed that the north and west–south regions significantly experienced air pollution, with the increasing trend for nitrogen dioxide (NO2) and sulfur dioxide (SO2) being 0.89× 1015 molecules/cm2 per year and 0.54 DU/year, respectively. For climate variability, mean precipitation (Precp) and mean surface pressure (SP) showed a prominent increasing trend, with a maximum value of 1 mm/year and 0.01 Kpa/year, respectively. The mean temperature maximum (Tmax) showed an increasing and decreasing trend, with the highest value of 0.28°C/year and 0.08°C/year, respectively. In the context of contribution, the conversion of cropland to grasslands increased the trend for SO2 concentrations. The highest increasing trend of 1.5 DU for ozone (O3) was found due to conversion of grasslands to shrublands. Additionally, regional climate played a significant role in making air pollution stagnant across the country. Precp and wind speed (WS) contributed significantly in escalating NO2 concentrations in Pakistan, while Precp contributed most (0.004 DU) to increasing SO2 concentrations. For O3, the most influential climate factor was Precp. These results on a long-term temporal scale demonstrated how maintaining climate variability through comprehensive land use management can help improve ambient air quality in Pakistan.

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.

The major emphasis of this study is showing the district-level inequality in the Standard of Living (SoL) in India. It shows the spatial effects and distribution aspect of SoL in India using various spatial econometric techniques e.g. Moran's I, LISA, spatial autoregressive model, GWR model and MGWR model to understand spatial inequality in India. Three clusters of districts having high SoL formed in North-western India, Western India and Southern India. These clusters formed due to urbanization, the spread effect of Delhi, the Green revolution in Punjab, the international trade link of Gujarat and Punjab, and impressive social sector development in southern India. The clusters of districts having low SoL mainly formed in the central, eastern and north-eastern parts of India. These are the area dominated by tribal communities having low socio-economic conditions and rural and agricultural populations with a severe resource-population mismatch. However, the Multiscale spatial regression supports that the level of urbanization, workforce structure, human capital, gender empowerment and group identity operates at a different geographic scale in determining the spatial heterogeneity of SoL. This study suggests that the government should focus on the lagging region and that policy responses should be cognizant of the multiple shades of spatial variation. [ABSTRACT FROM AUTHOR]

This study takes 1597 key villages and towns part of rural tourism in China as research objects and uses the ArcGIS spatial analysis method, combined with the geodetector and the multiscale geographically weighted regression model, to analyze the intensity and spatial differentiation of factors influencing the spatial distribution of these villages and towns. (1) The key villages and towns of rural tourism exhibit clustering distribution patterns with more locations in the east than in the west. The center of gravity of this distribution shifts to the northwest, displaying a "belt–point" trend with scattered hot spots in Beijing, Tianjin, Hebei, and the Yangtze River Delta, while cold spots are primarily concentrated in the northwest. (2) Spatial variation in the key villages and towns of rural tourism is due to multiple factors, of which population density, resident consumption expenditure, and per capita GDP display the highest explanatory powers for the spatial distribution of tourism. (3) The primary influencing factors are spatial differences in their roles and substantial local imbalances. Positive and negative correlation analysis units exhibit the aggregation characteristics of being blocked and banded. These results can provide valuable guidance for the development of rural tourism, promoting its sustainable development and contributing to the revitalization of rural areas. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has played an important role in the achievement of the global CO2 emissions mitigation goal. The monitoring and analysis of CO2 emissions in the Yangtze River Economic Belt (YREB) urban agglomerations is strategic to the carbon peak and carbon neutrality in China. In this paper, we revealed the spatial and temporal variations of CO2 emissions in Cheng-Yu urban agglomeration (CY-UA), Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and Yangtze River Delta urban agglomeration (YRD-UA) in YREB and investigated the carbon emission development stage of YREB urban agglomerations. Particularly, a carbon emission development stage framework that considered the relationship between economic growth and carbon emissions was built based on Environmental Kuznets Curves (EKCs). Meanwhile, multiscale geographically weighted regression (MGWR) was used to analyze the impact of different influencing factors, including population (POP), GDP per capita (GDPPC), the proportion of secondary industry (SI), carbon emission intensity (CI), and urbanization (UR), on the CO2 emissions of three urban agglomerations. The results illustrate the following: (1) The CO2 emissions of YREB urban agglomerations decreased, with YRD-UA having the highest CO2 emissions among the three urban agglomerations and contributing 41.87% of YREB CO2 emissions in 2017. (2) CY-UA, YRMR-UA, and YRD-UA reached the CO2 emissions peak in 2012, 2011, and 2020, respectively, all of which are at the low-carbon stage. (3) POP and GDPPC show the greatest impact on the CO2 emissions of the three YREB urban agglomerations. [ABSTRACT FROM AUTHOR]

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.

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]

The association between rural public services (RPSs) and population migration (PM) has become a key aspect of rapid urbanization in developing countries and an important breakthrough for improving rural–urban relations. An in-depth analysis of the heterogeneity of the weakening effect of RPSs on PM at different transformation phases and the internal mechanism of the evolution of association patterns driven by RPSs and PM helps to ensure better co-ordinated urban and rural development. This paper establishes an interactive analysis framework for measuring the spatiotemporal association and regional differences between RPSs and PM in the Sichuan–Chongqing region (SCR), and reveals the influence mechanism by employing multiscale geographically weighted regression (MGWR). The results indicate that the association rapidly increased with clear spatial heterogeneity across topographic units and the weakening effect of RPSs on PM begin to diverge during the urban–rural transition. The natural, economic, social, and urban–rural disparity factors in terms of the association exhibit significant spatial variability. In mountainous areas, where topography dominates, RPSs fail to effectively weaken rural migration. However, in the plain areas, urbanization is the main driver of urban–rural transition, and the adaptive upgrading and transformation of RPSs has made their weakening effect stronger, thus alleviating rural exodus and increasing population concentration. All these findings show that differentiated optimization strategies adhering to the association trends should be proposed for a deeper integration of rural revitalization and new urbanization in the SCR. [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]