Global Moran Index Research Articles

Climate change and vegetation greening jointly drive the spatial pattern of net radiation variability in northern China

ABSTRACT The spatial and temporal variations of net surface radiation (Rn) are critical for comprehending ecological environments. Nonetheless, the intricate interplay among Rn dynamics, vegetation growth, climate, and natural factors remains inadequately elucidated. In this study, we estimated net surface radiation based on Landsat data and ERA5 meteorological data in the Google Earth Engine (GEE) platform, which closely matched the observable spatial distribution (R 2 = 0.96), with an average growth rate of 0.15 MJ m−2 mth−1. Trend analyses and spatial autocorrelation were used to explore the spatial and temporal changes in net radiation from 2000 to 2020, the global Moran's index for net radiation was found to exceed 0.76, with fluctuating increases, showing a highly positive spatial distribution of Rn. Local Moran's I predominantly fell into two categories: ‘High-High’ and ‘Low-Low’, with the first increasing in range and the latter decreasing. Combining GeoDetector and PLS-SEM analyses, temperature and vegetation emerge as predominant drivers of net surface radiation variation within the study area, each contributing more than 17% to Rn change. Furthermore, interactions between any two factors typically exhibits nonlinear enhancement. PLS-SEM underscores the influence of vegetation and climate on Rn, with other factors indirectly affecting net radiation changes by influencing vegetation growth.

Space-time analysis, severity of the wilt disease in escabeche pepper (Capsicum baccatum var. Pendulum) and identification of the causal agent (Phytophthora capsici L.) under subtropical climate conditions in Peru

Phytophthora capsici is an aggressive pathogen in escabeche pepper on the Peruvian coast. Root rot has a strong correlation with humidity and environment. Disease behavior was evaluated epidemiologically using spatiotemporal variables. Severity was evaluated according to the advance of the secondary symptom according to grades 1 to 5. Then, coordinates of each plant were established by photogrammetric survey of a field with 1705 escabeche pepper plants. For temporal analysis, severity was adjusted to an exponential model (R2 = 0.909) and incidence to a Gompertz model (R2 = 0.921) that detected an initial delay of the disease due to temperature. For the spatial analysis, the Global Moran Index (Ii) showed a high spatial dependence of the disease reaching a peak of 0.4 and 0.7 for severity and incidence, respectively. Also, heat maps related to the Local Ii were generated from which an initial source of infestation was determined where the furrow irrigation started in random infestations. Then, the infestation spots were settled in areas of surface water accumulation. Also, rhizosphere samples were collected per plant by degree of severity on V8 or CMA whit PARB and PDA-A selective medium. As a result, significant differences were obtained between grade 1, grade 2, 3, 4 and grade 5. In addition, the effect on yield was significant for plants with grade 4 and 5 with respect to fruit weight (22.3 and 18.5g/fruit) and weight per plant (509.5 and 371.8g/plant), respectively.

Current Status of Nursing Home Distribution in China: ANational Cross-Sectional Study.

Population aging is a challenge that the whole world is facing, especially in China. This study aims to investigate the current distribution status of nursing homes in China using spatial epidemiology methods. Cross-sectional study in China. The data were obtained from the Integrated Civil Affairs Government Service Platform 'China County Statistical Yearbook' for 2020 and the 'China City Statistical Yearbook' for 2020. Using global Moran's index to test the clustering of nursing homes, inter-group and intra-group Theil index was utilised to differentiate the sources of differences. The coupling coordination analysis was conducted to explore the coordination. Geographically weighted regression was utilised to investigate the impact of economic development and aging on nursing home resources. All analyses were conducted by Arcgis 10.8 and R Studio 4.3.2. Global Moran's index indicated that the distribution of nursing homes in China exhibited clustering. The Theil index values for institutions and beds at the national county were 0.32450 and 0.30675. However, upon comparing provinces and regions, it was found that the differences across the country mainly stem from within provinces (contribution rate: institutions 65.0%; beds 73.0%) and within regions (contribution rate: institutions 99.0%; beds 91.0%). The majority of districts and counties had a coupling coordination index of institutions and bed numbers, both of which were < 0.5. The development of nursing home resources in China has been rapid, generally presenting a balanced state, but further optimisation is needed. This study established a foundation for the targeted distribution of essential public services, focusing on nursing home healthcare resources tailored to the needs of older persons. The study underscored the urgent need for targeted elderly care policies, emphasising the optimisation of resource distribution to enhance the overall quality of care provided to the aging population. The study did not involve humans.

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas. Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Spatial Clustering of Rabies by Animal Species in New Jersey, United States, from 1989 to 2023.

Identifying spatial clusters of rabies in animals aids policymakers in allocating resources for rabies prevention and control. This study aimed to investigate spatial patterns and hotspots of rabies in different animal species at the county level in New Jersey. Data on animal rabies cases from January 1989 to December 2023 were obtained from the New Jersey Department of Health and aggregated by county. Global Moran's index (I) statistics were computed for each species to detect global spatial clustering (GeoDa version 1.22). Local Moran's indicators of spatial association (LISA) were computed to identify local clusters of rabies. The results from the LISA analysis were mapped using ArcGIS Pro to pinpoint cluster locations. A total of 9637 rabies cases were analyzed among raccoons (n = 6308), skunks (n = 1225), bats (n = 1072), cats (n = 597), foxes (n = 225), and groundhogs (n = 210). A global Moran's test indicated significant global spatial clustering in raccoons (I = 0.32, p = 0.012), foxes (I = 0.29, p = 0.011), and groundhogs (I = 0.37, p = 0.005). The LISA results revealed significant spatial clustering of rabies in raccoons and foxes in southeastern New Jersey and in groundhogs in northern New Jersey. These findings could guide the development of targeted oral rabies vaccination programs in high-risk New Jersey counties, reducing rabies exposure among domestic animals and humans.

Geospatial patterns, and individual and community levels factors of cesarean section deliveries in Ethiopia: A spatial and multilevel analysis.

Cesarean Section (CS) is the most popular surgery worldwide in obstetric care to save a mother's or the fetus's life. The prevalence of CS delivery in Ethiopia was 0.7% and 1.9% in 2000 and 2016 respectively and its spatial distribution and variation in Ethiopia are limited. This study provides evidence for healthcare providers and pregnant women on the national CS geospatial distribution and variation to promote evidence-based decision-making and improve maternal and neonatal outcomes. Therefore, this study aimed to determine geospatial patterns and individual and community-level factors of CS deliveries in Ethiopia. A secondary data analysis of 5,527 weighted samples of mothers using the 2019 Ethiopian mini demographic and health survey was conducted. The spatial hotspot analysis using Getis-Ord Gi* hot spot analysis of ArcGIS version 10.7.1 was used to show the spatial cluster of CS and multilevel mixed effect logistic regression analyses were employed. Statistical significance was declared at p-value <0.05 and adjusted odds ratio (AOR) with 95% confidence interval (CI) was reported. The prevalence of CS delivery in Ethiopia was 5.4% with 95%CI (4.6%, 6.4%). The spatial autocorrelation shows CS was clustered in Ethiopia (global Moran's index = 1.009, and p-value<0.001). Spatial hotspot indicates CS was prevalent in Addis Ababa, Diredewa, Oromo, and Somali regions. The odds of CS delivery were higher among mothers aged 24-35: AOR = 1.98, 95% CI (1.3, 3.1), and 35-49: AOR = 3.7, 95% CI(2.2, 6.1), live with female household heads: AOR = 1.9, 95% CI (1.2, 3.1), mothers with primary AOR = 1.6, 95% CI (1.07, 2.7), secondary: AOR = 2.4, 95% CI (1.3, 4.25) and higher education: AOR = 2.06, 95% CI (1.03, 4.2), multiple births: AOR = 8.1, 95% CI (3.4, 19.74), mothers in Addis Ababa: AOR = 3.4, 95% CI (1.1, 11.9) and Diredewa: AOR = 7, 95% CI (1.23, 40.7) as compared to their counterparts. In Ethiopia, CS deliveries remain below the World Health Organization estimate with distinct geospatial variation. Geographically, CS delivery is concentrated in urban areas like Addis Ababa and Diredewa, as well as in parts of the Somali and Oromia regions. Maternal age, female household head, Muslim religion, higher educational status, and multiple births at the individual level, and region at the community level were factors of CS delivery in Ethiopia. Therefore, efforts should be geared towards strategies and interventions to ensure fair access to CS delivery in line with WHO recommendations, especially in the regions where the CS delivery rate is below WHO estimates in Tigray, Amhara, Afar, and Benishangul Gumze regions.

Relationship between social vulnerability and community resilience: A geospatial study in the context of natural disasters

Social vulnerability and resilience are critical factors for disaster risk management. Despite the significant progress in research on both concepts, only some studies have explored the empirical relationship between them. The relationship between community resilience and social vulnerability to natural disasters in Chile was studied using empirical and geospatial analysis. We used the Social Vulnerability Index (SoVI) and the Community Resilience Index (BRIC), previously calculated nationally for Chile. Based on these indicators, we constructed a matrix to classify the 3100 districts into high, medium, and low levels of vulnerability and resilience. In addition, we performed a spatial autocorrelation analysis using the Global Moran Index. Our results indicate that: Vulnerability and resilience are related concepts, but are not opposite within a continuum, (II) Rather than being randomly distributed, districts with higher (or lower) capacities to prepare for, respond to, and recover from a disaster tend to cluster geographically; (III) the districts with the highest levels of resilience and lowest levels of vulnerability were located in the main cities of the country. We expect that a better understanding of the relationship between vulnerability and resilience in each territory will help institutions in charge of disaster management to identify communities most susceptible to damage and least capable of recovering from a disaster. Consequently, it will facilitate the design and implementation of policies, programs, and plans best adapted to the needs of each community.

Carbon emission intensity measurement and spatial effect of high energy consuming industries: evidence from China

&lt;p style="text-indent:20.0pt"&gt;&lt;span style="font-size:10.5pt"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;,serif"&gt;&lt;span style="color:black"&gt;&lt;span class="fontstyle01" style="font-family:仿宋"&gt;&lt;span style="color:black"&gt;&lt;span lang="EN-US" style="font-size:10.0pt"&gt;&amp;nbsp;High energy consumption industry is an important source of carbon dioxide emissions, and reducing pollution and carbon is an important measure for China to achieve the goal of "2030 carbon peak 2060 carbon neutral". Based on the improvement of the traditional calculation method of IPCC carbon emission intensity, this paper measures the carbon emission intensity, selects the data of high energy consuming industries in 30 provinces in China from 1997 to 2022 as samples, uses the stipat model and Moran index to analyze the correlation of the influencing factors of carbon emission, and uses the spatial measurement model to study the spatial effect of carbon emission intensity. The results show that: first, the overall carbon emission intensity of high energy consuming industries shows a downward trend, with typical spatial heterogeneity. During the sample period, the carbon emission intensity of high energy consuming industries in 30 provinces in China was calculated based on the IPPC method, with the overall decline. Second, the carbon emission intensity of high energy consuming industries has significant spatial autocorrelation characteristics. According to the global Moran index, the center of gravity moves from east to West as a whole. Third, the carbon emission intensity of high energy consuming industries is affected by multiple environmental factors. Industrial structure (INS), regional gross domestic product (GDP) and regional economic development (ECO) have a significant impact. Fourth, the carbon emission intensity of high energy consuming industries has a significant spatial spillover effect. According to the regression results of spatial Dobbin model with double fixed effects, the direct and indirect effects of carbon emission intensity of high energy consuming industries are significant.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

Congenital and acquired hypothyroidism: Temporal and spatial trends in France from 2014 to 2019

PurposeTo assess the incidence of congenital hypothyroidism (CH) and acquired hypothyroidism (AH) between 2014 and 2019 in continental France. MethodsNew cases of CH and AH were identified using the French National Health Data System (Système Nationale des Données de Santé, SNDS). Temporal trends were studied using linear regression models. Spatial distributions were studied using Moran's global index (I) and the statistical method and local indicators of spatial association. ResultsThe incidence of permanent CH in females increased by 8.9 % per year (2014: 36.9 [31.1–43.7] per 100,000 birth-years vs. 2019: 51 [43.9–59.3] per 100,000 birth-years, p < 0.01). The incidence of AH decreased between 2014 and 2019 for both females (2014: 535.7 [533.2–538.2] per 100,000 person-years vs 2019: 335.5 [333.6–337.4] per 100,000 person-years, p < 0.01) and males (2014: 197.5 [195.9–199] per 100,000 person-years vs 2019: 141.7 [140.4–142.9] per 100,000 person-years, p < 0.01). The incidence of hypothyroidism was high in the Nord-Pas-De-Calais and Lorraine regions (CH and AH). ConclusionsThe incidence of permanent CH in females has increased over time. AH incidence decreased. It seems necessary to investigate environmental factors in the disparity of incidence distribution.

From space to service: Measuring the accessibility in a space-time heterogeneity perspective

Accessibility has been identified as the primary motivating factor for the use of daily service facilities. Existing methods for measuring the accessibility of service facilities primarily focus on location accessibility, while little research extends it to service accessibility, which includes both reaching the location and obtaining the services. This paper proposes a novel approach to measuring service accessibility, termed the Space-Time Heterogeneous Gaussian Two-Step Floating Catchment Area (STH-G2SFCA). Firstly, a fine-grain heterogeneity analysis of service accessibility is constructed based on the supply-demand relationship. Secondly, a space-time search strategy is proposed with a Gaussian distance attenuation function, optimized by the decay rate of service attraction. Lastly, we introduce a supply-demand score and the Global Moran's index to assess the space-time patterns of service accessibility. A case study is conducted in the central urban area of Wuhan, China, with catering facilities as an example. The experimental results show that the STH-G2SFCA method can more precisely assess service accessibility, which is often overestimated in classic methods, and capture the temporal dynamics process of service accessibility within a day. The proposed method promises to be replicated in other service facilities, providing urban planners with a valuable tool for digital planning.

A Highway Monitoring Necessity Grading Method Considering Traffic State Stability

In order to meet the basic requirements of expressway operation and maintenance management in the new era,refine the monitoring requirements of different expressway sections,and maximize the operation benefit of expressway monitoring system,this paper puts forward the stability of traffic state related to overtaking frequency,speed difference and traffic fluctuation at the entrance and exit,and obtains the grading detection method of expressway through entropy method and global Moran index,which provides a basis for optimizing monitoring and improving road safety.

Space-time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador.

Intestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables. In Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran's index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined. During the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services. This study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

Investigation into Spatial and Temporal Differences in Carbon Emissions and Driving Factors in the Pearl River Delta: The Perspective of Urbanization

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of “strong in the east and weak in the west”, with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises.

Are Pathogenic Leptospira Species Ubiquitous in Urban Recreational Parks in Sydney, Australia?

Leptospirosis is a zoonotic disease caused by the spirochete bacteria Leptospira spp. From December 2017 to December 2023, a total of 34 canine leptospirosis cases were reported in urban Sydney, Australia. During the same spatio-temporal frame, one locally acquired human case was also reported. As it was hypothesised that human residents and companion dogs might both be exposed to pathogenic Leptospira in community green spaces in Sydney, an environmental survey was conducted from December 2023 to January 2024 to detect the presence of pathogenic Leptospira DNA in multipurpose, recreational public parks in the council areas of the Inner West and City of Sydney, Australia. A total of 75 environmental samples were collected from 20 public parks that were easily accessible by human and canine visitors. Quantitative PCR (qPCR) testing targeting pathogenic and intermediate Leptospira spp. was performed, and differences in detection of Leptospira spp. between dog-allowed and dog-prohibited areas were statistically examined. The global Moran's Index was calculated to identify any spatial autocorrelation in the qPCR results. Pathogenic leptospires were detected in all 20 parks, either in water or soil samples (35/75 samples). Cycle threshold (Ct) values were slightly lower for water samples (Ct 28.52-39.10) compared to soil samples (Ct 33.78-39.77). The chi-squared test and Fisher's exact test results were statistically non-significant (p > 0.05 for both water and soil samples), and there was no spatial autocorrelation detected in the qPCR results (p > 0.05 for both sample types). Although further research is now required, our preliminary results indicate the presence of pathogenic Leptospira DNA and its potential ubiquity in recreational parks in Sydney.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

Evaluating spatial analysis of tuberculosis prevalence to identify priority districts or municipalities that need policy attention in West Java

Purpose: This study assesses the spatial autocorrelation of tuberculosis (TB) with population density, poverty, personal hygiene and environmental sani- tation (perilaku hidup bersih sehat, PHBS), and housing quality to identify critical areas for targeted TB management in West Java. Methods: This ecological study analyzes secondary data from all TB patients across 27 regencies/cities in West Java Province for 2022. Spatial patterns were examined using the Global Moran's Index and Local Indicators of Spatial Association (LISA). Results: The analysis revealed significant clustering of TB prevalence with population density (p-value = 0.001) and PHBS (p-value = 0.047). At the same time, dispersion was noted with poverty (p-value = 0.355) and a non-significant clustering with housing quality (p-value = 0.270). Notably, ten regencies/cities exhibited significant local spatial autocorrelat- ion, indicating priority areas for intervention. Conclusion: TB prevalence in West Java show a positive spatial correlation with population density and PHBS, indicating clustered patterns that necessitate focused policy attention. The absence of a spatial correlation between poverty and housing quality suggests different intervention strategies may be required. TB intervention should be prioritized in areas like Bekasi Regency, Bekasi City, Bogor Regency, Bogor City, Cianjur Regency, and Depok City, where TB prevalence is significantly associated with key spatial factors.

Spatio-temporal distribution of hospitalizations for chronic Chagas disease and risk factors associated with in-hospital mortality and surgical intervention in Chile.

Chagas disease (CD) is a neglected parasitic zoonotic disease that affects over 6 million people worldwide. We conducted a retrospective study to analyze the spatiotemporal trends and risk factors for hospitalization rates of CD with cardiac and digestive diagnoses in Chile. We used the Mann-Kendall analysis for temporal trends, Global Moran's Index, and Local Indicators of Spatial Association to identify spatial autocorrelation, and regression models to determine the risk factors associated with in-hospital mortality and surgical intervention. Between 2010 and 2020, a total of 654 hospitalizations were reported, corresponding to 527 individuals. The hospitalization rate steadily decreased over the years (t = -0.636; p = 0.009). The Global Moran's I for the study period showed a positive spatial autocorrelation for hospitalization municipality and for residence municipality of CD patients (I = 0.25, p<0.001 and I = 0.45, p<0.001 respectively), indicating a clustering of hospitalizations in northern municipalities. The most frequent diagnosis was a chronic CD with digestive system involvement (55.8%) followed by a chronic CD with heart involvement (44.2%). The highest percentage of hospital discharges was observed among males (56.9%) and in the 60-79 age group (52.7%). In-hospital mortality risk was higher with increasing age (OR = 1.04), and in patients with cardiac involvement (OR = 2.3), whereas factors associated with the risk of undergoing a surgical intervention were sex (OR = 1.6) and diagnosis of CD with digestive involvement (OR = 4.4). The findings of this study indicate that CD is still a significant public health burden in Chile. Efforts should focus on improving access to timely diagnoses and treatment, reducing disease progression and hospitalization burden, and supporting clinicians in preventing complications and deaths.

Research on the coordinated development of provincial urbanization and carbon emission efficiency of construction industry in China

BackgroundEnergy conservation and emission reduction policies restrict the economic and social development of all countries in the world, and the impact on China, which has low urbanization, is more serious. In the process of promoting urbanization, the pressure of carbon emission reduction in the construction industry has increased, and the high emissions of the construction industry have made the low-carbon development of cities face severe challenges. China is at a critical stage of urbanization development, and there is become a general consensus on how to improve the carbon emission efficiency of the construction industry. The interaction between urbanization and the carbon emission efficiency of the construction industry is a long-term and complex process. As one of the industries contributing to China’s urbanization process and carbon emissions, it is of great practical significance to explore the coordination relationship between urbanization and the carbon emission efficiency of the construction industry (CEECI) to realize the goal of “double carbon”, promoting urbanization construction and solving the problem of “green development”. Taking 30 provinces in China as the research target area, the double weighted summation method and the undesirable output superefficiency window-EBM-DEA model are used to measure the provincial urbanization level and CEECI, respectively. Then, the coupling coordination degree model of the relative development index is introduced, and the spatial autocorrelation model and the spatial and temporal differentiation characteristics of the coordination level of urbanization and the CEECI are analysed.ResultsFrom 2010 to 2021, China’s urbanization level increased steadily, but the growth rate gradually decreased. There were significant differences in urbanization levels among provinces. The eastern provinces have a higher level of urbanization but lack an impetus in the later period, while the western provinces have a lower level of urbanization but a faster growth rate. The low-carbon development trend of China’s construction industry is good, and the overall development of the CEECI shows an “N” type, and the inflection points appear in 2013 and 2018. The interprovincial urbanization level is significantly different from that of the CEECI, and the development form of the central and western provinces is better than that of the eastern provinces. The coordination level of urbanization and the CEECI in China is transitioning from the running-in stage to the coordination stage, and the coupling coordination degree between systems is on the rise, while the relative development degree is on the decline. The spatial distribution pattern is in a dynamic state of change, and the overall distribution pattern is “high in the east and low in the central and western regions”. The differences among provinces were significantly decreased, with 63.33% of provinces at the high running-in level. The provinces that entered the coordination stage were mainly located in the eastern region, and only Beijing was in the coordination stage by the end of the study. In addition, 90% of the provinces exhibited lagging efficiency, and only Jiangxi, Guangxi and Chongqing, which had low coordination levels maintained synchronous development of the two systems. The coordination level between provincial urbanization and the CEECI showed a positive spatial distribution, the global Moran index showed a “V” shape trend, and the spatial dependence of the coordination level between the two systems gradually weakened. In the local spatial distribution, there are two types of convergence: high and low.ConclusionsThe coordination degree of urbanization and the CEECI analysed in this study is an extension of the research on the relationship between the two. By integrating the two into a unified framework, the method of combining quantitative and qualitative analysis is used to further explore the coordination relationship between the two, which not only enhances the scientificity and accuracy of the research but also extends the breadth and depth of relevant theoretical research. At the same time, according to the coordination type between urbanization and the CEECI, China should propose corresponding targeted coordination and optimization paths from the perspective of urbanization and low-carbon development of the construction industry to achieve high-quality development of China’s economy and society.

Spatial autocorrelation and driving factors of carbon emission density of crop production in China.

Mitigating carbon emissions from crop production is essential for addressing global warming. At a macro-level, existing studies have often relied on the calculation of carbon emission intensity of crop production to understand comparable carbon effects between regions. However, this approach obscures the differences in crop planting scale and natural attributes across regions, leaving room for improvement in the methods and scope of analysis. To extend the existing research, we proposed an idea for calculating the carbon emission density of crop production based on planting area. Additionally, we developed an analytical framework for driving factors of carbon emission density of crop production from a spatial interaction perspective. The provincial carbon emission density of crop production in mainland China between 2000 and 2020 was calculated, and spatial econometric models were utilized to investigate the spatial autocorrelation and driving factors. The results indicate that the national average carbon emission density of crop production was 1.462 t/hm2 annually. Over 21years, the carbon emission density of agricultural materials, rice cultivation, soil management, and straw burning evolved from 0.384 to 0.470 t/hm2, 0.409 to 0.367 t/hm2, 0.171 to 0.169 t/hm2, and 0.317 to 0.448 t/hm2, respectively. The global Moran's index indicated a positive spatial autocorrelation of carbon emission density of crop production and the subdivided carbon sources among provinces. Regarding direct effects, an increase in the proportion of paddy fields in cropland composition and irrigation efficiency would significantly promote the carbon emission density, while factors such as cropland area, multiple cropping, agricultural personnel numbers, departmental proportion, and disaster degree would decrease the local carbon emission density. Certain factors, such as cropland area and agricultural disasters, had a spatial spillover effect on carbon emission density between provinces. The study suggests harnessing key drivers and spatial spillover effects to achieve regional low-carbon crop production.

Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China

It is particularly important to analyze the influencing factors of urban green competitiveness and the spatial distribution characteristics under the constraint of carbon emissions. The research ideas of this paper: firstly, this paper selects the carbon emission intensity and urban green competitiveness data in 2010, 2013, 2015, 2018 and 2020 for panel data regression; secondly, this paper applies a variety of methods to carry out the robustness test, and the results show that the regression model is better, and analyzes the development of urban green competitiveness for the heterogeneity of large cities and small cities; subsequently, the use of inverse geographic matrix to analyze the spatial correlation between the global Moran index and local Moran index for urban green competitiveness, and to analyze the spatial and temporal pattern evolution of urban green competitiveness. The conclusions of the study show that, from the viewpoint of influencing factors, carbon emission intensity presents a significant negative effect on the development of urban green competitiveness, and has a greater impact on the green competitiveness of large cities than that of small cities. From the perspective of spatial correlation, urban green competitiveness presents positive spatial correlation and shows a growing trend over time. Finally, this paper puts forward relevant policy recommendations based on the findings of the study.