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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.

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R2 estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.

Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016–2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O3) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O3 concentration increased year by year in most cities, but in 2020, the O3 concentration in all cities decreased. O3 concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O3 was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O3 concentration. Compared with NO, NO2, and NOx (NOx ≡ NO + NO2), the ratio of NO2/NO was more coherent with O3. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM2.5) exerted a greater impact on O3 in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O3 differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O3 pollution control in coastal and inland cities.

Promoting research on urban park use is important for developing the ecological and environmental health benefits of parks. This study proposes uniquely integrated methods combined with big data to measure urban park use. It combines comprehensive geographic detectors and multiscale geographically weighted regression from a geospatial perspective to quantify the individual and interactive effects of the parks’ characteristics, accessibility, and surrounding environment features on weekday and weekend park use. The study also explores the degree of influence of spatial changes. The results indicate that the park-surrounding facilities and services factor contributed most to use, while its interaction effect with park service capacity had the greatest impact on park use. The interaction effects showed binary or nonlinear enhancement. This suggests that park use should be promoted within multiple dimensions. Many influencing factors had significant changes in the geographic space, suggesting that city-level park zoning construction should be adopted. Finally, park use was found to be affected by users’ subjective preference on weekends and convenience factors on weekdays. These findings provide a theoretical basis for the influencing mechanisms of urban park use, which can help urban planners and policymakers formulate more specific policies to successfully manage and plan urban parks.

The Han River Basin is a main agricultural production area and a water source for the middle route of the South-to-North Water Diversion Project in China. Over the past 20 years, human exploitation and ecological construction have disturbed the sustainability of land productivity in the Han River Basin. Theil–Sen trend analysis, Mann–Kendall statistical test, and Hurst index methods were applied to examine spatial–temporal trends and sustainability characteristics of land net primary productivity (NPP) change in the Han River Basin from 2001 to 2019 using MOD17A3 NPP product, natural, and socio-economic data obtained from Google Earth Engine (GEE). The findings demonstrated that the interannual variation of land NPP exhibited a fluctuating upward trend, with a more pronounced growth rate from 2001 to 2010 than from 2011 to 2019. The spatial heterogeneity of land NPP was evident, with high values in the west and low values in the east. Of the basin area, 57.82% presented a significant increase in land NPP, while only 0.96% showed a significant decrease. In the future, land NPP in the Han River Basin will present sustained growth. The results were also compared with Trends.Earth’s calculations for the SDG 15.3.1 sub-indicator of land productivity. In addition, the spatial heterogeneity of factors influencing land NPP change was explored using a multiscale geographically weighted regression (MGWR) model. Precipitation and population count were the dominant factors in most regions. Besides, precipitation, population count, and human modification all exhibited inhibitory effects on the increase in land NPP except for elevation. The research can provide a scientific basis for tracking land degradation neutrality (LDN) progress and achieving sustainable socio-ecological development of the Han River Basin.

People’s perceptions and understanding of the built environment can shape and regulate travel intention and behavior. From the perspective of urban design, improving the built urban environment is an important way to encourage green travel. This study determined the impact path from the built environment to subway travel behavior, considering the intermediate effect of psychological factors. The impact path could provide feedback for optimizing the built environment, thereby improving the ratio of subway travel. In this study, the impact path hypothesis of “objective environment—subjective psychological—travel behavior” was first proposed, taking travelers’ psychological perceptions as the intermediary variable. Second, questionnaires and geographic information were used to measure the objective environment and subjective psychological perceptions. Third, a structural equation model was constructed to verify the proposed path hypothesis. Finally, multiscale geographically weighted regression was used to analyze the influence of subjective and objective factors on travel behavior and its spatial differences. The empirical case sampled 405 residents of Zhengzhou, China. The results verified the proposed impact path hypothesis and revealed spatial heterogeneity in its impact on travel behavior. The research explains how travel behavior is generated and could support the optimization of the urban built environment.

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

Over the last few decades, most regional ecosystem services (ESs) have significantly deteriorated, primarily driven by an increase in human dominance over the natural environment. Creating an assessment framework of ESs and identifying its driving factors at the regional scale is challenging for researchers, administrators, and policy-makers. In this study, we attempt to quantify the economic value of ESs (ESV) in Guizhou Province from 2000 to 2018, one of the most prominent areas of karst landforms in China. We identified the major factors affecting ESs using the geographical detector (GD) model. Then, we conducted a multiscale geographically weighted regression (MGWR) analysis to examine the spatial differentiation of the causal effects of both natural and anthropogenic factors on ESs. Our results demonstrate the following: (1) the total ESV of Guizhou Province was approximately USD 81,764.32 million in 2000, USD 82,411.06 million in 2010, and USD 82,065.31 million in 2018, and the increase of USD 300.99 million from 2000 to 2018 was the result of the remarkable conversion from cultivated land to forestland; (2) significantly considerable differentiation existed in the spatial distribution of ESV at the county level, with a higher value in the eastern region and a lower value in the western region; (3) among the driving factors, population density had a more significant effect on the spatial differentiation of ESV than did natural factors; and (4) agricultural output value was the dominant factor influencing the ESV during the study period, with a significantly positive correlation, whereas per capita GDP and population density had significantly negative impacts on ESV, according to the effective performance of the MGWR model that evaluated the spatial heterogeneity in geospatial relationships between the driving factors of ESV. Our findings can provide notable guidance to land administrators and policy-makers for effective land resource conservation and management plans, thereby improving regional sustainability.

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

Diagnosing degradation risks of ecosystem services (ESs) is a cornerstone for achieving sustainable development of the ecosystem and promote human well-being. In this study, we constructed and applied a diagnostic framework for degradation risks of ecosystem services (ESDRs) in Wuhan City, China. First, the risk receptors and risk sources were spatially overlaid to identify and measure the ESDRs. Second, a multiscale geographically weighted regression (MGWR) model was applied to identify land development factors that could be used to regulate ESDRs. Finally, the high value areas of ESDRs were clustered and the regression results were used to propose regulatory measures for each clustered zone. Our study showed that the uncontrolled expansion of construction land in Wuhan might weaken the functions of water retention, habitat maintenance and carbon sequestration services, while the functions of habitat maintenance and soil conservation services might be weakened by the expansion of farmland. The five ESDRs identified based on the above results had significant spatial clustering characteristics and spatial heterogeneity. The visual results of the MGWR model suggested that factors of land development could effectively mitigate conflicts between construction or farming activities and ESs. Ultimately, the spatial clusters of ESDRs were scientifically delimited and differentiated regulatory measures were developed simultaneously. Therefore, we argue that land use planning and environmental management in rapidly urbanizing areas should take into account the diagnostic framework of ESDRs in order to protect natural ecosystems.