Search Results (182)

Introduction: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States (U.S.). Mississippi has the highest rate of CRC incidence in the U.S. and has large populations of black and white individuals, allowing for studies of racial disparities. Methods: We conducted an ecological study using the county as the unit of analysis. CRC incidence data at the county level for black and white populations in Mississippi, covering the years 2003 to 2020, were retrieved from the Mississippi Cancer Registry. Age-adjusted incidence rate differences and their corresponding 95% confidence intervals (CIs) were then calculated for these groups. Getis–Ord Gi* hot and cold spot analysis of CRC incidence rate racial disparities was performed using ArcGIS Pro. We used global ordinary least square regression and geographically weighted regression (MGWR version 2.2) to identify factors associated with racial differences in CRC incidence rates. Results: Age-adjusted CRC incidence rate in the black population (median = 58.12/100,000 population) and in the white population (median = 46.44/100,000 population) varied by geographical area. Statistically significant racial differences in CRC incidence rates were identified in 28 counties, all of which showed higher incidence rates among the black population compared to the white population. No hot spots were detected, indicating that there were no spatial clusters of areas with pronounced racial disparities. As a post hoc analysis, after considering multicollinearity and a directed acyclic graph, a parsimonious multiple regression model showed an association (β = 0.93, 95% CI: 0.25, 1.62) indicating that a 1% increase in food insecurity was associated with a 0.93/100,000 differential increase in the black–white CRC incidence rate. Geographically weighted regression did not reveal any local patterns in this association. Conclusions: Black–white racial disparities in CRC incidence were found in 28 counties in Mississippi. The county-level percentage of food insecurity emerged as a possible predictor of the observed black–white racial disparities in CRC incidence rates. Individual-level studies are needed to clarify whether food insecurity is a driver of these disparities or a marker of systemic disadvantage in these counties. Full article

An accurate assessment of grassland carbon stocks is essential for understanding their role in China’s terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in theoretical foundations, variable selection, and algorithmic implementation, introduces significant uncertainty into estimating grassland carbon density. This study focuses on the grassland ecosystems in Gansu Province, China, employing both an overall approach (without distinguishing between grassland types) and a stratified approach, classifying the grassland into seven distinct types: alpine meadow steppe, temperate steppe, lowland meadow, alpine meadow, mountain meadow, shrubby grassland, and temperate desert. Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). The applicability of each model was then systematically compared and analyzed. The main conclusions are as follows: (1) The overall estimation results demonstrate that the GWR model is the optimal choice for inverting AGBC, DOMC, and SOC, with coefficients of determination (R²) of 0.67, 0.60, and 0.92, respectively. In contrast, the MGWR model is best suited for BGBC, with an R² value of 0.73. (2) The stratified estimation results suggest that the optimal inversion models for AGBC and BGBC are predominantly the MGWR and RF models selected through the recursive feature elimination algorithm. For DOMC, the optimal model is a spatial model, while SOC is most accurately estimated using the GWR and RF models selected via the Boruta algorithm. (3) When comparing the inversion results of the optimal overall and stratified approaches, the stratified estimations of AGBC, BGBC, and DOMC (R² = 0.80, 0.78, and 0.73, respectively) outperformed those of the overall approach. In contrast, the SOC estimates followed an opposite trend, with the overall approach yielding a higher R² value of 0.92. (4) Generally, variable selection significantly enhanced model accuracy, with spatial and non-parametric models demonstrating superior precision and stability in estimating the four carbon density components of grassland. These findings provide methodological guidance for converting sample point carbon density data into regional-scale estimates of grassland carbon storage. Full article

Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article

The deployment of electric vehicle charging stations (EVCSs) is crucial for the large-scale adoption of electric vehicles and the sustainable energy development of global cities. However, existing research on the spatial distribution of EVCSs has provided limited analysis of spatial equity from the perspective of supply–demand relationships. Furthermore, studies examining the influence of the built environment on EVCS accessibility are scarce, and often rely on single methods and perspectives. To explore the spatial characteristics of EVCS accessibility and its influencing factors, using multi-source urban spatial data, this study initially employs the Gaussian two-step floating catchment area (G2SFCA) method to measure and analyze the spatial distribution characteristics of EVCS accessibility in Guangzhou, China, with consideration of supply–demand relationships. Subsequently, it integrates the MGWR and random forest (RF) models to comprehensively investigate the impact mechanism of the built environment on EVCS accessibility from the perspectives of spatial heterogeneity and non-linear relationship. The results show that the EVCS accessibility exhibits a “ higher in the west and lower in the east, with extreme core concentration” distribution pattern, and has significant spatial autocorrelation. The built-environment variables exhibit different scale effects and spatial non-stationarity, with widespread non-linear effects. Among them, the auto service, distance to regional center, and distance to subway station play important roles in influencing EVCS accessibility. These findings offer important guidance for the efficient and equitable layout of EVCSs in high-density cities. Full article

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations. Full article

This paper describes the use of Generalized Additive Models (GAMs) to create regression models whose coefficient estimates vary with geographic location—spatially varying coefficient (SVC) models. The approach uses Gaussian Process (GP) splines (smooths) for each predictor variable, which are parameterised with observation location in order to generate SVC estimates. These describe the spatially varying relationships between predictor and response variables. The proposed GAM approach was compared with Multiscale Geographically Weighted Regression (MGWR) using simulated data with complex spatial heterogeneities. The geographical GP GAM (GGP-GAM) was found to out-perform MGWR across a range of fit metrics and resulted in more accurate coefficient estimates and lower residual errors. One of the GGP-GAM models was investigated in detail to illustrate model diagnostics, checks of spline/smooth convergence and basis evaluations. A larger simulated case study was investigated to explore the trade-offs between GGP-GAM complexity (via the number of knots), performance and computational efficiency. Finally, the GGP-GAM and MGWR approaches were applied to an empirical case study. The resulting models had very similar accuracies and fits and generated subtly different spatially varying coefficient estimates. A number of areas of further work are identified. Full article

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design. Full article

In recent years, the digital creative industry has manifested a vigorous growth trend along with the continuous upgrading of the Internet and the leap of the national economy. This research identifies the spatial distribution characteristics of digital creative enterprises in Shenzhen, employs big data of spatial information of various facilities such as transportation and commerce as the driving factor to construct a model, takes 1 km grid as the fundamental research unit, and explores the influence mechanism of enterprise location selection through methods like OLS and MGWR. The results are as follows: (1) The overall spatial distribution characteristics of digital creative industry are characterized by “widely distributed throughout the city, with a high concentration within the customs and a weak dispersion outside the customs”. (2) The factors of park foundation, production service, public service and life service exert a significant influence on the spatial distribution of digital creative industries in Shenzhen. Among them, the density of shopping facilities, staff, hotel and bus station exhibits a highly obvious spatial heterogeneity in terms of the influence on enterprise location. (3) The correlation of local scale factors is high and the influence range is precise, which frequently presents complex correlation outcomes in small scales such as streets or communities. Full article

Atmospheric particulate pollution in China’s megacities has heightened public concern over air quality, highlighting the need for precise identification of urban pollution characteristics and pollutant transport mechanisms to enable effective control and mitigation. In this study, a new method combing the High Accuracy Surface Modeling (HASM) and Multiscale Geographically Weighted Regression (MGWR) was proposed to derive seasonal high spatial resolution PM_2.5 concentrations. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) was applied to analyze the seasonal spatial variations, transport pathways, and potential sources of PM_2.5 concentrations across China’s four megacities: Beijing, Shanghai, Xi’an, and Chengdu. The result indicates that: (1) the proposed method outperformed Kriging, inverse distance weighting (IDW), and HASM, with coefficient of determination values ranging from 0.91 to 0.94, and root mean square error values ranging from 1.98 to 2.43 µg/m³, respectively; (2) all cities show a similar seasonal pattern, with PM_2.5 concentrations highest in winter, followed by spring, autumn, and summer; Beijing has higher concentrations in the south, Shanghai and Xi’an in the west, and Chengdu in central urban areas, decreasing toward the rural area; (3) potential source contribution function and concentration weighted trajectory analysis indicate that Beijing’s main potential PM_2.5 sources are in Hebei Province (during winter, spring, and autumn), Shanghai’s are in the Yellow Sea and the East China Sea, Xi’an’s are in Southern Shaanxi Province, and Chengdu’s are in Northeastern and Southern Sichuan Province, with all cities experiencing higher impacts in winter; (4) there is a negative correlation between precipitation, air temperature, and seasonal PM_2.5 levels, with anthropogenic emissions sources such as industry combustion, power plants, residential combustion, and transportation significantly impact on seasonal PM_2.5 pollution. Full article

Detailed spatial planning serves as statutory guidance for regulating specific spatial functions, including public services, living conditions, and production spaces. It emphasizes meeting the comprehensive needs of the local population, making it crucial to understand the relationship between population distribution and the mix of various city functions, particularly in the era of urban regeneration. Therefore, this study utilized point-of-interest (POI) data representing land functions and population data to investigate these relationships via spatial analysis and Multiscale Geographically Weighted Regression (MGWR). Applied to the central urban area of Shanghai, the study reveals that the level of mixed land use and various functionalities affect population distribution at different adaptive scales. We also found a higher degree of functional mix does not always meet population needs. Although generally there is a positive correlation between functional mix and population distribution, they are not always closely bonded. The proposed method provides an efficient workflow for identifying the applicable scale of various functions to increase functional mix and attract the population, which can provide real-time evidence supporting detailed planning. Test results also reveal the less-considered space along the boundaries of administrative districts. We also found developing tools for detailed planning is an urgent need to facilitate cross-boundary cooperation and development, especially in the context of urban regeneration where they always are overlooked at the detailed planning level. By using open-sourced POI and population data, our proposed workflow can be easily applied to other cities or regions, enhancing their practical value for similar research contexts. Full article

Resource-based cities face numerous sustainability challenges, making the coupled and coordinated relationship between urbanization and the eco-environment critical for sustainable development strategies. The Loess Plateau is an essential energy base and ecologically fragile area in China, holding unique and significant research value. This research employed the Remote Sensing Ecological Index (RSEI) and the Compound Night Light Index (CNLI), based on MODIS and night light data, to investigate the socio-economic development and eco-environmental changes across 25 resource-based cities on the Loess Plateau (LP) in China over the past 20 years. The Coupling Coordination Degree Model (CCDM) and Multi-Scale Geographically Weighted Regression (MGWR) were utilized to assess the relationship between urbanization and ecological factors. The average RSEI values for these cities ranged from 0.4524 to 0.4892 over the 20 years, reflecting an upward trend with a growth rate of 8.13%. Simultaneously, the average CNLI values ranged from 1.5700 to 6.0864, with a change of 4.5164. Over the past two decades, all cities in the study area experienced rapid urbanization and ecological development. The correlation between urbanization and ecological factors strengthened, alongside an increasing spatial heterogeneity. While the coupling coordination relationship in most cities showed improvement, many remained within the low to middle grades. These findings enhance the understanding of the intricate relationships between urbanization and ecology, offering valuable insights for policy-making aimed at creating sustainable and livable resource-based cities. Full article

Exploring the relationship between land use characteristics and ridership in railway station areas provides crucial decision-making support for station area planning. Previous research has mostly focused on subways, with a lack of studies on the land use characteristics and ridership of commuter rail stations, particularly in relation to the differences and impacts across various passenger catchment areas (PCAs). This study employed a multinomial logit model to evaluate the land use characteristics within 1000 m of Japan Railways (JR) stations in four different PCAs of the Tokyo metropolitan area (TMA). Additionally, regression models and a multiscale geographically weighted regression (MGWR) model were used to analyze how land use characteristics in these PCAs affected station ridership. The key findings were as follows: (1) the land use characteristics around commuter rail stations exhibit distinct zonal patterns; within 250 m, public transport stops and public service facilities are the most densely concentrated; the highest residential population density is found between 250 and 750 m; and commercial facilities are mostly clustered in the 500 to 750 m range; (2) the impact of land use factors on ridership varies in intensity across different spatial zones; the density of public transport stops and street network density is most significant within 250 m, whereas commercial facility density is greatest within the 500–750 m PCA; (3) The land use characteristics within 500 m of stations have greater explanatory power for passenger flow, and the goodness of fit of the MGWR model surpasses that of the linear regression model. Full article

In the context of global climate change and the increasing severity of the urban heat island effect, it is particularly important to study the spatial variation mechanism of urban land surface temperature (LST). The LST data provided by ECOSTRESS offer a new perspective for deepening our understanding of the diurnal cycle and spatial variation of urban LST. In this study, based on a block scale, Tianjin is divided into nine block types, and a multi-scale geographic regression weighting (MGWR) model is used to comprehensively explore the relative contributions of urban 2D and 3D landscape indicators of different block types to the spatial changes in diurnal urban LST cycles. The results indicate that ① the thermal effect during the daytime is mainly influenced by the building density, while at night, it is more influenced by the building height and the heat retention effect; ② the building indicator and the water-body indicator had the most significant effect on surface temperature at different observation times; ③ the influence of urban morphology on land surface temperature shows significant spatial non-stationarity across different block types. This study enhances the understanding of the mechanisms driving urban heat island formation and provides a scientific basis for urban authorities to develop more effective urban planning and heat island mitigation strategies. Full article

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations. Full article

Urban vitality is a concept that reflects dynamic changes in economic, social, and cultural aspects, emphasizing the importance of diversified land use and dense population. With technological advancements, research methods on urban vitality are increasingly diverse, particularly with the application of big data and geographic information systems providing new perspectives and tools for such studies. Currently, research on the vitality of inland Central Plains cities in China is relatively limited and largely confined to specific administrative areas, leading to an inadequate understanding of basic economic activities and population distribution within cities. Therefore, this study aims to explore the spatial distribution characteristics of urban vitality and its influencing factors in Zhengzhou’s main urban area, providing a scientific basis for urban planning and sustainable development. This study utilizes methods that include Densi graph curve analysis, the entropy method, and the multiscale geographically weighted regression (MGWR) model, integrating statistical data, geographic information, and remote sensing imagery of Zhengzhou in 2023. The MGWR model analysis reveals: (1) Urban vitality in Zhengzhou’s main urban area exhibits a concentric pattern, with high vitality at the center gradually decreasing toward the periphery, showing significant spatial differences in economic, population, and cultural vitality. (2) Various influencing factors positively correlate with urban vitality in the main urban area, but due to shortcomings in urban development strategies and planning, some factors negatively impact vitality in the central area while positively affecting vitality in peripheral areas. Based on these findings, this study provides relevant evidence and theoretical support for urban planning and sustainable development in Zhengzhou, aiding in the formulation of more effective urban development strategies. Full article