Urbanization Research Articles

Strategy to mitigate substrate inhibition in wastewater treatment systems

Global urbanization requires more stable and sustainable wastewater treatment to reduce the burden on the water environment. To address the problem of substrate inhibition of microorganisms during wastewater treatment, which leads to unstable wastewater discharge, this study proposes an approach to enhance the tolerance of bacterial community by artificially setting up a non-lethal high substrate environment. And the feasibility of this approach was explored by taking the inhibition of anammox process by nitrite as an example. It was shown that the non-lethal high substrate environment could enhance the nitrite tolerance of anammox bacterial community, as the specific anammox activity increasing up to 24.71 times at high nitrite concentrations. Moreover, the system composed of anammox bacterial community with high nitrite tolerance also showed greater resistance (two-fold) in response to nitrite shock. The antifragility of the system was enhanced without affecting the operation of the main reactor, and the non-lethal high nitrite environment changed the dominant anammox genera to Candidatus Jettenia. This approach to enhance tolerance of bacterial community in a non-lethal high substrate environment not only allows the anammox system to operate stably, but also promises to be a potential strategy for achieving stable biological wastewater treatment processes to comply with standards.

Assessment of short-term effects of ambient air pollution exposure on osteoarthritis outpatient visits

The association of short-term ambient air pollution exposure with osteoarthritis (OA) outpatient visits has been unclear and no study has assessed the modifying roles of district-level characteristics in the association between ambient air pollution exposure and OA outpatient visits. We investigated the cumulative associations of ambient air pollution exposure with daily OA outpatient visits and vulnerable factors influencing the associations using data from 16 districts of Beijing, China during 2013–2019. A total of 18,351,795 OA outpatient visits were included in the analyses. An increase of 10 μg/m3 in fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), maximum 8-hour moving-average ozone (8 h-O3), and 0.1 mg/m3 in carbon monoxide (CO) at representative lag days were associated with significant increases of 0.31 %, 0.06 %, 0.77 %, 0.87 %, 0.30 %, and 0.48 % in daily OA outpatient visits, respectively. Considerable OA outpatient visits were attributable to short-term ambient air pollution exposure. In addition, low temperature and high humidity aggravated ambient air pollution associated OA outpatient visits. District-level characteristics, such as population density, green coverage rate, and urbanization rate modified the risk of OA outpatient visits associated with air pollution exposure. These findings highlight the significance of controlling ambient air pollution during the urbanization process, which is useful in policy formation and implementation.

Spatiotemporal Characteristics and Influencing Factors of the Synergistic Effect of Pollution Reduction and Carbon Reduction in China

Based on the use of the coupling coordination model to calculate the coupling coordination degree of carbon emission and pollutant control, the national, regional, and provincial spatiotemporal characteristics of the synergistic effect of pollution control and carbon emissions reduction in China were further analyzed, facilitating the crucial to identification of key areas. The fixed effects regression models and provincial panel data from 2006 to 2020 were used to explore factors contributing to better synergizing the reduction of pollution and carbon emissions in China. On this basis, the adjustment variable of R&D investment intensity was introduced, and the regulation effect model was constructed to further explore the influence mechanism of the synergistic effect of pollution reduction and carbon reduction. The results showed that： synergy exists between carbon emission reduction and the air pollution control system, the evolution of the synergistic effect of pollution reduction and carbon reduction in China presented an inverted "U"-shaped trend from 2006 to 2020, and there was spatial aggregation and a spatial spillover effect in pollution reduction and carbon reduction. The synergistic governance of carbon emission and pollutant control was still at a relatively low level. The carbon emission and air pollutant emission systems were still in an unstable and uncoordinated state. The results showed that： The degree of coordination of eastern China, central China, and western China decreased in turn. At the national level, energy consumption structure, per capita GDP, and the proportion of green investment were the main factors affecting the synergistic effect of pollution reduction and carbon. The heterogeneity of the influencing factors existed in the central, eastern, and western regions on industrial structure, energy consumption structure, energy utilization efficiency, per capita GDP, urbanization rate, the proportion of green investment, and transportation structure. The intensity of R&D played a significant moderating effect in the whole country, eastern, and central regions. However, no significant moderating effect was identified in the western region. In the eastern region, the urbanization rate, the proportion of green investment, and the transportation structure could not have a significant effect on the synergistic effect of pollution reduction and carbon reduction alone, and it must be coordinated with the intensity of R&D.

Rural-urban Migration and Overweight Status in Low- and Middle-income Countries: Evidence from Longitudinal Data in Indonesia

With rapid urbanization and intensive rural-urban migration, being overweight has become an increasingly common source of health risks in low- and middle-income countries (LMICs). However, the impact of rural-urban migration on overweight status is understudied in the LMIC context. Previous studies primarily used cross-sectional data, failing to adjust for migration health selection. Furthermore, the potential gender pattern in the impact of rural-urban migration remained unclear, and the potential cumulative effect of the duration of urban residence among migrants was rarely examined with longitudinal data. Meanwhile, the mediating effects of health behavioral factors were poorly understood. Using data from the fourth and fifth waves of the Indonesia Family Life Survey (IFLS) and employing fixed-effects (FE) models (N=7267), this study provided new evidence on the impact of rural-urban migration on overweight status across time and gender. Findings suggest that rural-urban migration significantly and positively predicted being overweight, and the association was significantly stronger among women than men. The results also show that years lived in urban areas did not significantly predict overweight among migrants, and that mediating health-related behaviors did little to explain adverse weight outcomes of migrants. As the number of rural-urban migrants continues growing, LMIC governments should implement health interventions aiming for healthy weight status among migrant communities. Meanwhile, gender-specific programs targeting women should be considered. Future research should explore other potential mediators of the link between rural-urban migration and overweight.

Structural Transformation in the Indonesian Economy: Why does 'Financial Development' Matter?

This study offers a comprehensive examination of Indonesia's process of changing its economic structure from 2000 to 2020. Structural transformation, characterized by changes in the distribution of jobs among different sectors, is crucial for fostering economic growth. It emphasizes phenomena such as the decrease in agricultural GDP shares, rural-urban migration and shifts in economic sectors, which resemble the patterns observed in emerging countries. Nevertheless, the rate of employment transformation is comparatively slower than the fluctuations in GDP, suggesting a more gradual and protracted process of structural change. The analysis focuses on obstacles to transformation, such as insufficient personnel and digital infrastructure. The study examines the relationships between structural transformation and economic indicators in the provinces of Indonesia from 2011 to 2022, utilizing panel data regression models. The findings indicate strong correlations between economic development, human capital, financial development, digital technology, and structural transformation. The study highlights the significance of tackling these elements to accelerate transformation. The policy implications of this proposal suggest that Indonesia should prioritize economic and digital growth, improve human capital, and strengthen the financial sector. This will lead to a more rapid and comprehensive structural transformation in the country.

Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam

Abstract In recent decades, global rapid urbanization has exacerbated the impacts of natural hazards due to changes in Southeast Asia’s environmental, hydrological, and socio-economic conditions. Confounding non-stationary processes of climate change and global warming and their negative impacts can make hazards more complex and severe, particularly in Vietnam. Such complexity necessitates a study that can synthesize multi-dimensional natural-human factors in disaster risk assessments. This synthesis study aims to assess and monitor climate change and land-cover/land-use change impacts on flood and landslide hazards in Vietnam’s Gianh River basin. Three Deep Neural Network (DNN) and optimization algorithms, including the Adam, Tunicate Swarm Algorithm (TSA), and Dwarf Mongoose Optimization (DMOA) were used to determine the regions with the probability of the occurrence of flood and landslide and their combination. All efficiently evaluated hazard susceptibility based on a synthesis analysis encompassing 14 natural and anthropogenic conditioning factors. Of the three, the Deep Neural Network (DNN)-DMOA model performed the best for both flood and landslide susceptibility, with area-under-curve values of 0.99 and 0.97, respectively, followed by DNN-TSA (0.97 for flood, 0.92 for landslide), and DNN-Adam (0.96 for flood, 0.89 for landslide). Although the area affected by flooding is predicted to decrease, the overall trend for total hazard-prone areas increases over 2005–2050 due to the more extensive area affected by landslides. This study develop and demonstrate a robust framework to monitor multi-hazard susceptibility, taking into account the changes in climate and land-use influence the occurrence of multiple hazards. Based on the quantitative assessment, these findings can help policymakers understand and identify confounding hazard issues to develop proactive land-management approaches in effective mitigation or adaptation strategies that are spatially and temporally appropriate.

Construction engineering in China under green Transition: Policy research Topic clusters and development forecasts

China’s construction engineering market is gradually transitioning towards high-quality development, driven by policies such as green and low-carbon. Identifying research topics for China’s construction engineering policy and predicting future research trends are crucial for policymakers to make informed decisions and promote the transformation of the construction industry. This study combines Latent Dirichlet Allocation (LDA) and Autoregressive Integrated Moving Average Model (ARIMA) to analyze more than 6,500 relevant literature abstracts. The LDA model identified 11 key topics, with the five hottest topics conceptualized with trend prediction. ARIMA-based trend forecasts show that these topics will continue to grow over the next two years. Energy Consumption in Construction (Topic 2) is caused by the urgency of transforming China’s construction industry. The environmental impacts of green construction (Topic 6) are due to the sustainability potential of the green construction model. The relationship between urbanization, the economy and the environment (Topic 7) was prompted by the discussion that the relationship between them is still unclear. The use of recyclable materials in construction (Topic 8) is a result of resource depletion and environmental degradation. The relationship between urbanization and ecosystems (Topic 9) was prompted by the environmental problems associated with China’s high rate of urbanization. These findings reveal the focus and development trend of construction engineering transformation in China and provide feedback to policy makers and companies from the perspective of experts and scholars. The accuracy of the predictions can be further improved in the future through data updates and multi-modeling combinations.

Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

Agrarian Distress in Indian Agriculture: Causes, Socio-economic Consequences and Coping Strategies

Agrarian distress is the economic, political and social challenges faced by the farmers and rural communities due to factors such as low crop yields, fluctuating prices of agricultural produce, high input costs, indebtedness, and lack of access to credit, markets, and infrastructure. Over 8,00,000 individuals are dies by suicide each year because of various reasons. More over the suicides are happening in the families whose own land is less than 1-2 hectares, who are in debt, have been identified the most at risk for suicide. The present article aims at better understanding of the causes, consequences and possible coping strategies for the agrarian distress in India. Causes such as fluctuations in crop sales, weather and climate conditions, dependent on natural resources, etc. are discussed in detail. Consequences of agrarian stress are suicides of farmers, rural migration, food insecurity, environmental impacts and so on. Solutions such as strengthening the forward markets, comprehensive crop insurance schemes for the framers where the non-MSP (Minimum Support Price) crops also may be covered, assured price to the farmers are discussed. Many researchers argued that farmers suicides are increasing but the research is limited in the specialised area of interest. In addition to that, researchers are focussing only on the socio-economic variables rather than the psychological variables. In order to mitigate the risks out of agrarian distress, farmers should be encouraged by the government and extension service providers to take up the subsidiary occupations and secondary agricultural occupations which helps them in getting profits in the situations where the crop damages due to the extreme weather patterns. As rightly pointed out by National Bank for Agriculture and Rural Development (NABARD), distress of the framers should have a long-term potential solution such as major reforms in the existing agricultural laws rather than farm loan waivers which acts as the short-term relief to the farmers.

The Role of Diversified Geo-Information Technologies in Urban Governance: A Literature Review

Global urbanization has made urban governance a crucial aspect of sustainable urban development. While geo-information technologies have emerged as indispensable tools for effective urban governance, a comprehensive analysis of their application in this context remains lacking. This study seeks to review and assess the pivotal role of geo-information technologies in the field of urban governance. A total of 219 related studies were used for bibliometric analysis and key content analysis. Planning Support Systems (PSSs), Participatory Geographic Information Systems (PGISs), Building Information Modeling (BIM), and City Information Modeling (CIM) are identified as the main information technologies progressively employed across diverse stages of urban planning and construction over recent decades. These advancements have propelled the digital and intelligent management of urban areas, yielding significant benefits such as enhanced visualization, informed decision-making, and increased opportunities for citizen participation. However, a noticeable disparity between supply and demand during the application process arises from a lack of transdisciplinary cooperation. This study sheds light on the existing literature and offers policy implications and recommendations for more effective utilization of geo-information technologies in future spatial governance.

The role of green finance in the construction of new energy system: Evidence from China

Under the rapid development of energy transition and new energy technologies, it is of practical significance to construct a new energy system and explore the empowerment mechanism of green finance on the new energy system. This study conducted empirical research, analyzing the specific theoretical mechanism and transmission path of green finance in constructing a new energy system. The research results indicate that green finance directly promotes the construction of a new energy system, further driving the improvement of technical innovation levels, and facilitating the establishment of more environmental protection institutions, thereby indirectly promoting the construction of a new energy system. Additionally, green finance has a stronger promotion effect under conditions of lower openness levels, economic development levels, urbanization rates, and proportions of the tertiary industry. Moreover, under higher government fiscal expenditure conditions, green finance also generates a greater promotion effect. As the development level of the new energy system increases, the marginal impact of green finance first rises, then stabilizes, and ultimately decreases. Within the empirical analysis framework, this paper provides a novel perspective on the empowerment of green finance, contributing to the construction of a new energy system under new development conditions.

Research and analysis of urban-rural residential carbon emissions in China

Residential carbon emissions are an important component of anthropogenic carbon emissions. a significant increase in residential carbon emissions has become a reality under the global urbanization process. In this context, this paper built a feature combination value model based on NPP-VIIRS nighttime light remote sensing data, and divided urban-rural areas through breakpoint analysis method and reference comparison method. Then, explored the characteristics and differences of residential carbon emissions and per capita residential carbon emissions in nine different levels of cities in 2019 from the perspective of urban-rural areas. The results indicate that the residential carbon emissions and per capita residential carbon emissions shows the spatial distribution characteristics of first tier cities>second tier cities>third tier cities. Among them, the residential carbon emissions in Beijing, Guangzhou, Nanjing, Taiyuan show a distribution pattern of urban>urban-rural fringe>rural. The residential carbon emissions in Shijiazhuang, Wuxi, Xiangyang, Zunyi, Huai’an show a distribution pattern of rural>urban-rural fringe>urban. The per capita residential carbon emissions of urban areas are relatively low, while the per capita residential carbon emissions of rural areas are relatively high, show a distribution pattern of rural>urban-rural fringe >urban. The results can help the Chinese government balance the needs of urban-rural development in different levels of cities, so as to formulate targeted carbon emission reduction policies and achieve low-carbon goals.

Beyond the Idyll: Unveiling the realities of risk perception and neighborhood influence on rural in-migrants in the COVID-19 Era in Dali, China

While the COVID-19 pandemic has intensified counter-urban migration trends globally, with rural areas often portrayed as ideal havens, the post-migration realities and risks faced by rural in-migrants remain understudied. Addressing this research gap, this research investigates the neighborhood factors influencing risk perceptions of rural in-migrants during the COVID-19 pandemic, focusing on four rural destinations in Dali, China. Using a mixed-method approach combining questionnaires, participatory mapping, and regression analysis, we surveyed 234 rural migrants. Our study incorporates objective measurements of neighborhood environments through Participatory GIS. Findings reveal that reduced life quality and economic income are the main perceived risks among rural in-migrants during the pandemic. Neighborhood factors significantly mitigating perceived risks include green spaces, accessible living services, government satisfaction, and strong local community networks. The study also identifies varying vulnerability levels among migrant groups, with migrant workers showing higher risk perceptions than entrepreneurs or unemployed individuals. This research addresses a critical gap in migration studies by examining post-relocation risks faced by rural in-migrants, especially during global crises. It provides empirical evidence on the role of neighborhood environments in mitigating migrants' risk perceptions, offering insights for enhancing rural community resilience. The findings contribute to our understanding of rural in-migration dynamics in the context of the COVID-19 pandemic and beyond, informing strategies for sustainable rural development and migrant integration in the face of future crises.

Fostering resilience and post-traumatic growth in overseas Chinese left-behind children: The role of autonomy, competence, and relatedness.

This study examines the impact of parental migration on the psychological well-being and development of left-behind children (LBCs) in Zhejiang, China, within the broader context of the country's rural transformations and urban migration. It investigates how intellectual and relational engagement (RE), autonomy (AUT), competence (COM), and relatedness (RES) contribute to resilience (REL) and post-traumatic growth (PTG) in these children, reflecting on the shift from viewing parental separation merely as a source of trauma to recognizing its potential to foster significant personal growth. Utilizing a cross-sectional design, the research was conducted in April and May 2023 with 1348 LBCs from a total sample of 4049 students inZhejiang. A two-step random, stratified, cluster-based sampling strategy was employed, and structural equation modeling was used to examine the hypothesized relationships among the constructs. The statistical analysis demonstrated significant positive effects of intellectual engagement (IE), AUT, COM, and RE on both REL and PTG (p<.05 for all). IE strongly correlated with AUT (r=.68, p<.001) and COM (r=.71, p<.001), enhancing REL and facilitating PTG. Additionally, the presence of secure and consistent relationships was identified as crucial for maintaining psychological well-being, with high correlation coefficients (r>.60) underscoring their importance. Notably, REL was found to moderate the relationships among RES, COM, and PTG, highlighting its critical role in the psychological adaptation of left-behind children. The study underscores the importance of nurturing intellectual and REs, AUT, and COM to enhance psychological REL and well-being among LBAs. These elements are crucial for supporting the mental health and developmental needs of children facing the challenges of parental migration. The findings advocate for targeted interventions that can address the unique needs of this vulnerable population, emphasizing the potential for growth and adaptation despite adversities.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units arecrucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

Measuring Changes in Temperature Rates due to Urban Expansion in Selected Municipalities of Baghdad using GIS Technology

The expansion of building blocks at the expense of agricultural land is one of the main problems causing climate change within the urban area of a city. The research came to determine these indicators, as a study was conducted on the expansion of the building blocks in three municipalities in the city of Baghdad for a period of four decades extended in the form of time cycles for the period (1981-2021) and using ArcMap GIS 10.7 technology. Then, the impact of this expansion on temperature rates was evaluated, as they are the most important climatic elements due to their significant effect on the rest of the elements. The results showed a clear, direct relationship between the increase in urban expansion rates and the corresponding rise in temperature rates, which results in urban heat islands. The results for the last time cycle showed high expansion rates for the municipalities of Al-Kadhimiya, Al-Shaala, and New Baghdad (62.2, 82.4, and 92.1), respectively, and were offset by high-temperature rates (25.2, 25.49, and 25.67) for the same municipalities, respectively, as the highest rates were recorded in the New Baghdad municipality, followed by the municipality of Al-Shaala, in second place, then the municipality of Al-Kadhimiya, with the lowest rates due to its location on the Tigris River.

Evaluation of urban low-carbon development efficiency: evidence from 30 cities in China

With the acceleration of global urbanization, the issues faced by urban development are becoming increasingly apparent. Some countries and regions have placed less emphasis on low-carbon emissions in the development process and prioritized urban development. However, blindly pursuing rapid urbanization has led to excessive urban carbon emissions, which outweigh the gains. Therefore, based on panel data from 30 cities in China from 2010 to 2019, this study combines the super-efficiency slacks-based measure (SBM) model and the Malmquist index, taking urban carbon emissions as undesirable outputs, to evaluate urban development efficiency. Through a multidimensional comparative analysis, this study reveals the developmental challenges encountered by various cities. The results show that: statically, the overall urbanization progress of these 30 cities is commendable, but significant fluctuations are observed in the development trend. The mean super-efficiency of cities in the eastern coastal areas is relatively high, whereas other regions exhibit instances of ineffective mean super-efficiency. Dynamically, there are few periods of efficiency improvement during 2010–2019, and there is an imbalance in the sources of efficiency improvement. Shanghai and Hangzhou exhibit the best continuous dynamic improvement status, with small fluctuations in dynamic efficiency, whereas Guiyang has the worst dynamic efficiency improvement status, indicating poor urban development. This study underscores the urgent need for a balanced approach to urban development that incorporates low-carbon initiatives to ensure sustainable progress and mitigate adverse environmental impacts.

Widespread introduced species dominate the urban tree assemblage on the endemic-rich tropical island of São Tomé.

The Afrotropics are experiencing some of the fastest urbanisation rates on the planet but the impact of city growth on their rich and unique biodiversity remains understudied, especially compared to natural baselines. Little is also known about how introduced species influence β-diversity in these contexts, and how patterns coincide with native ranges of species. Here we investigated how tree assemblages of the endemic-rich Afrotropical island of São Tomé differed between urban, rural and natural zones. These were primarily characterised by urban greenspaces, shade plantations, and old-growth forests, respectively. Based on 81 transects, we assessed biodiversity metrics of endemic, native and introduced species. Tree abundance and species richness were highest in the natural zone, where the composition was most different from the urban zone. The tree community of the rural zone was the most uneven and had the least variation among transects, representing the lowest β-diversity. The urban zone was dominated by introduced species (57.7%), while the natural zone hosted almost exclusively native species (93.3%), including many endemics (26.1%). The biogeographic realms that species originated from were particularly diverse in the urban zone, with few species from the Afrotropics. In contrast to native and endemic trees, introduced trees were clearly associated with urban and rural expansion, as they were much more abundant and species-rich in these zones than in the natural zone, facilitating biotic homogenisation. These findings highlight how urban and rural environments are affecting the native tree flora of São Tomé, and the need for conservation measures geared towards globally threatened and endemic tree species. Importantly, these require the protection of natural forests, despite the rising land demands for settlements and agriculture. Ultimately, such action to conserve endemic trees will contribute to global efforts to prevent further biodiversity declines.

Evaluation of Mortality Rate Disparities for Cardiac Arrest Between Urban and Rural Cohorts in the United States Using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Database.

Introduction The United States includes diverse geographic areas with distinct urban and rural settings. Urban areas served with higher health services and the rural regions with restricted facilities. This disparity results in higher rural mortality rates. Thus, the study uses the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database to assess the disparities in cardiac arrest mortality rates in urban versus rural areas. Methods This is a retrospective study to assess trends in overall mortality rates for urban versus rural areas in the United States between 1999 and 2020, using the CDC WONDER data for cardiac arrest (ICD-10 CODE I46), extracted on May 25, 2024. Urban/rural classification was based on the Metropolitan 2013 scheme. Statistical analysis was done via RStudio v.4.3.3 and included measures of central tendency, mortality rates per 100,000, and plotting of temporal trends. Results Between 1999 and 2020, the total number of deaths due to cardiac arrest in rural and urban areas was 103,115 and 262,505, respectively. Among the age groups, infants <1 year and elderly >85 years showed a high mortality rate in rural areas compared to urban areas.Gender analysis revealed both males (3.3 per 100,000) and females (3.52 per 100,000) had a high rural mortality rate, compared to urban rates of 1.51 and 1.54 per 100,000, respectively. Racial analysis showed that American Indian or Alaska Native and Asian or Pacific Islander populations had higher mortality in rural areas, with rates of 1.1 and 1.81 per 100,000, respectively, compared to the urban rates of 0.34 and 0.8 per 100,000. Conclusion Trends in mortality rate showed a general decline over time but the gap between urban and rural mortality persists, highlighting the need for continued efforts in rural areas.

A multiscale and multiperspective quantifying framework for spatial patterns and influencing mechanisms of geographical indications

Geographical indications (GIs) embody distinctive regional elements characterized by widespread spatial distribution, serving as vital agricultural intellectual properties and significant tourism assets. This case study introduces a multiscale and multi-perspective quantification framework for Shandong Province, focusing on spatial agglomeration, association, and heterogeneity. This approach quantitatively explores GIs’ spatial patterns and influencing mechanisms, analyzing both at the monomer and regional scales, considering static and dynamic perspectives. Leveraging methods such as kernel density analysis, Moran’s I index, Geary’s C index, Internet hot word analysis, and Geodetector, the coastal municipalities, prosperous urban areas, and zones with high-quality arable soils were identified as spatial agglomeration areas for GIs. The study revealed a strong correlation between the spatial distribution pattern of socioeconomic factors and GIs’ spatial density from a static perspective. However, diminished spatial autocorrelation was observed for GIs’ public perception from a dynamic standpoint, indicating a reduced influence of Internet connectivity. The spatial heterogeneity in GIs is determined by multiple interconnected factors, displaying an enhancement effect between any two influencing factors. Among the 15 influencing factors analyzed, urbanization rate; gross value of agricultural, forestry, animal husbandry, and fishery production; and per capita gross domestic product significantly contribute to spatial heterogeneity effects at both urbanization rate, gross value of agriculture, forestry, animal husbandry, fishery production, and per capita gross domestic product significantly contributed to spatial heterogeneity effects at regional and monomer scales. The results suggest that GIs are likely to flourish with regional economic growth. The analytical framework and quantification methods support exploring and sustainably utilizing GIs, improving output quality, and modernizing GIs’ production, ultimately contributing to regional agriculture’s high-quality and sustainable development.