Kernel Density Estimation Analysis Research Articles

Exploring the strategies for upgrading public facilities in urban villages: A case study of Wuhan, China

Public facilities are essential components of urban villages in large cities in China, playing a key role in the quality of life for residents. However, our understanding of these public facilities is limited. There is an urgent need to upgrade them in the context of a new wave of urban redevelopment in China. To address this gap, we first identify Wuhan’s urban villages by correlating the proportion of construction land with the percentage of migrants. Next, we employ kernel density estimation and fishnet analysis methods to examine the characteristics of public facilities in urban villages. We then develop a new research framework, characterized by “upgrade sequence – upgrade mode”, for strategically arranging the upgrading of these facilities. Our findings indicate that: (1) the upgrade of public facilities in Wuhan’s urban villages should focus on the suburban districts; (2) it is essential to balance supply and demand when addressing issues related to public facilities, avoiding indiscriminate large-scale demolition and reconstruction, as in-situ upgrades may become the new trend; and (3) different upgrade approaches should be adopted based on the specific features of each urban village. Overall, this study fills a gap in the systematic examination of public facilities in urban villages and proposes a new strategy for in-situ upgrades. We contribute to a nuanced understanding of the dynamics of urban villages in China and the broader context of planetary urban redevelopment.

Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Genomic and Spatial Analysis on the Recent Transmission of Mycobacterium tuberculosis in Eastern China: A 10-Year Retrospective Population-Based Study.

Understanding the mode of Mycobacterium tuberculosis (M. tuberculosis) transmission is crucial for disease prevention and control. Compared to traditional genotyping methods, whole genome sequencing (WGS) provides higher resolution and comprehensive genetic information, enabling the tracing of infection sources and determining of transmission routes to resolve extensive tuberculosis (TB) outbreaks. We conducted a ten-year study on the transmission of M. tuberculosis in a population in eastern China. We selected Lianyungang, an eastern city in China, as the study site. Patients diagnosed with active pulmonary TB from 2011 to 2020 were enrolled as the study subjects. We isolated and sequenced 2252 M. tuberculosis. Strains with pairwise genetic distances of less than 12 single nucleotide polymorphisms were defined as genomic clusters and which were considered recent transmissions. Kernel density estimation and K-function analysis were applied to explore the spatial distribution of recently transmitted strains. After excluding non-tuberculous mycobacteria and duplicated samples, 2114 strains were included in the final analysis. These strains comprised lineage 2 (1593, 75.35%) and 4 (521, 24.65%). There were 672 clustered strains, with a recent transmission rate of 31.79%. The logistic regression model showed that the risk of recent transmission was high in students [adjusted odds ratio (aOR): 2.68, 95% confidence interval (CI): 1.63-4.49, P<0.001] and people infected with L2.2.1 strains (aOR: 1.59, 95% CI: 1.20-2.12). Higher spatial aggregation of TB transmission has been concentrated in Haizhou, Donghai, and Guanyun for the past 10 years. Three outbreaks affecting 46 patients were spatially spaced, with 11 to 23 persons each. Different groups exhibited varying geographic distances between the initial and later cases. There are areas with a high risk of transmission for M. tuberculosis in the research site, and the risk varies among different populations. Accurate prevention strategies targeted at specific regions and key populations can help curb the prevalence of TB.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China

BackgroundThe built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).MethodsTaking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.ResultsThe results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran’s I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.ConclusionsThe spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

Effects of foliation angle on mechanical characteristics of carbonaceous slate using uniaxial compression tests

Carbonaceous slate is one kind of metamorphic rocks with developed foliation, which is frequently encountered during tunnel construction in Western China. The foliation plays a crucial role in the stability of tunnels. For this, we conducted uniaxial compression tests, acoustic emission (AE) monitoring and scanning electron microscope (SEM) tests on carbonaceous slate. The results show that the strength, failure mode, and AE characteristics exhibit marked anisotropy with the angle between the axial and the foliation (β). As β increases, the ultrasonic wave velocity decreases monotonically, whereas the uniaxial compressive strength (UCS) displays a distinctive U-shaped trend. The elastic modulus initially decreases and then increases. The cumulative AE counts curve and energy curve show a stepped growth when β ≤ 45°. The AE events are active during the crack compaction phase and remain calm during the linear elastic deformation phase when β > 45°. Upon failure, the energy release accounts for the highest proportion (67%) when β = 45°, while the proportions in other cases are less than 37%. The maximum percentage (31%) of shear cracks is reported when β = 60°, which is six times greater than that at β = 0°. Moreover, Kernel density estimation analysis reveals that the high concentration area with low AF (AE counts / duration time) and high RA (rise time / amplitude) increases initially, and then decreases when β > 60°. In addition, nine types of cracks and seven modes of failure were identified. The foliation angle has a pronounced impact on shear failure modes in comparison with tensile failure modes. The supports could suffer larger deformation when β ≥ 60° compared to other cases. The failure behaviors correspond well with field observations.

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

Spatial–temporal evaluation of total-factor energy efficiency in Chinese construction industry based on three-stage super-efficiency SBM-DEA model

PurposeThe study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.Design/methodology/approachUtilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.FindingsAnalysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&amp;D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.Originality/valueThis research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Home range, habitat use and capture-release of translocated leopards in Gir landscape, Gujarat, India.

Understanding the spatial ecology of translocated leopards (Panthera pardus fusca) is crucial for their conservation and the effective assessment of conflict management strategies. We investigated the home range and habitat preferences of five radio-collared leopards (n = 5; 2 males; 3 females) in the Gir landscape. Additionally, we examined the usefulness of the capture-release strategy for these animals. We assessed home range and habitat selection using kernel density estimation (at 95% and 50% levels) and compositional analysis. Our findings revealed that leopards exhibited distinct patterns of movement, often returning to their original capture site or nearby locations or exploring new areas within 3 to 25 days, covering distances ranging from 48 to 260 km. The average home range (95% FK) was estimated at 103.96±36.37 (SE) km2, with a core area usage (50% FK) of 21.38±5.95 km2. Seasonally, we observed the largest home ranges during summer and the smallest during winter. Males exhibited larger home ranges (95% FK, 151±64.28 km2) compared to females (56.18±14.22 km2). The habitat analysis indicated that agricultural areas were consistently preferred in the multi-use landscape at the 2nd order habitat selection level. Additionally, habitat around water bodies was highly favoured at the 3rd order, with distinct variations in habitat selection observed during day and night. This study highlights the significance of riverine and scrubland habitats, as leopards exhibited strong preferences for these habitats within their home ranges. We emphasize the importance of conserving natural habitat patches, particularly those surrounding water bodies. We also report on the characteristics of the capture-release strategy and provide our observations indicating no escalated aggression by leopards' post-release. In conclusion, this study evaluates widely employed approaches to conflict mitigation and suggests the continuous review and assessment of management strategies for mitigating human-leopard conflicts.

Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China

Cultivated land plays a crucial role as the basis of grain production, and it is essential to effectively manage the unregulated expansion of non-grain production (NGP) on cultivated land in order to safeguard food security. The study of NGP has garnered significant attention from scholars, but the prediction of NGP trends is relatively uncommon. Therefore, we focused on Jiangsu Province, a significant grain production region in China, as the study area. We extracted data on cultivated land for non-grain production (NGPCL) in 2000, 2005, 2010, 2015, and 2019, and calculated the ratio of non-grain production (NGPR) for each county unit in the province. On this basis, Kernel Density Estimation (KDE) and spatial autocorrelation analysis tools were utilized to uncover the spatio-temporal evolution of NGP in Jiangsu Province. Finally, the Patch-Generating Land Use Simulation (PLUS) model was utilized to predict the trend of NGP in Jiangsu Province in 2038 under the three development scenarios of natural development (NDS), cultivated land protection (CPS), and food security (FSS). After analyzing the results, we came to the following conclusions:(1) During the period of 2000–2019, the NGPCL area and NGPR in Jiangsu Province exhibited a general decreasing trend. (2) The level of NGP displayed a spatial distribution pattern of being “higher in the south and central and lower in the north”. (3) The results of multi-scenario simulation show that under the NDS, the area of NGPCL and cultivated land for grain production (GPCL) decreases significantly; under the CPS, the decrease in NGPCL and GPCL is smaller than that of the NDS. Under the FSS, NGPCL decreases, while GPCL increases. These results can provide reference for the implementation of land use planning, the delineation of the cultivated land protection bottom line, and the implementation of thee cultivated land use control system in the study area.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p &gt; 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Spatial Analysis of Internally Displaced Persons’ Camps in Borno State, Nigeria

The situation in Borno State, Nigeria has been exacerbated by the ongoing insurgency led by Boko Haram in northeast Nigeria, leading to widespread displacement and the establishment of Internally Displaced Persons (IDP) camps. This study aims to identify the locations of IDP camps in Borno State and analyze their spatial distribution, density, and clustering patterns. Furthermore, it seeks to investigate the geospatial implications of these patterns. To achieve this, the study employs geospatial analysis techniques such as Kernel Density Estimation, Cluster and Outlier Analysis, Nearest Neighbour Analysis, and Spatial Autocorrelation Analysis. The data collected for this study includes location coordinates, satellite imagery, demographic information, and administrative maps. The findings reveal that the distribution of IDP camps in Borno State is non-random and demonstrates varying camp densities across senatorial districts, with predominant high-high clusters and a deliberate, non-random arrangement. These results align with spatial analysis theories, emphasizing the importance of recognizing hidden inequalities in IDP camps and informing targeted interventions and resource planning for a more effective and equitable humanitarian response in Borno State. The implications extend to policy planning and enhanced vulnerability assessment for effective humanitarian responses, stressing the need for tailored interventions based on identified hotspots that address root causes and implement both immediate and long-term solutions. Future research directions could involve more in-depth investigations into the influencing factors, socio-economic impacts, and temporal dynamics of spatial patterns in displacement.

Decoupling government spending from carbon emissions: A spatio-temporal analysis of 283 Chinese cities

A comprehensive evaluation of government spending efficiency and its connection to emissions reduction is vital for sustainable green economic progress. However, the precise nature of the decoupling relationship between carbon emissions and government spending at the city level remains uncertain. Here, using the panel data from 283 cities covering the period from 2007 to 2020, we employ diverse methodologies, such as Kernel density estimation and spatiotemporal autocorrelation analysis, to examine the spatiotemporal distribution of decoupling and identify key contributors through LMDI decomposition analysis. We find that initially, most cities demonstrated weak decoupling, but after 2010, a subset of cities achieved strong decoupling. The spatiotemporal distribution analysis highlights an expansion of inter-city differences in decoupling values and a reduction in the number of cities forming clusters after 2015. Furthermore, an increasing number of cities experienced positive emission intensity effects, while the positive effects of government spending on labor input consistently decreased. Initially, the positive effect of government spending on economic development declined but later showed a slight increase. The distribution of cities with a positive impact of government spending scale shifted from dispersed to concentrated in northern regions, resulting in a decrease in the number of cities with a positive impact.

Fishing effort dynamics around the Galápagos Marine Reserve as depicted by AIS data.

The waters around the Galápagos Marine Reserve (GMR) are important fishing grounds for authorized artisanal vessels fishing within the reserve as well as for national and foreign industrial fleets operating in the wider Ecuadorian Insular Exclusive Economic Zone (IEEZ). Although it was not originally designed for fisheries management, Automatic Identification System (AIS) data provides useful, open access, near real-time and high-resolution information that allows for increased monitoring, particularly around Marine Protected Areas (MPAs) and in Areas Beyond National Jurisdiction. This study uses AIS data provided by Global Fishing Watch to assess the spatial distribution and seasonal dynamics of fishing effort by vessel flag within the GMR and the IEEZ from 2012 to 2021. Based on kernel density estimation analysis, we determinate the core-use areas (50%) and spatial extent (95%) of fishing activities by fleets (Ecuadorian and foreign), gear types and seasons (warm, from December to May; and cold, from June to November). Our results show that the Ecuadorian fleet recorded the most observed fishing hours in the study area, with 32,829 hours in the IEEZ and 20,816 hours within the GMR. The foreign flags with the most observed fishing hours in the IEEZ were Panama (3,245 hours) and Nicaragua (2,468.5 hours), while in the GMR were the 'Unknown flag' (4,991.4 hours) and Panama (133.7 hours). Vessels fished employing different fishing gears, but the waters of the GMR and IEEZ were mostly targeted by tuna purse-seiners and drifting longlines. The spatial distribution of the fishing effort exhibits marked seasonal variability, likely influenced by seasonal migrations of target species such as tunas (e.g., Thunnus albacares, T. obesus and Katsuwonus pelamis), marlins (e.g., Makaira nigricans) and sharks (e.g., Alopias pelagicus). The collection and use of this type of spatial and seasonal information is an essential step to understand the dynamics of fishing activities in national waters and improve fisheries management, particularly in less studied areas and fisheries.

BESOINS ET DÉFIS D’INTÉGRATION POUR UNE TRANSFORMATION VERTE ET INTELLIGENTE DE L’INDUSTRIE PORTUAIRE BASÉE SUR DES DONNÉES MULTI-SOURCES

This research explores the integration needs and challenges for achieving green and smart transformation in the port industry, leveraging multi-source data and geographic information technology. The study utilizes comprehensive data, including information from the National Enterprise Credit Information Publicity System, the National Catalogue Service for Geographic Information, and road network maps of Jiangsu Province in China. Geographical analysis methods, such as weighted kernel density estimation and standard deviation ellipse analysis, are employed to identify spatial distribution patterns and changes in the port and shipping service industry. The results indicate a spatial distribution pattern with weak expansion from the center, along the "northwest-southeast" direction. Challenges such as decentralized management and competition for interests hinder port integration for sustainable development. By understanding the spatial dynamics, policymakers can make informed decisions for a greener, smarter, and more efficient port sector.

Study on the spatiotemporal evolution of coupled and coordinated digital economic resilience and efficiency

The coordinated development of resilience and efficiency in the digital economy guarantees a sustainable and efficient growth model. We used panel data from 30 provinces and regions in China from 2013 to 2021 to evaluate the resilience and efficiency levels of the digital economy. We calculated their coordinated development levels using kernel density estimation, spatial autocorrelation, and Markov chain analysis to explore the spatiotemporal evolution characteristics of their coordinated development. The results indicate that (1) the resilience level of the digital economy is on the rise, while efficiency fluctuations are decreasing. Resilience exhibits an “east high–west low” spatial pattern with the “Hu Huanyong Line” as a boundary, while efficiency shows a “collapse in the central and northern regions” trend. (2) The overall level of coordinated development between resilience and efficiency has decreased; however, the differences in coordination between different regions have reduced, gradually forming a spatial pattern of “high in the east, low in the west, low in the south, and high in the north.” (3) A significant positive spatial correlation exists between the development levels of digital economic resilience and efficiency, which continues to strengthen over time. This finding reveals a spatial pattern of high-level concentration centered around Beijing–Tianjin, Jiangsu–Zhejiang–Shanghai, and low-level concentration centered around the central and western regions. (4) Coordinated development shows a “club convergence” effect, indicating stability in maintaining the existing state.

Study on the Spatial Arrangement of Urban Parkland under the Perspective of Equity—Taking Harbin Main City as an Example

The connotation and measurement standard of “fairness” in parkland planning is closely related to the level of urban development and residents’ needs, with solid realism and dynamics, and it is still a complex problem to control accurately in planning. Therefore, to conduct a more comprehensive study, taking the main urban area of Harbin as an example, this paper combined urban development background and practical problems at different stages and, based on POI and AOI data, divided the connotation of “equity” in park green space planning into two levels: “spatial equity” and “social equity”. Based on these two levels of fairness, the research framework of urban park green space layout is constructed. Kernel density estimation and GIS-based network analysis were used to study the spatial layout and accessibility of urban parkland. The ability to match supply and demand for parkland in the study area as a whole was analyzed using a gridded two-step moving search method, as well as the degree of equity in the enjoyment of urban parkland by vulnerable groups including the elderly and children using the Lorenz curve with the Gini coefficient, the share index, and the entropy of the locality. The results show that: (1) the parkland is concentrated in the seven main urban areas of the two–two junctions, and the cohesion distribution trend is outside the distribution. (2) Motorized vehicles have the best 15-min accessibility for travel, but it still does not cover all the parkland. (3) Supply and demand within the region are unsatisfactory, with the area with the strongest supply and demand capacity for parkland accounting for only 3.19% of the total area. (4) The Gini coefficient of parkland enjoyed by the residents of Harbin city center is 0.407, with a massive gap in the overall distribution. In addition, the share index of vulnerable groups of the elderly and children all have share indices below 1, and the results of the analysis of locational entropy indicate that the level of street park green space resources enjoyed by these two groups belongs to the low socio-spatial equity pattern, which is not balanced. This study investigates the spatial layout of urban parkland from two equity levels, which can provide new ideas for the equity study of urban green space planning.

Spatiotemporal Evolution Characteristics and Driving Factors of Water-Energy-Food-Carbon System Vulnerability: A Case Study of the Yellow River Basin, China

With the growing influences of anthropogenic activities and climatic change, the problem concerning the vulnerability of the Water-Energy-Food-Carbon (WEFC) system in the Yellow River Basin is prominent. Using the VSD framework, the WEFC system vulnerability evaluation index system was constructed with 60 cities in the Yellow River Basin as the samples, and the WEFC system vulnerability of each city was measured from 2010 to 2019. Kernel density estimation, Theil index, and spatial correlation analysis were employed to investigate spatio-temporal evolution characteristics. Geodetector was utilized to analyze the driving factors of WEFC system vulnerability. The results demonstrate that: (1) The vulnerability of the WEFC system in the Yellow River Basin tends to decrease, with a spatial pattern of “low in the middle and high on both sides”; the vulnerability is largest in the upper and lower reaches, while smallest in the middle reaches. (2) The spatial difference in vulnerability narrows in the middle and lower reaches, while expanding in the upper reaches. The differences among the three major regions mainly originate from within the region, with the upper reaches having the largest difference and contribution; the vulnerability is featured with a significant spatial correlation, with the upper and lower reaches cities mostly displaying a “high-high” agglomeration and the middle reaches mainly showing a “low-low” one. (3) Factors, including the carbon and ecological carrying capacity coefficient, water resource development and utilization rate, and urbanization rate, mainly influence the WEFC system vulnerability; the spatial heterogeneity of core drivers at the regional scale is obvious, with the upper reaches regions being more strongly influenced by factors of the water resources system, while the middle and lower reaches regions are more sensitive to factors concerning industrial pollution of the energy subsystem. The explanatory power of carbon ecological carrying capacity reaches its peak in the middle reaches. The interaction of factors increases the strength of the impact on vulnerability. This study provides decision support and policy suggestions for achieving a balanced and coordinated development of water resource utilization, energy development, food production, and carbon cycle system in the Yellow River Basin. Investigating WEFC system vulnerability to support SDG 11 provided valuable insights and policy strategies for building cities that are inclusive, secure, resource-efficient, and resilient in the face of climate change and disaster risks.

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning

The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10−3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.

Measurement and characteristics of the temporal-spatial evolution of China’s healthcare services efficiency

BackgroundHealthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country’s coordinated economic and social development.MethodsIn this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010–2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE.ResultsThe study found that China’s HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an “East > Central > West” distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the “club convergence” phenomenon of “high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression” was formed in the spatial distribution.ConclusionsThe results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

Spatial accessibility dynamics of urban parks in Nanjing, China: implications for spatial equity

PurposeThis study aimed to explore the spatial accessibility dynamics of urban parks and their driving forces from 1901 to 2010 in terms of the dynamic relationships between spatial morphology and road networks, taking Nanjing City as an example.Design/methodology/approachThis study mapped and examined the spatiotemporal distribution of urban parks and road networks in four time points at Nanjing: the 1910s, 1930s, 1960s and 2010s, using the analysis methodology of spatial design network analysis, kernel density estimation and buffer analysis. Two approaches of spatial overlaying and data analysis were adopted to investigate the accessibility dynamics. The spatial overlaying compared the parks' layout and the road networks' core, subcore and noncore accessible areas; the data analysis clarified the average data on the city-wide and local scales of the road networks within the park buffer zone.FindingsThe analysis of the changing relationships between urban parks and the spatial morphology of road networks showed that the accessibility of urban parks has generally improved. This was influenced by six main factors: planning implementation, political policies, natural resources, historical heritage and cultural and economic levels.Social implicationsThe results provide a reference for achieving spatial equity, improving urban park accessibility and supporting sustainable urban park planning.Originality/valueAn increasing number of studies have explored the spatial accessibility of urban parks through the relationships between their spatial distribution and road networks. However, few studies have investigated the dynamic changes in accessibility over time. Discussing parks' accessibility over relatively long-time scales has practical, innovative and theoretical values; because it can reveal correlational laws and internal influences not apparent in short term and provide reference and implications for parks' spatial equity.