Geographically Weighted Regression Research Articles

Analysis of the Evolution of Spatio Temporal Pattern of Rural Industrial Integration and its Influencing Factors

Regional development has a significant impact on Rural Industrial Integration (RII), which substantially boosts economic growth in rural regions and decreases the economic disparity between rural regions and urban areas. Addressing the Spatio-Temporal Patterns (STP) of RII and the factors that impact these developments is essential for today's economies to attempt balanced regional development successfully. The objective of the present study is to investigate the STP of RII during time considering Zhejiang Province, China, as a case study. The present research examines the primary social, economic, and environmental variables that result in RII applying spatial economic frameworks like Adaptive Geographically Weighted Regression (AGWR) and Multiscale Geographically Weighted Regression (MGWR). The research study evaluated how they relate and impact these factors to integration across multiple spatial scales. With AGWR and MGWR values achieving 0.0083 and 0.0085, respectively, the study indicated that the most significant variable determining RII is the development of urban infrastructure. Significant grouping impacts have been shown by the spatial autocorrelation (Moran's I) for this metric, which attained values that were as high as 0.4205. Significant variables comprised the cost of investment and the urban-rural per capita disposable income (PCDI) proportion, with PCDI ratio ratios of 0.0053 (AGWR) and 0.0056 (MGWR), respectively.

Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Under global warming, surface urban heat islands (SUHI) threaten human health and urban ecosystems. However, scant research focused on exploring the complex associations between urban form factors and SUHI at the county scale, compared with rich studies at the city scale. Therefore, this study simultaneously examined the nonlinear and spatial non-stationary association between SUHI and urban form factors (e.g., landscape structure, built environment, and industrial pattern) across 2321 Chinese counties. An explainable spatial machine learning method, combining the Geographically Weighted Regression, Random Forest, and Shapley Additive Explanation model, was employed to deal with nonlinearity, spatial non-stationary, and interpretability of modeling. The results indicate the remarkable spatial disparities in the relationship between urban form factors and SUHI. Landscape structure contributes the most in southern counties, while the built environment is more important in northeastern counties. The impact of building density and building height increases with the county size and becomes the main driver of urban heat in mega counties. Most urban form factors exhibit nonlinear impacts on SUHI. For example, urban contiguity significantly affects SUHI beyond a threshold of 0.93, while building density does so at 0.17. By comparison, the influence of shape complexity remains stable above a value of 7. Factors such as industrial density and diversity have a varied influence on SUHI between daytime and nighttime. The results of local explanations and nonlinear effects provide targeted regional mitigation strategies for urban heat.

A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities

Assessing the spatial distribution of heavy metals in soil is essential for mitigating risks to human health and ensuring the sustainable use of soil resources. This study proposed a geographically weighted neural network (GWNN) model, leveraging deep learning and geographically weighted regression (GWR). The model was designed based on the GWR concept to address the spatial autocorrelation of copper (Cu) in soil and incorporated convolutional neural network (CNN) to capture the nonlinear relationships between Cu and environmental covariates. The GWNN model was compared with GWR, extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. XGBoost was employed to select important environmental covariates and spatial autocorrelation of Cu concentrations was assessed using Moran’s I. The model’s performance was evaluated using 10-fold cross-validation, and prediction uncertainty was quantified with 100 bootstrap models. The results indicated that temperature covariates were the most significant predictors of soil Cu concentrations. The R2 values for Cu prediction accuracy were 0.60 for GWNN, 0.53 for ANN, 0.49 for XGBoost, and 0.32 for GWR. The spatial distribution of Cu showed a trend of higher concentrations in the north and lower concentrations in the south, consistent with spatial clusters identified by local Moran’s I. The mean uncertainty of the 90% confidence interval for GWNN was 16.49%, closely aligning with XGBoost (15.44%) and ANN (16.29%) and significantly outperforming the GWR (18.25%). Overall, the GWNN model demonstrated strong predictive accuracy and low uncertainty, offering an improved approach for digital soil mapping applications.

Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Spatial Variation of Pollutant (NO2, SO2 & CO) and Its Impact Factors in Jakarta: An Application of Sentinel-5P Products

Abstract This study investigates the impact of transportation, manufacturing, and energy sectors on air quality in Jakarta using Geographically Weighted Regression (GWR) and remote sensing data. Utilizing datasets from Sentinel-5P for NO2, SO2, and CO concentrations, and predictor variables including road density, industrial activity, and energy production, the research aims to quantify the spatial heterogeneity of air pollution. The findings reveal significant spatial variations in pollutant concentrations across Jakarta and its neighbouring provinces, Banten and West Java, during the dry and wet months of 2023. High NO2 levels were primarily linked to dense traffic in central Jakarta, while elevated SO2 and CO concentrations were associated with industrial and energy activities in the peripheral regions. Seasonal analysis indicated that meteorological factors play a crucial role in pollutant dispersion. The integration of remote sensing data with GWR provided a robust framework for environmental monitoring, addressing gaps in traditional air quality observation methods. This research contributes to the broader understanding of urban air pollution dynamics and offers a replicable model for other regions facing similar challenges.

Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity

Data-fusion techniques leverage the strengths of multisource precipitation data and can significantly enhance the accuracy of precipitation estimates. However, the extent to which these techniques improve precipitation estimates (i.e., added value) compared to interpolation algorithms and the factors driving this improvement remain unclear. To address these gaps, this study compared the performance of two merging techniques, i.e., double machine learning (DML) and geographically weighted regression (GWR), with multiple interpolation algorithms in estimating precipitation across China. The interpolation algorithms vary in complexity and include typical methods (IDW and Kriging), semi-physical methods (GIDS, DAYMET, and MicroMet), and climatologically aided interpolation (CAI). We quantified the added value of the merging techniques over these interpolation algorithms and investigated the driving factors using a data-driven approach. Results indicate that the merging techniques outperform all the interpolation algorithms, regardless of their complexity. The merging techniques provide greater added value in gauge-scarce regions (e.g., Northeast China) than in gauge-rich regions (e.g., Northwest China). The magnitude of the added value from merging techniques is significantly influenced by the choice of interpolation algorithms due to their varying performance. Additionally, our data-driven model reveals that factors such as the amount of precipitation, number of wet days, performance of precipitation products, and gauge density are key drivers that negatively affect the added value of merging techniques. This study highlights the importance of integrating multisource data to improve precipitation estimates, especially in regions with sparse gauges, rather than relying solely on gauge-only interpolation.

Diagnosis of ecological security and the spatial heterogeneity of its driving factors in the mining-impacted watershed, based on ecosystem health-risk-services framework

A comprehensive diagnosis of ecological security (ES) and its driving mechanisms in the watershed under mining influence is essential for the conservation and restoration of watershed ecosystems. Few studies have comprehensively evaluated ES by considering the condition of the ecosystem itself, the ecological function it provides, and the risk it faces. Therefore, by innovatively synthesizing ecosystem health (EH), ecological risk (ER), and ecosystem services (ESs), an ES evaluation framework based on EH-ER-ESs was constructed. Based on quantifying the ES of a typical watershed with mines clustered in China’s Northern Sand Prevention Belt, a spatial correlation analysis was used to elucidate the spatial differentiation characteristic of ES and verify the necessity of the evaluation framework. The driving mechanism of ES and the spatial heterogeneity of its key driving factors were explored using the Geodetector model and the geographically-weighted regression model. The results show that (1) ES was generally medium-security, and very low-security areas (8.16%) were mainly concentrated in the eastern mining aggregation areas. (2) ES showed high-high and low-low spatial clusters. Overall, ES was positively correlated with EH and ESs, while negatively correlated with ER, but there were individual cases of “high health-low security” and “high services-low security”, and combining EH, ER, and ESs to diagnose ES comprehensively was necessary. (3) Drought index, vegetation cover, distance from mining land, population density, and CONTAG were the key driving factors of ES. The explanatory power of factor interactions was higher than that of single factors. The impact of driving factors showed significant spatial heterogeneity, with the effects of mine agglomeration on ES primarily concentrated in the east. The EH-ER-ESs framework can be used for ES evaluation in other ecosystems, and the findings provide important guidance for conducting integrated watershed management.

Decoding Socio-Economic Demographic Trends: The Power of Spatial Econometrics and Geographic Analysis

Greece is experiencing a steady population decline caused by the declining migratory and natural balance. This research investigates the spatial impact of socio-economic and demographic factors on the natural population balance in Greece for the spatial zoning of municipal administrative units. Using geographically weighted regression (GWR) on data from the 2011 Greek census, the research explores the local impacts of factors like housing repair permits, vacant housing, employment rates, population inflows, distance from regional centers, aging, gender ratios, and education levels. An initial ordinary least squares (OLS) regression was conducted, revealing significant spatial variation and emphasizing the necessity of spatial econometric methods. The GWR model proved to be more effective in accounting for the variance in the data, removing spatial autocorrelation and revealing high local variation. Results show the high negative impact of the aging index in Western Greece and the Ionian islands, the counterintuitive positive effect of the gender ratio in urban areas, and the positive influence of population inflows in high-migration regions like Northern Greece and Crete. The results of this study underline the need to utilize spatial econometric methods for a precise and detailed understanding of demographic trends and provide valuable insights for localized strategies to address demographic challenges.

A Geographically Weighted Regression–Compute Unified Device Architecture Approach to Explore the Spatial Agglomeration and Heterogeneity in Arable Land Consumption in Southwest China

Arable land loss has become a critical issue in China because of rapid urbanization, industrial expansion, and unsustainable agricultural practices. While previous studies have explored the factors contributing to this loss, they often fall short in addressing the challenges of spatial heterogeneity and large-scale dataset analysis. This research introduces an innovative approach to geographically weighted regression (GWR) for assessing arable land loss in China, effectively addressing these challenges. Focusing on Chongqing, Guizhou, and Yunnan Provinces over the past two decades, it examines spatial autocorrelation with R-squared values exceeding 0.6 and residuals. Eight factors, including environmental elements (rain, evaporation, slope, digital elevation model) and human activities (distance to city, distance to roads, population, GDP), were analyzed. By visualizing and analyzing R² spatial patterns, the results reveal a clear spatial agglomeration distribution, primarily in urban areas with industries, highly urbanized cities, and flat terrains near rivers, influenced by GDP, population, rain, and slope. The novelty of this study is that it significantly enhances GWR computational capabilities for handling extensive datasets by utilizing Compute Unified Device Architecture (CUDA) on a high-performance GPU cloud server. Simultaneously, it conducts comprehensive analyses of the GWR model’s local results through visualization and spatial autocorrelation tools, enhancing the interpretability of the GWR model. Through spatial clustering analysis of local results, this study enables targeted exploration of factors influencing arable land changes in various temporal and spatial dimensions while also evaluating the reliability of the model results.

Spatially Explicit Analysis of Landscape Structures, Urban Growth, and Economic Dynamics in Metropolitan Regions

Assuming that settlement morphologies and landscape structures are the result of economic transformations, the present study illustrates a statistical framework investigating metropolitan growth due to the inherent changes in landscape configurations vis à vis socio-demographic functions. Focusing on the evolution of their spatial drivers over time, metropolitan development was studied by adopting land parcels (or ‘patches’, as they are referred to in the ecological literature) as the elementary analysis unit—with the individual surface area and a specific shape indicator as the dependent variables and background socioeconomic attributes as predictors of landscape change over time. We specifically ran a Multiscale Geographically Weighted Regression (MGWR) testing the spatial dependence of the size and shape of landscape parcels on a vast ensemble of socioeconomic factors in a dense region (metropolitan Athens, Greece) with natural landscapes exposed to increasing human pressure. To investigate the spatial direction and intensity of the settlement expansion and landscape change, local regressions using the parcel area and fractal index (perimeter-to-area ratio) as the dependent variables and the elevation, distance from selected economic nodes, transport infrastructures, and natural amenities as the predictors were run separately for 1990 and 2018, representative of, respectively, a mono-centric configuration and a moderately polycentric organization of economic spaces. In a strictly mono-centric setting (1990), the parcel size showed a linear dependence on the distance from business districts, elevation, and wealth. Changes in the relationship between the parcel size and spatial (economic and non-economic) drivers may suggest a latent process of settlement de-concentration, and a possible shift toward polycentric development (2018), as documented in earlier studies. By integrating socioeconomic and ecological dimensions of landscape analysis and land evaluation, the empirical results of this study outline the increased complexity of dispersed landscape structures within dense metropolitan regions and along urban–rural gradients in Europe.

Geographical Variation in the Associations Between School Characteristics and Homophobic Bullying: a Contextual Analysis.

Homophobic bullying constitutes a serious threat to adolescent well-being and could be understood as an ecological phenomenon, influenced by diverse school, regional, and community contexts. This study examined geographic variations in the relationship between school characteristics and homophobic bullying. Data from 2244 California schools, including student surveys and administrative records, were combined and analyzed using geographically weighted regression (GWR). Results showed that the associations between school characteristics and general victimization were consistent across geographic areas. However, when it came to homophobic bullying, the relationships with school characteristics varied significantly based on location. Notably, regions with high intolerance, urbanity, large school size, and small student-teacher ratios appeared to offer protection against bullying. Additionally, student socioeconomic status influenced bullying in disadvantaged rural schools with limited Gay-Straight Alliance (GSA) presence. Ethnic diversity also played a role, with low diversity or dominance of two ethnic groups linked to higher bullying rates.

Geospatial determinants of urban poverty in Nigeria: an analysis of locally weighted factors

ABSTRACT Determining the factors that influence poverty has been seen as one of the ways of identifying appropriate poverty alleviation strategies in cities. This has been done, in most cases, through the application of ordinary least squares (OLS), which assumes homogeneous influence without considering the spatial heterogeneity that may occur in this influence. This study aims to determine the spatial heterogeneity in the influence of public infrastructure, economic, environmental, and social factors on poverty, by carrying out comparison analysis using OLS and Geographically Weighted Regression (GWR) in ArcGIS Pro 3.0.1, with a view of proffering poverty alleviation strategies for each section of an urban area. A questionnaire in Geographic Open Data Kit (GeoODK) was administered to 366 urban residents across the twenty wards in the selected city in Nigeria. The results reveal that factors that influence income poverty are spatially varying in direction and weight across a city. Spatial heterogeneity of poverty’s determinants should be considered in the formulation and implementation of effective poverty alleviation policies and programs.

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

Impact of Typical Land Use Expansion Induced by Ecological Restoration and Protection Projects on Landscape Patterns

Land use and land cover (LULC) changes driven by ecological restoration and protection projects play a pivotal role in reshaping landscape patterns. However, the specific impacts of these projects on landscape structure remain understudied. In this research, we applied geographically weighted regression (GWR) to analyze the spatial relationships between typical land use expansion and landscape pattern characteristics in the Lesser Khingan Mountains–Sanjiang Plain region between 2017 and 2022. Our results indicate three key findings: (1) Significant spatial heterogeneity exists in the relationship between landscape patterns and land use expansion, which varies across geographic locations; (2) Ecological restoration projects generally reduce fragmentation, dominance, and heterogeneity while enhancing connectivity, particularly in forest and farmland regions. However, excessive land use expansion in certain areas may reverse these positive effects; (3) Landscape complexity increases in high-altitude mountainous regions due to land use expansion but decreases in plains, particularly in forest-to-farmland conversions. These findings provide new insights into how landscape patterns respond to ecological restoration efforts and offer actionable guidance for improving future land use planning and policy decisions. Our study highlights the need to consider local geomorphological factors when designing ecological projects, ensuring that restoration efforts align with regional landscape dynamics to maintain landscape integrity.

Geographically Weighted Regression of Maternal Human Capital on Nutrition of Stunted Toddlers in Tin and Non-Tin Minning Areas

The perspective of maternal human capital on stunted toddler nutrition has a different spatial relationship between tin mining and non-tin mining areas. This study will look at which spatial patterns of maternal human capital influence the nutrition of stunted children under five in tin-mining villages. This study uses primary data on 346 stunted children under five in Bangka Belitung Islands Province, Indonesia. The analysis used geographically weighted regression (GWR) by dividing mining and nonmining areas of Bangka Island and Belitung Island. Maternal human capital that spatially influences the nutrition of stunted toddlers in tin mining and non-mining areas is maternal knowledge about nutritious food. Maternal knowledge of a clean and healthy environment has a negative effect on the nutrition of stunted children under five in the non-mining area of Bangka Island. Maternal education and skills to diversify fish food have an effect on the nutrition of children under five years old in Belitung Island tin mining area. Mothers' skills in diversifying fish food affect the nutrition of stunted children under five in 11 villages in the non-tin-mining area of Belitung Island.

Accessibility and Older and Foreign Populations: Exploring Local Spatial Heterogeneities across Italy

The interplay between accessibility and population change is a relatively new subject in Italian academic research. Along with social and economic factors such as regional economic prosperity, the ease of movement inside and outside an area can play a pivotal role in shaping population dynamics. This study seeks to explore the spatial distribution and spatial relationships of three indicators, including one related to real accessibility (RAI) and two others related, respectively, to the shares of the older population (SOP) and of the foreign population (SFP). An exploratory spatial data analysis is, therefore, conducted at the local level using Italian municipalities as the statistical units for the empirical analysis. Local univariate spatial autocorrelation analysis is used together with a regression analysis based on ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results provide valuable insights into the local heterogeneity that characterizes the distribution of each indicator and the local relationship between them, highlighting the importance of thinking locally in quantitative social sciences.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

Spatial variation of healthcare resource use in Wales following the COVID-19 pandemic

BackgroundThe COVID-19 pandemic caused significant disruption to healthcare services and changed patterns of healthcare resource utilisation in Wales, but the geographic variation of this disruption is not known. Objective/Approach We aimed to examine geo-spatial variation in the impact of the pandemic on healthcare service use, and identify socio-demographic and environmental factors associated with the geographical differences. We accessed and linked individual electronic healthcare records within the Secure Anonymised Information Linkage (SAIL) Databank. We calculated age-sex standardised rates of interactions across 1909 small areas in Wales, from January 2017 to December 2022. We compared rates in the 3 years pre-COVID (2017-2019) to the following 3 years (2020-2022) and investigated if spatial autocorrelation of results existed, using the Moran’s I test. We linked area-level characteristics and carried out Geographically Weighted Regression (GWR) analysis to understand the socio-demographic and environmental characteristics associated with ‘hot’ and ‘cold’ spots of healthcare resource use. Results We included 1,277,532 individuals with 7,502,485 healthcare service interactions related to a long-term condition. Across the 1909 small areas in Wales, we observed large variations in age-sex standardised rates of healthcare interactions. Area-level general practice and hospital admission rates decreased by a median of 22%, and 13.1% respectively, from 2017-2019 to 2020-2022. The change in general practice rates were more significantly spatially autocorrelated than the change in hospital admission rates. Spatially varying relationships between healthcare resource use and area-level characteristics such as income deprivation, travel time to general practice/hospital, have been analysed using GWR (results to be finalised). Conclusions/Implications We have found that the COVID-19 pandemic impacted population level healthcare resource use in some areas of Wales more than others. Identifying the underlying characteristics causing these differences will help inform policy to provide targeted services to areas and subgroups of the population.

Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model （GTWR） to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression （GWR） and ordinary least squares regression （OLS）, it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.