High-high Clusters Research Articles

The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

Tourism sustainability is a significant approach to forming a synergistic model of industry and ecology in ecologically vulnerable areas. Scientifically detecting the effect mechanism of tourism development on eco-environment resilience is important in achieving regional social-ecological system sustainability. In this work, empirical exploration is conducted on the tourism development index (TDI) and eco-environment resilience index (ERI) in the Yangtze River Economic Belt (YREB) to study the spatio-temporal heterogeneity of TDI’s effect on the ERI. The results indicate significant growth in the TDI in the YREB, with the formation of tourist clusters around Shanghai and Chongqing as the core. Although the ERI typically exhibits a declining trend, the rate of decline has notably slowed, forming a “high at the sides and low in the middle” spatial pattern. The TDI and ERI are spatially dependent in the YREB, with predominantly high-high (HH) and low-high (LH) clusters in Shanghai, Zhejiang, and Jiangsu. Conversely, upstream regions with strong eco-environmental foundations exhibit low-low (LL) and high-low (HL) clusters. In general, the TDI promotes the ERI, but there is significant spatio-temporal heterogeneity in the YREB. Positive impact regions are expanding, while negative impact regions are shrinking. These results could provide scientific evidence for differentiated classification and control policies in the YREB.

Identification Of Local Clusters Of Hand Foot And Mouth Disease Based On Local Geary C Statistic

Background: Hand, foot, and mouth disease (HFMD) is a common childhood illness caused by enteroviruses. Increasingly, the disease has a substantial burden throughout Vietnam. To better understand of the epidemiology of HFMD, this study aims to investigate the use of local Geary’s C statistic in the identification of local clusters of HFMD in Ho Chi Minh city, Vietnam. Methods: Histogram was first used to study the distribution of HFMD cases the incidence of HFMD. Local Geary C statistics was then applied to identify the spatial clusters of HFMD cases and HFMD incidence including high-high, low-low, negative and other positive clusters. Finally, HFMD cases and infection rates collected in Ho Chi Minh were used to explore the spatial clusters of HFMD. Results: It was found that, in the case of using HFMD cases, two high-high clusters in District 12 and Binh Tan in the city center, one low-low cluster in District 5 in the east of the city center and two negative clusters in Binh Thanh and District 7 in the west of the city were successfully detected. On the other hand, in the case of using HFMD infection rates, a total of three high-high clusters, one low-low cluster, one negative and one other positive clusters were successfully detected, in which, the three high-high clusters were found in the districts of Binh Tan (289 cases/100,000 inhabitants), Binh Chanh (283 cases/100,000 inhabitants) and Tan Phu (281 cases/100,000 inhabitants) in the east of the city. Conclusions: findings in this study confirm the usefulness of local Geary’s C statistic in the identification of spatial clusters of HFMD.

The allometric growth relationship between electricity consumption and economics in China.

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy.

Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism.

It is an important way for mining the characteristics of regional carbon emission efficiency and exploring regional similarity to learn from intergovernmental learning mechanisms and regional industrial low-carbon development experiences. This study proposes a regional learning mechanism of industrial carbon emission efficiency (ICEE) prediction and regional similarity analysis to explore strategies for carbon emission reduction. We first calculated the industrial carbon emission efficiency of 30 provinces in China from 2000 to 2021 using the super-SBM model. Secondly, the spatiotemporal characteristics of industrial carbon emission efficiency were explored through the space-time cube model, time series clustering method, and local outlier analysis. Finally, the screening of regions with low efficiency levels and the search for learning objects were realized by forest regression prediction and regional similarity calculation. The results of the study were as follows: (1) There were significant differences in industrial carbon emission efficiency among different provinces. (2) Based on the time series clustering results, we found that there were similar change characteristics of industrial carbon emission efficiency in different provinces. (3) The industrial carbon emission efficiency of most provinces had significant correlation in space and time, mainly in high-high clustering. (4) The industrial carbon emission efficiency of most regions will maintain a high efficiency level in the next 10 years, but the six provinces of Xinjiang, Qinghai, Gansu, Ningxia, Liaoning, and Heilongjiang will always be at a low efficiency level. It is possible to set appropriate learning targets for each region and to find lessons to be learned from the regions with high similarity by calculating the similarity between each province and the six provinces.

Conservation Effectiveness Assessment of the Three Northern Protection Forest Project Area

The Three-North Shelterbelt Project is the largest ecological engineering initiative in China to date, distinguished by its immense scale, extended construction period, and widespread benefits for the population. The gross ecosystem product (GEP) serves as a crucial indicator for assessing ecological benefits. This study focuses on the Three Northern Protection Forest Project Area, utilizing GEP calculations for the years 2000 to 2020. This study evaluates variations in the production values of different ecosystem services to reflect the ecological conservation benefits of the restoration project. Additionally, it analyzes the spatiotemporal evolution and trends of the GEP calculations, offering data references and decision support for the enduring efficacy of ecological restoration projects. The findings are as follows. (i) Between 2000 and 2020, the GEP of the Three-North region exhibited significant growth with continuous enhancement of various ecosystem service functions; the most substantial rate of change was observed in the water conservation function, followed by carbon sequestration and oxygen release, soil retention, windbreak and sand fixation, flood regulation, and environmental purification functions. (ii) The per-unit area value of different ecosystem types generally increased; the forest ecosystem displayed the largest growth rate at 61.18%, followed by shrubland ecosystems at 49.84%. (iii) The spatial distribution of ecosystem service in the Three-North region displayed a clustering trend alongside notable spatial heterogeneity. High-high clustering zones were identified in areas such as the Tianshan Mountains, Altai Mountains, Qilian Mountains, and Greater and Lesser Khingan Mountains. Conversely, low-low clustering areas were scattered, forming patchy distributions in regions like the Tarim Basin, northern Qinghai-Tibet Plateau, and the Hexi Corridor. This study, by analyzing the gross ecosystem product of the Three-North Shelterbelt Project region, unveils the spatial distribution characteristics, trends, and variations in ecosystem service values over the past two decades. It provides data support and decision guidance for the long-term efficacy of future ecological conservation and restoration projects. This study incorporates the GEP accounting method into the assessment of the effectiveness of major conservation projects. Compared to the traditional methods of effectiveness assessment, this represents a significant exploration and innovation.

Spatio-temporal evolution and the driving factors of municipal solid waste in Chinese different geographical regions between 2002 and 2020

Urbanization and economic development have contributed to the rapid and massive generation of municipal solid waste (MSW) and significant changes in spatial patterns, which are becoming a serious pollution problem. Previously, macroscopic studies on the driving factors of MSW have been widely conducted at the national level, but the exploration of the driving factors in different geographical regions on a regional scale has not received much attention in the previous literature. This study is based on China, spatial patterns were analyzed using spatial autocorrelation and movement of center of gravity, and time series clustering was used to explore temporal trends. Subsequently, Geodector was adopted to quantify the relationship between MSW generation and driving factors. The results of the study are as follows: 1) By analyzing the spatial pattern of MSW, this study found that MSW showed a spatial pattern of high in the southeast and low in the northwest during 2002–2020, and its separating line was the same as the Hu-line; the average center of gravity of MSW generation in the past 20 years was always located in Henan Province and shifted southward by 339.7 km. 2) The local spatial autocorrelation analysis results showed that the Low-Low clusters moved from southeast to northwest from 2002 to 2020, increasing to 20 cities. High-High clusters mainly appeared in the East Coast and South Coast regions, increasing from 8 to 17 cities in the last 20 years. 3) The analysis of driving factors by Geodetector revealed that Urbanization is the most critical dimension factor influencing MSW generation, with the strongest impact on the East Coast region. The next dimension is Economy, which has the most significant impact on MSW generation in the North West region. Energy is the third dimension that influences MSW generation, with the greatest impact on the North Coast region. The results of this study reveal trends in the spatial and temporal distribution of MSW in different geographic regions of China over the past 20 years and the impact of their driving factors, which can help the Chinese government take action to control MSW in a site-specific manner.

Spatial and temporal evolution of urban carbon emission efficiency in China.

Improving carbon emission efficiency is crucial for achieving carbon peak and carbon neutrality. This paper analyzes the carbon emission efficiency of Chinese cities using panel data from 275 prefecture-level cities between 2006 and 2020. The super-efficiency SBM (undesirable slacks-based measurement) model of undesired output, combined with kernel density estimation and exploratory data analysis, is employed. This study finds, firstly, from 2006 to 2020, the average efficiency of carbon emissions in Chinese cities declined from 0.53 to 0.48, indicating a downward trend. Secondly, from a temporal perspective, the level of dispersion in urban carbon emission efficiency has become increasingly distinct over time, coupled with a decrease in polarization. Thirdly, in terms of spatial analysis, urban carbon emission efficiency in China shows a positive correlation in a global context. In terms of local spatial correlation, it primarily exhibits high-high and low-low clusters, with a few instances of low-high and high-low clusters. Finally, based on the research findings, this paper proposes suggestions to improve urban carbon emission efficiency.

Spatial heterogeneity of factors affecting GHG emission intensity in urban water supply and wastewater treatment systems in China

The water supply and treatment in urban water systems (UWS) consume massive energy, quantitative estimate of the intensity of their accompanying GHG (Greenhouse Gas) emissions is important to clarify the major source of the GHG for UWS. Herein, on the city scale, the intensity of energy consumption and GHG emissions of the water supply and wastewater treatment systems were calculated, based on the operating data of 5972 water or wastewater treatment plants in China. Results showed that the water supply system consumed 38.6 billion kWh of electrical energy and accounted for 26.8 Mt CO2-eq GHG emissions, accordingly of 14.4 billion kWh and 21.9 Mt CO2-eq for the wastewater treatment system. For wastewater treatment, significant spatial autocorrelation was observed for the intensities of energy consumption and GHG emission, and the clustering areas highly matched with the topography, especially the N2O and CH4 emissions high-high clustering in the Loess Plateau and Inner Mongolia Plateau. Screened by multi-scale geographically weighted regression, the annual precipitation was found to be the main factor influencing the intensities, with a higher regression coefficient than social factors (e.g., GDP and population density). And a clear spatial heterogeneity was observed with the regression coefficient distributed higher in the northwest than in the southeast, with a gap of 2.3–7.5 times. This study provides systematic estimation results of energy consumption and GHG emissions intensity in urban water supply and wastewater treatment systems on the city scale in China, quantifies regional differences in energy consumption and GHG emission intensity of UWS, and revealed the inequality of UWS in regions with different basic conditions while facing low-carbon transformation. Our findings provide a reference for the formulation of regionally targeted UWS low-carbon transition policies.

An investigation of geographical clusters of leptospirosis during the outbreak in Pangandaran, West Java, Indonesia.

Leptospirosis is neglected in many tropical developing countries, including Indonesia. Our research on this zoonotic disease aimed to investigate epidemiological features and spatial clustering of recent leptospirosis outbreaks in Pangandaran, West Java. The study analysed data on leptospirosis notifications between September 2022 and May 2023. Global Moran I and local indicator for spatial association (LISA) were applied. Comparative analysis was performed to characterise the identified hotspots of leptospirosis relative to its neighbourhoods. A total of 172 reported leptospirosis in 40 villages from 9 sub-districts in Pangandaran District were analysed. Of these, 132 cases (76.7%) were male. The median age was 49 years (interquartile range [IQR]: 34-59 years). Severe outcomes including renal failure, lung failure, and hepatic necrosis were reported in up to 5% of the cases. A total of 30 patients died, resulting in the case fatality rate (CFR) of 17.4%. Moran's I analysis showed significant spatial autocorrelation (I=0.293; p=0.002) and LISA results identified 7 High-High clusters (hotspots) in the Southwest, with the total population at risk at 26,184 people. The hotspots had more cases among older individuals (median age: 51, IQR: 36-61 years; p<0.001), more farmers (79%, p=0.001) and more evidence of the presence of rats (p=0.02). A comprehensive One Health intervention should be targeted towards these high-risk areas to control the transmission of leptospirosis. More empirical evidence is needed to understand the role of climate, animals and sociodemographic characteristics on the transmission of leptospirosis in the area studied.

Evaluation of water-carbon-ecological footprints and its spatial–temporal pattern in the central plains urban agglomeration

Maintaining the stability of natural capital stock is an important basis for realizing regional sustainable development goals. Based on the improved three-dimensional (3D) footprint family model, this study analyzes the spatial and temporal evolution of the natural capital utilization of water-carbon-ecological footprints in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2020. It considers water resources, carbon emissions and ecological environment comprehensively, and establishes an evaluation system to assess the regional resource and environmental pressure. The results showed that: (1) During the period of 2010–2020, each component of the per capita water-carbon-ecological footprint of the CPUA were in deficit. Footprint depths were all greater than 1 and in a state of depleting capital stock. The water footprint depth (WFdepth) showed a large fluctuation between 1.721 and 4.302, the carbon footprint depth (CFdepth) increased from 6.025 to 7.440, and the ecological footprint depth (EFdepth) fluctuated between 4.801 and 5.779. (2) There were spatial differences in the depth of each type of footprint, with the WFdepth showing a south-high and north-low distribution, the CFdepth showing a northwest-high and southeast-low distribution, and the EFdepth showing an east-high and west-low distribution. (3) Exploratory spatial data analysis (ESDA) showed that there were spatial correlations between the various types of footprint size and footprint depth in the CPUA, and that cities showed local spatial characteristics of High-High cluster and Low-Low cluster. (4) All types of resource and environmental pressure measured were at high levels (>0.8), and the resource-environment pressure index (REPI) in the northwest showed a gradual increasing trend.

Spatial Patterns Of Hand-Foot And Mouth Disease In Ho Chi Minh City, Vietnam

Background: Hand-Foot-and-Mouth Disease (HFMD) is most frequently caused by Enterovirus71 (EV-A71) or Coxsackie virus A16 (CV-A16), infants and young children are at greatest risk. Describing the spatial patterns of HFMD can help develop and better target interventions. The objective of this study is to identify spatial patterns of HFMD in the first 8-months of 2023 in Ho Chi Minh city, Vietnam. Methods: The global Moran’s I statistic, Moran’s I scatterplot and local Moran’s I statistic and Boxplot will be applied to study spatial patterns of HFMD. Spatial patterns including spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low) will be identified for HFMD cases and HFMD infection rates. Results: three high-high spatial clusters were mainly distributed in districts in the western region of the city such as Binh Tan, Binh Chanh, and Tan Phu. These high-high spatial clusters belonged to districts having the highest rates of HFMD infections in the city with their corresponding rates of 289, 283 and 281 cases/100,000 inhabitants, respectively. On the other hand, two low-low spatial clusters were detected in Districts 1 and 5 in the city center with their HFMD infection rates of 190 and 209 cases/100,000 inhabitants, respectively. Conclusions : findings in this study support the usefulness of boxplots, local and global Moran’s I statistics, and Moran’s I scatterplots in the identification of spatial clusters and spatial outliers of HFMD.

Spatial distribution of Oncomelania hupensis spread in Hubei Province from 2020 to 2022

To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province. Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest-neighbor analysis and Spearman correlation analysis. O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm2, including 1 230.77 hm2 emerging snail habitats and 3 089.87 hm2 reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran's I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran's I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high-high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high-high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong'an. Kernel density estimation showed that there were high-, secondary high- and medium-density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high- and medium-density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = -0.108 9, P < 0.05). There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.

Spatial Clustering Analysis Of Dengue Hemorrhagic Fever In The First 9-Monthsof 2023 In Ho Chi Minh City, Vietnam

Background: Dengue hemorrhagic fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. The objective of this study is to investigate spatial clustering of dengue hemorrhagic fever incidence in the first 9-months of 2023 in Ho Chi Minh city, Vietnam. Methods: the global Moran’s I statistic, Moran’s I scatterplot and local statistic were employed to spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low) in the study area of Ho Chi Minh city. The first and third order of contiguity were used to constructe spatial weight matrix. Results: it was found from a case study of the first 9-months of 2023 in Ho Chi Minh city, a total of four high-high clusters, two low-low spatial clusters were detected in urban area and rural areas in the north and south of the Ho Chi Minh city, respectively when using the first order contiguity (statistically significance at the 0.05 level). For the case of using the third order of contiguity, a total of six high-low, two low-high spatial clusters and one low-low spatial cluster were successfully identified. Conclusions: the study results has proven the effective use of the global Moran’s I statistic, Moran’s I scatterplot and local Moran’s I statistic in the identification of spatial clustering of dengue hemorrhagic fever incidence.

Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

The rapid pace of urbanization has resulted in pressing climatic challenges, notably an escalation in carbon emissions and rising urban temperatures. Urban expansion frequently coincides with heightened carbon emissions, releasing substantial quantities of greenhouse gases such as CO2. This exacerbates the urban heat island (UHI) effect and poses a threat to urban living conditions. To shed light on the intricate relationship between land surface temperature (LST) and carbon emissions and provide novel insights into the synergistic regulation of “heat” and “carbon” in sustainable urban development, we conducted an in-depth investigation of the Wuhan Urban Agglomeration spanning from 2003 to 2018. Employing data on impervious surface fraction (ISF), LST, and carbon emissions, we harnessed weighted center and standard deviation ellipse analyses to explore the spatiotemporal expansion patterns of ISF, LST, and carbon emissions. We examined the relationship between LST and carbon emissions using spatial correlation analysis. The results revealed that regions with high carbon emissions and elevated LST were predominantly concentrated in the central urban areas of each city, aligned with the distribution of high-density impervious surfaces. The weighted centers of ISF, LST, and carbon emissions exhibited a progressive shift toward the central region of Wuhan, intensifying spatial clustering effects. The global Moran's I index for LST and carbon emissions increased from 0.33 in 2003 to 0.48 in 2018, signifying a strong positive correlation between LST and carbon emissions. In the clustering maps of LST and carbon emissions, the high-high clustering trend persistently expanded, signifying a shift from “dispersion” to “agglomeration” in urban heat and carbon effects. In regions marked by consistent carbon emission growth, the correlation coefficient (R) between carbon emissions and LST was 0.4, and the Pearson coefficient was 0.64, both higher than those in areas with gradual carbon emission growth. Within stable carbon emission growth areas, every 0.1-ton increase in carbon emissions has a 40% probability of a 1.33 °C temperature rise. As the rate of carbon emissions increased, the positive synergistic effect between carbon emissions and LST continued to increase. This study provides empirical support and academic groundwork to reduce urban emissions and mitigate the effects of UHIs.

Spatial association between socio-economic health service factors and sepsis mortality in Thailand.

Sepsis is a significant global health issue causing organ failure and high mortality. The number of sepsis cases has recently increased in Thailand making it crucial to comprehend the factors behind these infections. This study focuses on exploring the spatial autocorrelation between socio-economic factors and health service factors on the one hand and sepsis mortality on the other. We applied global Moran's I, local indicators of spatial association (LISA) and spatial regression to examine the relationship between these variables. Based on univariate Moran's I scatter plots, sepsis mortality in all 77 provinces in Thailand were shown to exhibit a positive spatial autocorrelation that reached a significant value (0.311). The hotspots/ high-high (HH) clusters of sepsis mortality were mostly located in the central region of the country, while the coldspots/low-low (LL) clusters were observed in the north-eastern region. Bivariate Moran's I indicated a spatial autocorrelation between various factors and sepsis mortality, while the LISA analysis revealed 7 HH clusters and 5 LL clusters associated with population density. Additionally, there were 6 HH and 4 LL clusters in areas with the lowest average temperature, 4 HH and 2 LL clusters in areas with the highest average temperature, 8 HH and 5 LL clusters associated with night-time light and 6 HH and 5 LL clusters associated with pharmacy density. The spatial regression models conducted in this study determined that the spatial error model (SEM) provided the best fit, while the parameter estimation results revealed that several factors, including population density, average lowest and highest temperature, night-time light and pharmacy density, were positively correlated with sepsis mortality. The coefficient of determination (R2) indicated that the SEM model explained 56.4% of the variation in sepsis mortality. Furthermore, based on the Akaike Information Index (AIC), the SEM model slightly outperformed the spatial lag model (SLM) with an AIC value of 518.1 compared to 520.

Spatial pattern and influencing factors of land border cultural heritage in China

This paper provides an analysis of China’s land border cultural heritage, aiming to offer guidance for its protection and development. The study compiled a comprehensive database of heritage sites and analyzed their mathematical characteristics based on quantity, type, and quality. The spatial pattern of the cultural heritage was described using kernel density analysis, spatial variability, and spatial correlation, while the driving mechanism was measured based on Geo-Detector model. The findings revealed that the land border areas of China have a total of 1227 cultural heritage, comprising two main types and 18 subtypes. National cultural heritage accounted for 22.74%, with ancient city ruins being the largest type of cultural heritage. The Southwest and Northeast regions of China exhibited high-high clusters and low-low clusters, respectively. Moreover, the study identified history of administrative succession, evolution of historical and cultural, altitude, distance from the city, population density, minority population ratio, urbanization process, and per capita GDP as significant factors impacting the spatial pattern of land border cultural heritage in China. Based on these findings, the study suggests that the government should invest more in border cultural heritage, improve transportation infrastructure in border areas, ensure dynamic inheritance and protection of cultural heritage, and promote the joint development of different types of cultural heritage. The research results will provide data support and decision-making reference for the sustainable development of cultural heritage in land border areas, and can provide a new perspective for the management of cultural heritage in similar areas.

Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery.

Precise identification and spatial analysis of land salinity in China's Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran's I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6-8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high-high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low-low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies.

Spatial clustering analysis of COVID-19 using LISA: A case study of the 2022 winter Day in Hanoi, Vietnam

Background:A novel coronavirus disease outbreak in 2019 (COVID-19) caused by he emergence of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) in China quickly spreads throughout the world. This study aimed to analyze the spatial clustering of COVID-19. Methods:The global and local Moran's I statistic (LISA) was used to investigate the spatial clusters of COVID-19 including spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low). Results: A case study of COVID-19 locally transmitted cases reported in a 2022 winter day in Hanoi city has indicated that high-high spatial clusters were totally concentrated in 6 urban districts in the Hanoi metropolitan including such as districts of Dong Da, Gia Lam, Thanh Tri, Hai Ba Trung, Cau Giay, and Long Bien. Whereas, low-low spatial clusters were mainly in sub-urban districts such as Ba Vi, Thach That, Phuc Tho, and Son Tay town (0 cases) in the northwest and Ung Hoa district in the south of Hanoi. Conclusions:The study results indicated the effectiveness of LISA in analysis of spatial clustering of COVID-19. Findings in this study make great contributions to the fight of the COVID-19 pandemic.

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015–2020 in Shaanxi, China

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

Collaborative governance on SO2 pollution from thermal power energy consumption: Evidence from Yangtze River Delta urban agglomeration in China

In order to explore an effective method for the effect of collaborative governance on SO2 pollution from thermal power energy consumption in the Yangtze River Delta (YRD) urban agglomeration, the spatiotemporal evolution of SO2 pollution from thermal power energy consumption was empirically studied by Moran's I first; then the revised Difference-in-Difference-in-Differences model was constructed to examine the effect of collaborative governance on SO2 emission intensity from thermal power energy consumption, and the time effect of collaborative governance was also examined; finally, the Grey System Prediction Model was used to forecast the SO2 emission intensity from thermal power energy consumption in YRD urban agglomeration from 2021 to 2025. The results show that SO2 emissions from thermal power energy consumption in YRD urban agglomeration have positive spatial autocorrelations, and high-high spatial clusters have the tendency of moving to inland cities from coastal cities. Moreover, SO2 pollution from thermal power energy consumption in YRD urban agglomeration has improved year-by-year after the implementation of collaborative governance and the predicted SO2 emission intensity from thermal power energy consumption in YRD urban agglomeration generally shows a downward trend.