High-high Clusters Research Articles

Spatiotemporal dynamics and driving factors of ecosystem services value in Lanzhou City, China

Aligned with the imperatives of national ecological civilization construction, the systematic investigation into the intricate interplay between shifts in land utilization and the assessment of ecosystem services plays a pivotal and indispensable role in advancing ecological civilization. This endeavor holds significant implications. It aids in optimizing the ecological landscape at the regional level and fosters harmonious coexistence between humanity and the natural world. The study utilizes land-use remote sensing interpretation data from three time periods (2000, 2010, and 2020) and employs various methodologies, including equivalent factor coefficient correction, sensitivity analysis, and spatial autocorrelation. The objective is to uncover the spatiotemporal dynamics of land-use changes and Ecosystem Service Value (ESV) in Lanzhou City. Furthermore, geographic detectors are applied to explore the driving factors influencing ESV spatial heterogeneity and their interactions. The research findings indicate the following: (1) From 2000 to 2020, grassland and cropland were the predominant land-use types in Lanzhou City, with cropland and urban land experiencing the most active changes. (2) ESV in Lanzhou City increased from 179.37 billion RMB in 2000 to 193.86 billion RMB in 2020, reflecting an ESV total growth rate of 8.07% and a gradual improvement in the ecological environment. Spatially, ESV exhibits a “west high, east low” distribution pattern, with the center shifting towards the northwest and southeast, gradually reducing spatial imbalance. (3) Analysis of ESV spatial autocorrelation reveals that high-high clusters are predominantly found within the Tulu Gou National Forest Park and the Xinglong Mountain National Natural Reserve, while low-low clusters are primarily concentrated in the central urban area of Lanzhou City. Over the period from 2000 to 2020, the spatial clustering effect of ESV within the study area has progressively intensified. 4)NDVI, precipitation, and GDP emerge as pivotal factors influencing spatial differentiation within Lanzhou City, with natural and societal elements exerting interactive effects on ESV spatial disparities. The research results integrate environmental considerations into the decision-making process, offering valuable insights for formulating targeted ecological protection policies in Lanzhou City. This study embodies concrete measures taken by Lanzhou City in practicing China’s concept of “green water and green mountains are golden silver mountains,” providing a theoretical basis for the harmonious and sustainable development of the ecological economy.

Spatial heterogeneity in unintended pregnancy and its determinants in India

BackgroundUnderstanding the geographic variation of unintended pregnancy is crucial for informing tailored policies and programs to improve maternal and child health outcomes. Although spatial analyses of unintended pregnancy have been conducted in several developing countries, such research is lacking in India. This study addresses this gap by investigating the geographic distribution and determinants of unintended pregnancy in India.MethodsWe analysed data from the National Family Health Survey-5 encompassing 232,920 pregnancies occurring between 2014 and 2021 in India. We conducted a spatial analysis to investigate the distribution of unintended pregnancies at both state and district levels using choropleth maps. To assess spatial autocorrelation, Global Moran’s I statistic was employed. Cluster and outlier analysis techniques were then utilized to identify significant clusters of unintended pregnancies across India. Furthermore, we employed Spatial Lag Model (SLM) and Spatial Error Model (SEM) to investigate the factors influencing the occurrence of unintended pregnancies within districts.ResultsThe national rate of unintended pregnancy in India is approximately 9.1%, but this rate varies significantly between different states and districts of India. The rate exceeded 10% in the states situated in the northern plain such as Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, as well as in the Himalayan states of Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. Moreover, within these states, numerous districts reported rates exceeding 15%. The results of Global Moran’s I indicated a statistically significant geographical clustering of unintended pregnancy rates at the district level, with a coefficient of 0.47 (p < 0.01). Cluster and outlier analysis further identified three major high-high clusters, predominantly located in the districts of Arunachal Pradesh, northern West Bengal, Bihar, western Uttar Pradesh, Haryana, Delhi, alongside a few smaller clusters in Odisha, Madhya Pradesh, Uttarakhand, and Himachal Pradesh. This geographic clustering of unintended pregnancy may be attributed to factors such as unmet needs for family planning, preferences for smaller family sizes, or the desire for male children. Results from the SEM underscored that parity and use of modern contraceptive were statistically significant predictors of unintended pregnancy at the district level.ConclusionOur analysis of comprehensive, nationally representative data from NFHS-5 in India reveals significant geographical disparities in unintended pregnancies, evident at both state and district levels. These findings underscore the critical importance of targeted policy interventions, particularly in geographical hotspots, to effectively reduce unintended pregnancy rates and can contribute significantly to improving reproductive health outcomes across the country.

Discovering Sericulture Clusters Through the Use of Local Indicators of Spatial Association (LISA)

The aim of this study is to analyze the spatial pattern of sericulture in Türkiye with spatial analysis approaches, taking into account the data of 81 provinces, by creating a database based on Geographic Information Systems (GIS). This study employed data from the TURKSTAT, which included the amount of fresh cocoon produced and the silkworm egg boxes opened, between 2004-2022. In order to evaluate the relationship between the cocoon yield in each province and the cocoon yield in neighboring provinces, Moran's I measurements were made for the relevant time periods, and each period's Moran's I scatter plot was created. The Local Spatial Association Indicator (LISA) was used to assess the spatial structure of fresh cocoon yield. There was determined spatial clustering and a positive spatial association for cocoon yield, and all of the Moran's I values were found to be more than 0. The LISA maps were demonstrated the distinct local clustering patterns in the cocoon yield. More local clusters were identified (High-High or Low-Low) compared to local spatial outliers (High-Low or Low-High). High-High clusters were generally identified in the Marmara Region. Important implications have been obtained in terms of sustainability, such as how to take advantage of hotspot spillover effects and prioritize hotspot locations when allocating resources. As a result, spatial relations should be taken into account in the policies to be developed for sericulture and more activities should be carried out on sericulture.

Ecological Environment Quality Monitoring of Qitai Oasis Based on Land Use/Cover Change and Remote Sensing Ecological Index

Based on Landsat remote sensing images, this study conducted a quantitative assessment of the spatiotemporal alterations in the ecological environment quality at the Qitai Oasis in Xinjiang, situated at the northern foothills of the Tianshan Mountains, triggered by human activities. This assessment was conducted through a combination of the remote sensing ecological index （RSEI） and land use/cover change （LUCC）. In addition, we used spatial autocorrelation analysis and geographic detector to examine the spatial distribution characteristics and its influencing factors of RSEI. The results demonstrated that： ① The average RSEI values for Qitai Oasis in four different years （2001, 2007, 2014, and 2022） were 0.392, 0.423, 0.452, and 0.438, respectively, indicating a lower overall level of ecological environment quality in the area. The spatial distribution highlighted a poor level in the northern and southern regions, with the central area displaying noticeably better quality. The temporal trend showed an initial growth followed by a subsequent decline, with an overall tendency toward an increase. ② The ecological environment quality in the Qitai Oasis exhibited positive spatial autocorrelation, with low-low clustering primarily concentrated in the northern part of the oasis, closer to the Gurbantünggüt Desert. Further, the high-high clustering was featured by an aggregation from scattered distribution towards the central agricultural areas of the oasis. ③ Natural factors had a negligible influence on the spatial distribution of RSEI in Qitai Oasis, and the primary catalyst for RSEI changes was LUCC. ④ RSEI aptly portrayed the present condition of the ecological environment quality in the Qitai Oasis. Changes in RSEI of natural ecosystems, such as RSEI of grassland and bare land, provided significant cues, illustrating the variations in the ecological environment quality of arid regions.

Spatiotemporal variations of farmland crop residue burning in China from 2013 to 2022

Farmland crop residue burning (FCRB) significantly impacts air quality, necessitating understanding its spatiotemporal variations to evaluate control measures and guide future policies. This study analyzes FCRB across China from 2013 to 2022 using remote sensing technology and spatiotemporal analysis. Results indicate that the first phase (2013–2017) of the “Clean Air Action” control measures has been proven ineffective, with a 30.9 % increase in FCRB. Effective measures implemented in the second phase (2018–2020) resulted in a reduction of 41.4 % and led to a decrease of 34.3 % in 2022 compared to 2013. FCRB exhibits significant seasonal and regional variations, with a 69 % decrease in autumn and a 23 % increase in spring. While the variations in FCRB across regions are found to be significantly negatively correlated with the stringency of control measures, there is a 61.2 % reduction in the central region, 42.4 % in the western region, 29.7 % in the eastern region with the highest intra-regional disparities, and only 4.5 % in the northeastern region, which is attributed to the high FCRB in Heilongjiang province. FCRB demonstrates the “club convergence” phenomenon and the spillover effects of adjacent provinces, leading to high-high clusters in the northeastern region, emphasizing the need for region-specific control measures (e.g., Heilongjiang province in the northeastern) and the implementation of similar control measures in adjacent areas. Additionally, it is necessary to avoid the rebound phenomenon determined by the analysis of the kernel density estimation curve. Our findings underscore the importance of tailored strategies in managing FCRB and improving air quality in critical areas.

Diagnosis of ecological security and the spatial heterogeneity of its driving factors in the mining-impacted watershed, based on ecosystem health-risk-services framework

A comprehensive diagnosis of ecological security (ES) and its driving mechanisms in the watershed under mining influence is essential for the conservation and restoration of watershed ecosystems. Few studies have comprehensively evaluated ES by considering the condition of the ecosystem itself, the ecological function it provides, and the risk it faces. Therefore, by innovatively synthesizing ecosystem health (EH), ecological risk (ER), and ecosystem services (ESs), an ES evaluation framework based on EH-ER-ESs was constructed. Based on quantifying the ES of a typical watershed with mines clustered in China’s Northern Sand Prevention Belt, a spatial correlation analysis was used to elucidate the spatial differentiation characteristic of ES and verify the necessity of the evaluation framework. The driving mechanism of ES and the spatial heterogeneity of its key driving factors were explored using the Geodetector model and the geographically-weighted regression model. The results show that (1) ES was generally medium-security, and very low-security areas (8.16%) were mainly concentrated in the eastern mining aggregation areas. (2) ES showed high-high and low-low spatial clusters. Overall, ES was positively correlated with EH and ESs, while negatively correlated with ER, but there were individual cases of “high health-low security” and “high services-low security”, and combining EH, ER, and ESs to diagnose ES comprehensively was necessary. (3) Drought index, vegetation cover, distance from mining land, population density, and CONTAG were the key driving factors of ES. The explanatory power of factor interactions was higher than that of single factors. The impact of driving factors showed significant spatial heterogeneity, with the effects of mine agglomeration on ES primarily concentrated in the east. The EH-ER-ESs framework can be used for ES evaluation in other ecosystems, and the findings provide important guidance for conducting integrated watershed management.

The Synergistic Evolution Characteristics and Influencing Factors of Tourism Economy and Urban Green Development Efficiency in the Yellow River Basin

In the context of the “ecological priority and green development” strategy, examining the co-evolution between the tourism economy and the efficiency of urban green development can offer both theoretical insights and quantitative foundations to support ecological preservation and high-quality development in China’s Yellow River Basin. This research utilized approaches such as the Haken model and the geographically and temporally weighted regression model to investigate the spatiotemporal patterns, synergistic characteristics, and driving factors of the tourism economy and urban green development efficiency within the Yellow River Basin. The findings reveal the following: (1) Regional disparities in the tourism economy are progressively widening, whereas the efficiency of green development tends to decline. Furthermore, both the tourism economy and urban green development efficiency display “high-high clustering” and “low-low clustering” spatially. (2) The synergistic evolution of the two systems displays spatial characteristics of transitioning from polarization to trickle-down effects. (3) Natural factors such as topography and vegetation coverage, as well as human economic factors like industrial structure and the degree of openness, positively promote the synergy. However, elements such as temperature, precipitation, economic development level, and openness to innovation have a certain inhibitory effect on the synergistic evolution.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data.

Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest

Heavy metal(loid)s (HMs) in agricultural soils not only affect soil function and crop security, but also pose health risks to residents. However, previous concerns have typically focused on only one aspect, neglecting the other. This lack of a comprehensive approach challenges the identification of hotspots and the prioritization of factors for effective management. To address this gap, a novel method incorporating spatial bivariate analysis with random forest was proposed to identify high-risk hotspots and the key influencing factors. A large-scale dataset containing 2995 soil samples and soil HMs (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn) was obtained from across Henan province, central China. Spatial bivariate analysis of both health risk and ecological risks revealed risk hotspots. Positive matrix factorization model was initially used to investigate potential sources. Twenty-two environmental variables were selected and input into random forest to further identify the key influencing factors impacting soil accumulation. Results of local Moran's I index indicated high-high HM clusters at the western and northern margins of the province. Hotspots of high ecological and health risk were primarily observed in Xuchang and Nanyang due to the widespread township enterprises with outdated pollution control measures. As concentration and exposure frequency dominated the non-carcinogenic and carcinogenic risks. Anthropogenic activities, particularly vehicular traffic (contributing ∼37.8 % of the total heavy metals accumulation), were the dominant sources of HMs in agricultural soils. Random forest modeling indicated that soil type and PM2.5 concentrations were the most influencing natural and anthropogenic variables, respectively. Based on the above findings, control measures on traffic source should be formulated and implemented provincially; in Xuchang and Nanyang, scattered township enterprises with outdated pollution control measures should be integrated and upgraded to avoid further pollution from these sources.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Carbon metabolism modelling and regulation in Beijing

Land use and cover change leads to significant increases in urban carbon emissions. Considering the insufficiency of spatially fine-grained management in previous carbon emission studies, this study integrated top-down and bottom-up methods to simulate urban carbon metabolism. Metabolic actors were allocated to grids based on land use and POI data and then attributed to districts. Results indicated that from 2000 to 2018, Beijing's Carbon Imbalance Index increased by 55.81% in 2010 and declined by 12.46%. Carbon emission dominated the changes in the Carbon Imbalance Index, with the Production and Supply of Electric Power actors consistently accounting for over 25.96%, while the contribution of Urban and Transportation actors reached 20.11% and 27.74% in 2018, respectively. The metabolic actors exhibited a gradient distribution pattern from southeast to northwest. Artificial actors' carbon emissions decreased from the center to the periphery and spread along the road network, with the High-High cluster in the southeast mainly contributed by Manufacturing (22.33%) and the Production and Supply of Electric Power (20.16%) actors. Spatial distribution changes of Natural actors mainly occurred in Daxing and Chaoyang districts, whose absorption area extended by 45% and 37.26%, respectively, contributing to an increase in carbon absorption by 2.47 and 0.15 times. The Semi-natural actor exhibited net carbon emissions, with the High-High cluster in the southeast zone contributing 60.96% of emissions in 2000. We also categorized Beijing into four Areas based on the relationship between carbon emission and absorption. Targeted policy recommendations were formulated for different districts and metabolic actors.

Analysis of the epidemiological characteristics and influencing factors of tuberculosis among students in a large province of China, 2008–2018

This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence.

Climate change ecological vulnerability and hotspot analysis of himalayan forests in North-Eastern region, India

The Himalayan forests are vulnerable to climate change leading to disturb the current flow of critical ecosystem services to the dependent population. Present study attempts to evaluate the current vulnerability status of major forest types in Nagaland, India due to climate change along with identifying the hot spots for mitigating the vulnerability of the forests. Vulnerability of mixed moist deciduous, pine, wet hill and wet temperate forests were evaluated using the IPCC framework. Local Moran's I analysis, hot spot analysis and a histogram analysis of the vulnerability were made in QGIS. Field data such as Shannon-Wiener Index and Importance Value Index along with secondary data such as edaphic factors, vegetation factors and climatic variables were used for vulnerability analysis. Analysis results that mixed moist deciduous forests had the highest area in under high-high clusters, hot spots and was highly vulnerable. The result suggests that very dense or moderately dense forest cover were less vulnerable than low density forests in the region. The study provides baseline information along with identification of vulnerable hot spot forests for future climate change policy and management of resources at the division level. The study suggests strengthening the less dense forests along with increasing the biodiversity through suitable strategy for mitigating the vulnerability.

Geographic Patterns of Youth Suicide in San Diego County

ObjectiveOur objectives were to evaluate for any cluster patterns of youth suicide deaths and characterize the level of child opportunity in the communities where suicide deaths occurred. MethodsDecedents <18 years were identified from San Diego County Medical Examiner death reports from 2000 to 2020. We mapped decedents’ residential Zone Improvement Plan (ZIP) codes and calculated suicide rates per 10,000 youths. ZIP codes identified in overlapping spatial statistical approaches — the spatial scan statistic and Local Moran with Empirical Bayes (EB) rates — were considered a cluster for the final analysis. We obtained Child Opportunity Index (COI) scores for each ZIP code to determine if there were differences in: 1) ZIP codes with suicide deaths compared to ZIPs with no deaths 2) differences in distribution of suicide death rates across quintiles of COI. ResultsScan statistic identified 25 ZIP codes within a cluster (RR 2.6, P = 0.00066). Local Moran with EB rates identified two ZIP codes as a high-high cluster (P < 0.05). The location identified as a cluster in both approaches was in Alpine. The median COI for ZIP codes with suicide deaths was higher at 63.5 (IQR 38–83) compared to 47 (IQR 22.5–75.5) for ZIP codes without suicide deaths. There was a significant difference in suicide rates between Very Low and Moderate levels of Overall opportunity (P = .013). ConclusionWe identified a cluster of youth suicides in one of the most populous counties in the country. These findings serve to inform policies and prevention programs that aim to mitigate youth suicide mortality.

Perceptions Matter! Active Physical Recreation Participation of Children with High and Low Actual and Perceived Physical Competence.

Emerging evidence suggests that the accuracy of a child's perceived physical competence (PPC) impacts participation in physical activity. We examined differences in active physical recreation (APR) participation based on clusters of high and low motor competence and perceptions from grades 3-5. Participants were a longitudinal sample (n = 155; 85 girls) of grade three, four, and five children. Fundamental motor skills (FMSs) were assessed using the TGMD-2, perceptions were assessed using the Self-Perception Profile for Children, and APR was measured using the Children's Assessment of Participation and Enjoyment. K-means cluster analysis was used to create four clusters per grade based on participants' FMSs and PPC z-scores. Differences in FMSs, PPC, and APR with cluster group as a factor were examined using a series of factorial ANOVAs. In each grade, participants in the high-high cluster participated in significantly more APR than those in the low-low cluster (p = 0.002 in grades three and four; p < 0.001 in grade five). Among the less accurate clusters (e.g., low FMSs with high PPC) was a trend toward positive FMSs growth among the children with higher perceptions. Results show that combinations of FMSs and PPC influence patterns of engagement or disengagement in active physical recreation persistently across middle childhood.

Coupling coordination relationship between urbanization and water environment in China

The intensification of urbanization (U_UR) in China exacerbates the pressure on the water environment (U_WE). The U_UR and U_WE exhibit incredibly intricate and intertwined interactions. The coordination between U_UR and U_WE is crucial for the sustainable development of cities. Hence, a comprehensive evaluation index system was developed to analyze the coupling coordination relationship and spatial effects between the U_UR and U_WE in China, based on the entropy weight method, the coupling coordination degree (CCD) model, and the spatial autocorrelation analysis method. The data was collected from the China Statistical Yearbook (2013–2023), the China Urban Construction Statistical Yearbook (2012–2022), and provincial statistical yearbooks. The results indicated that the overall trend of CCD in China showed an upward trajectory, increasing from 0.44 to 0.55 in 2012 to 0.56–0.66 in 2022. The average values of CCD for 31 provinces rose from 0.51 in 2012 to 0.61 in 2022, with a growth rate of 19.61%. The regional development level of CCD exhibited spatial heterogeneity, characterized by East > North > South > Northeast > Northwest > Southwest. However, the development of the U_WE subsystem lagged behind the U_UR subsystem, and there was a significant spatial imbalance in the CCD between the U_UR subsystem and U_WE subsystem in China from 2012 to 2022. The High-High Cluster areas of CCD were concentrated in the eastern part of China, while the Low-Low Cluster areas were concentrated in the western part. The research can provide theoretical and technical support to promote the sustainability of urban development and water environment protection.

Identifying driving factors and their interacting effects on accumulation of heavy metals in cultivated soils based on optimal parameter geographic detector model

Identifying the factors and their interactive effects on soil heavy metals (HMs) accumulation in karst areas is a significant challenge in preventing and controlling soil contamination by HMs. A total of 1043 topsoil (0–20 cm) samples were collected from northwestern Guizhou to determine the concentrations of HMs (Cd, Pb, Zn, Cr, Cu, and Ni). Then, the optimal parameter based geographic detector (OPGD) model was used to identify the driving factors and interactions of natural variables (such as strata, soil organic matter, terrain, etc.) and human activity variables (such as distance from mining sites (DFM), distance from road (DFR), population density (DOP), etc.) on the spatial accumulation of soil HMs in the region. The findings revealed that the average concentrations of all HMs exceeded the corresponding background values of soil in Guizhou Province. Cd had the highest accumulation, followed by Cu, Ni, and Cr. Moran's I and inverse distance interpolation results showed clear clustering trends in the spatial distribution of HMs. The high-high clusters of Cd, Pb, and Zn were concentrated in the northern and southern parts of the study area, while the high-high clusters of Ni, Cr, and Cu were distributed mainly in the eastern and western parts of the study area. OPGD analysis results indicated that soil Cd, Pb, and Zn accumulation was influenced primarily by SOM, DFM, and stratigraphic distribution, followed by pH and soil type. Whereas Ni, Cr, and Cu were mainly affected by stratigraphic distribution. Additionally, DOP, terrain, and soil type were secondary factors affecting the accumulation of these three types of HMs. Notably, the interactive effects among these factors were found to have a more significant impact on HMs accumulation than individual factors alone. Overall, this study provides valuable insights into the main factors influencing the spatial distribution of HMs and their interactive relationships, contributing to the theoretical basis for preventing and controlling HMs pollution in the study area.

Regional coordinated development of low-carbon sports industry in China

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial–temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.

Mapping and assessing marine ecosystem services supply in the Baltic Sea

Coastal and marine ecosystems supply multiple Ecosystem Services (ES). Nevertheless, these ecosystems are among the most impacted by human activities, harming the ES sustainable supply. Since ES are a spatial phenomenon, mapping can contribute to understand ES supply. For this, we use quantitative spatio-temporal frameworks to map and assess the supply of one provisioning (food from fisheries) and two regulating ES (nursery habitats and nutrient regulation), considering two periods: Baltic Sea Holistic Assessment (HOLAS) 2 (2011–2016) and 3 (2016–2021). The ES supply was assessed following a process-based modelling approach, using bio-physical indicators as proxies. The three ES models were applied and validated, showing moderate results. For fisheries and nursery ES the results showed a significantly higher supply in HOLAS 3 than in 2, and for nutrient ES the opposite. This indicates that the assessed ES changed due to environmental activities. The Anselin Local Moran's results showed that most ES index values aggregate in the High-High cluster; Moran's I and semi-variogram results showed a clustered pattern; and the Getis Ord* analysis showed that hot and cold spots corresponded to high and low supply areas. For fisheries, high ES supply areas were located in the central-southern part of the Baltic Sea, while low-supply regions were located in the northern part. For nursery ES, high supply areas were located in the southwestern Finnish and western Estonian coasts. For nutrient ES, high supply areas occurred in the central- and eastern-southern parts close to the coast. Correlations showed a statistically significant negative correlation between fisheries and nursery ES and a significant positive correlation between fisheries and nutrient ES. No statistically significant correlations were observed between nursery and nutrient ES supply. The results obtained are essential to support coastal and marine management and planning in the Baltic Sea as well as international environmental policies and directives.

Spatial differentiation characteristics of the Hemiptera insects in China.

The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high-high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio-economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision-making basis for biodiversity conservation and efficient pest control.