In this study, we adopted individual baseline data from the UK Biobank cohort of participants aged 40–69 across the UK to explore whether there is evidence of the geography related to health disparities in obesity. First, we used multilevel models to decompose the variation in body mass index (BMI) values to examine the presence of spatial clustering patterns of individual BMI values at various geographic scales. Next, we looked at whether key predictors of obesity, such as physical activities and dietary habits, differ across 6 cities in England by using a machine learning approach. To do this, we trained random forest models in one city, and we used them to predict BMI values in other cities to see if the models were spatially transferable. Subsequently, we turned to explore socio-economic status, which is one of the direct interests in the literature with obesity and used those in combination with multilevel models to check for the existence of spatially varying effects. The results of the multilevel null models indicate that most of the variance of BMI is due to individual variation, suggesting little evidence of geographical clustering at any geographical scales. The machine learning prediction results show that the effects of the main identified risk factors for obesity are stable (spatially stationary) across cities, based on approximately the same predictive power and broadly constant effect sizes of main factors. Multilevel models taking socio-economic status into account further support that individual and neighbourhood deprivation levels display limited geographical variation in their effects on obesity across the study areas. Contrary to our expectations, the models together suggest the limited association of geographical context with obesity, among the UK Biobank participants.

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran’s I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status.

Using economic and water consumption data from Inner Mongolia and its 12 cities (2004–2023), this study employs the Tapio decoupling model to investigate the relationship between water consumption and economic growth. The results show a general shift from weak to strong decoupling across the region, with extreme events such as the 2020–2021 pandemic period (decoupling index, DI = 10.31) causing clear disruptions. Regional disparities followed a triple pattern: industrial areas (e.g., Ordos, Baotou) achieved strong decoupling via innovation; agricultural regions (e.g., Tongliao, Bayannur) remained in weak negative decoupling modes due to rigid water demand; and ecologically vulnerable areas (e.g., Alxa League, Xilin Gol) saw high volatility and unsustainable policy effects. Our interpretation of the three patterns highlights the need for region-specific governance. The driving mechanisms mainly include uneven adoption of water-saving technology (e.g., low drip irrigation rates in agriculture), virtual water trade shifting pressures across regions, and climate extremes worsening imbalances. Based on these findings, we recommend differentiated subsidies, regional compensation mechanisms, and adaptive policies to support sustainable water–economy coordination in arid regions.

With the rapid development of the social economy, nitrogen dioxide (NO ₂ ), a trace gas that is an air pollutant, has dual impacts on human health and climate change. In this study, the Google Earth Engine platform is used to retrieve the vertical column concentration inverted by the Tropospheric Monitoring Instrument (TROPOMI) sensor onboard the Sentinel-5 Precursor satellite, and the characteristics of the spatial–temporal distribution of the tropospheric NO ₂ column concentration in Shandong Province from 2019 to 2023 are analyzed. The results show that there is a high correlation between the NO ₂ column concentration retrieved from the TROPOMI and the NO ₂ concentration observed at ground-level monitoring points in Shandong Province ( R = 0.8289). The distribution of the NO ₂ column concentration in Shandong Province exhibits a progressive structure of “high in the west and low in the east” with significant spatial heterogeneity. In addition, the fluctuation in the NO ₂ column concentration in Shandong Province has obvious cyclical and seasonal characteristics, and the change rate is “summerflat in spring and autumn, high in winter and low in summer.” The results of the geographical detectors indicate that temperature has the greatest influence on the NO ₂ column concentration in Shandong Province ( q = 0.5). The geographically weighted regression model indicated that the NO ₂ column concentration was negatively correlated with the topography, temperature, and precipitation, and the correlation coefficients are −0.010, −0.952, and −0.330, respectively. It is positively correlated with the intensity of human activities, the degree of urbanization of the land, and the population density, and the correlation coefficients are 0.1610, 0.982, and 0.001, respectively.

This study investigates the structural transformation of Shanghai’s economy (2004–2023), analyzing the interplay between industrial shifts and sustainable growth. While prior work has focused on short-term trends or isolated sectors, we provide the first comprehensive analysis of Shanghai’s two-decade transition from manufacturing to services, leveraging annual nominal GDP data and three forecasting models (Autoregressive Integrated Moving Average model ARIMA, Support Vector Machine SVM, and Grey Model GM). Our findings reveal that the tertiary sector’s contribution surged from 50.8% to 75.2% of GDP, driven by finance, technology, and real estate, while the secondary sector declined to 24.6%. The autoregressive integrated moving average ARIMA(1,1) model outperformed alternatives (mean absolute percentage error 2.97%), projecting GDP growth to CNY 60,321.54 trillion by 2026. Crucially, we demonstrate that Shanghai’s structural evolution aligns with advanced urban economies (e.g., New York, Tokyo), yet retains distinct industrial resilience due to China’s dual-circulation policy. These results challenge assumptions about manufacturing decline, instead highlighting a rebalancing toward high-value-added sectors. The study contributes (1) a validated framework for forecasting urban GDP in policy-stabilized economies and (2) empirical evidence for prioritizing tertiary innovation in sustainable development strategies. Policymakers and researchers can leverage these insights to navigate trade-offs between growth, equity, and environmental goals in rapidly urbanizing regions.

Rapid changes in terrestrial water storage (TWS) pose serious threats to water security in arid and semi-arid regions such as Iran. However, the inherent uncertainties of individual hydrological models hinder robust assessments of the respective impacts of natural variability and anthropogenic influence on water storage dynamics in these areas. To address this issue, our study integrates GRACE/GRACE-FO satellite gravity data, five mainstream hydrological models (GLDAS-Noah, GLDAS-VIC, GLDAS-CLSM, ERA5, and WGHM), and GPM global precipitation data. Four observational datasets related to precipitation, runoff, and evapotranspiration were derived, and 64 different hydrological model combinations were constructed. These combinations were comprehensively evaluated against Mascon products as a benchmark. Ultimately, the model combination with the best fitting performance was selected for spatial and temporal variation analysis and attribution analysis. The findings reveal that: (1) The model combination constructed using ERA5-derived evapotranspiration and runoff data, combined with precipitation data from VIC/CLSM, exhibits the highest consistency with Mascon data. (2) In densely populated northern and southwestern regions, the natural water flux shows significant upward trends. Nevertheless, TWSA declines there because anthropogenic extraction (captured in the net residual) outweighs the natural increase, causing groundwater discharge to surface systems and amplifying evapotranspiration and runoff losses. (3) In sparsely populated arid central regions, TWSA remains relatively stable, with an average annual natural water anomaly change rate of approximately + 0.01 cm/yr, primarily due to the offsetting effects of precipitation and evapotranspiration. This study systematically evaluates the applicability of a multi-model approach in arid regions. The integrated hydrological modeling framework developed herein provides a methodology for selecting model configurations and quantifying associated uncertainties in water storage assessment, thereby offering a scientific basis for sustainable water resource management in arid environments.