Standard Deviational Ellipse Research Articles

Investigating the temporal and spatial evolution of bank performance and its impact pathways in the context of financial technology development

Financial technology transforms humans and businesses as globalization and the digital economy accelerate. This affects bank performance. Thus, studying whether financial technology affects bank performance is crucial to enhancing living standards and business development. This article examines the development and interaction between financial technology and bank performance from 2012 to 2021 using standard deviation ellipses, kernel density estimation, Moran's index, and spatial econometric models. The research found that (1) financial technology development improves regional bank performance. In contrast, control variables like economic development, urbanization, tax burden, capital adequacy ratio, net interest margin, and loan-to-deposit ratio also affect bank performance. (2) From 2012 to 2021, Chinese bank performance initially grew, then declined, while financial technology declined slowly and improved rapidly. Financial technology and bank performance development were highest in the eastern coastal regions and lowest in the northwest and northeast. (3) China's financial technology and bank performance had high-high or low-low spatial agglomeration. (4) Financial technology and control variables have a spatial spillover effect on bank performance, so their development in one region can affect neighboring regions. This article provides recommendations for governments and banks based on these findings.

Spatio-temporal evolution and influencing factors of high quality economic development: Case study of Guangdong-Hong Kong-Macao Greater Bay Area

With the increase in global economic integration, high-quality economic development (HQED) has become a common goal of all countries. Based on these five development concepts, this paper uses the Gini coefficient, trend surface analysis, geographically weighted regression (GWR), the entropy weighting method, and standard deviation ellipse analysis to study the spatio-temporal pattern and driving mechanism of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This paper examines the spatial and temporal patterns and driving mechanisms of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) based on five development concepts. The study revealed that (1) HQED is on the rise overall, but at the same time, it highlights the uneven development of multiple dimensions, especially in terms of significant differences in innovation, openness, and sharing. (2) HQED shows a clear center-periphery structure, with Guangzhou, Shenzhen, and Hong Kong as the core high-value areas; the growth rate of HQED capacity in the internal areas is significantly greater than that in the external areas, and HQED is prominent in the cities around Guangzhou, Shenzhen, and Hong Kong. (3) Factors such as agglomeration level, human capital, foreign investment, infrastructure development, financial and environmental protection expenditures, and financial inputs, and scientific, and technological inputs have a significant positive effect on HQED, and their interactions are further strengthened. This study reveals the importance of the realization of HQED in the GBA and the promotion of the overall development of the region.

Analysis of Spatial and Temporal Changes and Drivers of Urban Sprawl in Xinjiang Based on Integrated DMSP-OLS and NPP-VIIRS Data

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Analysis of the spatial mismatch pattern of net carbon in agriculture and its influencing factors

Agricultural production has dual functions as a carbon emitter and sink, and fully exploiting the carbon sink function of agriculture is highly valuable for ensuring carbon neutrality. The layout of net carbon in agriculture shows an imbalance, i.e., underscaling in areas with a net carbon advantage or overscaling in areas with a net carbon disadvantage, which restricts the fulfillment of agricultural carbon sinks. Clarifying the global pattern of net carbon imbalances in agriculture and their drivers is highly important for improving the overall net carbon. In this regard, this paper focuses on the global net carbon in agriculture in 155 countries and regions, and a spatial mismatch index of net carbon in agriculture (MNCA) is constructed based on the relative difference between net carbon and cultivated land. Spatial autocorrelation analysis and the standard deviation ellipse are used to reflect its spatiotemporal evolution pattern. In addition, through geographically weighted regression, we explored the effects of agricultural structure, agricultural economic development, the scale of labor and cultivated land and organized the mismatches into carbon and area effects. The results show that (1) global agricultural production represents net carbon emissions, while developing countries represent net carbon sinks, and there is a mismatch of net agricultural carbon in most regions; (2) the MNCA is characterized by low-low clustering in North America, Asia and Europe, with its center of gravity falling in western Africa and migrating northeastward; and (3) the main factors influencing the MNCA are the agricultural structure and economic level. Underscaling in areas with a net carbon advantage is a major problem. To increase the global agricultural carbon sink capacity, we suggest that North America, South Asia and other regions strictly implement agricultural emission reduction policies and that Central America, South America and Africa tentatively upgrade agricultural technology and their economies to alleviate farmland pressure through transnational cooperation. In this paper, the global net carbon imbalance in agriculture is identified and the overall implementation path of increasing carbon sinks is explored to provide a reference for achieving carbon neutrality. SynopsisThis study reports a global mismatch in net carbon in agriculture and identifies the wasted carbon sink advantage of developing countries as the root problem.

Research on the Spatial Network Structure of Tourist Flows in Hangzhou Based on BERT-BiLSTM-CRF

Tourist flows, crucial information within online travelogues, reveal the interactive relationships between different tourist destinations and serve as the nerve center and link of the tourism system. This study takes Hangzhou, China, as a case to investigate the spatial network structure of its tourist flows. Firstly, a BERT-BiLSTM-CRF model and pan-attraction database are built to extract tourist attractions from online travelogues and create the tourist flow matrix. Then, this study uses social network analysis (SNA) to examine the structure of the tourist flow network from a county-level perspective. Additionally, GIS spatial analysis methods are applied to analyze the evolution of the tourist gravity center and standard deviation ellipse (SDE) of the network. The results reveal that the identification performances of the tourist flow extraction model this study proposed are significantly better than those of previous mainstream models, with an F1 value of 0.8752. Furthermore, the tourist flow network in Hangzhou displays a relatively sparse and unbalanced distribution, forming a “Core–Semi-Periphery–Periphery” structure. Lastly, from 2020 to 2022, the network’s gravity center experienced a shift towards the southwest, paralleled by an initial expansion and subsequent contraction of the SDE in the same southwest direction. These findings provide valuable insights into the spatial network structure of tourism in Hangzhou and can serve as a reference for policymakers to promote the “all-for-one” tourism.

Spatiotemporal patterns, effects, and the interactive driving mechanisms of urban sustainability based on the eco-efficiency framework: Evidence from Chinese prefecture-level cities

Eco-efficiency is a vital tool for evaluating and advancing regional sustainable development. This study presents an urban eco-efficiency (UEE) framework that deserves widespread application. By using the Super Slacks-Based Measure model, the research calculates the UEE of 284 prefecture-level cities in China from 2005 to 2022. The study employs the Standard Deviational Ellipse to illustrate the spatiotemporal pattern of UEE in China and examines its effects using non-parametric kernel density estimation, the coefficient of variation, and both global and local Moran's I. The integration of UEE with urban development patterns, which is not immediately clear, is achieved through the entropy-based TOPSIS model. Additionally, the Malmquist index, the Two-way Fixed Effects (TWFE) model, and the Panel Vector Autoregression (PVAR) model reveal the dynamic changes and interactive driving mechanisms of UEE in China. The results indicate that: (1) UEE in China displays a consistent “core-periphery” pattern, exhibiting a notable hierarchical impact from regional, scale, and administrative perspectives. The complexity of UEE's spatiotemporal variations has increased due to the impact of COVID-19. (2) The UEE of Chinese cities shows a significant ‘club’ effect, with efficiency thresholds for high-high and low-low aggregations set at 0.8. (3) Only 25% of Chinese cities have achieved optimal UEE levels. Notably, cities in the west contribute most to national UEE growth, while midland cities contribute the least. The decreasing trend in UEE growth underscores the urgent need for a shift in China's urban development model. (4) UEE in China benefits from a resilient self-reinforcing mechanism, with urban development and economic growth significantly aiding UEE growth. Despite a non-significant negative impact from government intervention, the inhibitory effect of an intensive industrial structure on UEE turns into a promotional effect, especially following COVID-19.

A Study of the Spatial–Temporal Development Patterns and Influencing Factors of China’s National Archaeological Site Parks

National Archaeological Site Parks are strategic projects in China for innovatively exploring the scientific protection and rational utilization of large heritage sites for the purpose of balancing urban development and protecting cultural heritage. Therefore, research on the spatial–temporal evolution and influencing factors of National Archaeological Site Parks can improve and optimize their management and pattern systems and is therefore of great significance for the sustainable development of large sites and their surrounding areas. Targeting the 135 National Archaeological Site Parks of China, this paper adopts the nearest-neighbor index analytical method, the kernel density estimation method, the standard deviation ellipse method, the method of constructing an indicator system, and an SPSS factor analysis method to analyze the spatial–temporal evolution and influencing factors of these parks. The findings are as follows: (1) In terms of the temporal evolution, the number of National Archaeological Site Parks increased from 2010 to 2022, and the ages and types of the large heritage sites they are built upon gradually became more balanced and diverse. (2) In terms of the spatial evolution, National Archaeological Site Parks form two high-density areas in Central China and East China. Their overall distribution is consistent with China’s “Hu Line”. (3) In terms of spatial–temporal evolution, the center of gravity of National Archaeological Site Parks’ distribution is advancing toward Southwest China, and the trend of a more balanced distribution is rising. (4) Finally, regional development, heritage tourism, historical resources, and government support were observed to be factors that affect the spatial–temporal patterns of the National Archaeological Site Parks. Based on these findings, we propose specific strategies for coordinating and linking the above four major influencing factors to promote the rational utilization of large heritage sites and the sustainable development of National Archaeological Site Parks. We aim to improve and optimize the management and pattern systems of National Archaeological Site Parks, to promote urban renewal based on large heritage sites, and to provide valuable insights for policy makers and heritage practitioners in other countries with historical resources around the world.

Use of Remote Sensing and Geographic Information System for the Analysis of Urban Development: A Case Study of Banyumas Regency, Indonesia

The change in land-use/land-cover (LULC) is one of the physical factors in urban development and the rapid growth in population has necessitates the need for space, driven by high socio-economic activities. Banyumas is a regency that had experienced rapid population growth in the last two decades, establishing an activity and service center in the Barlingmascakeb region. This rapid population growth has led to massive changes in LULC. Therefore, this study aimed to observe the changes in LULC in 2000 and 2020 to determine the direction of urban development in Banyumas Regency within 20 years. Multispectral classification with a Maximum Likelihood algorithm was used to extract LULC information from Landsat images. The changes obtained by crosstab analysis on the multispectral classification results were used as a reference to observe the direction of urban development. This procedure used four quadrants according to the cardinal directions and Standard Deviational Ellipse (SDE). The result showed that LULC in the forest class experienced the highest change of 142,584.3 km², accounting for 48%. Based on the increase in built-up land over 20 years, the direction of urban development according to the cardinal directions showed that the most dominant increase was in quadrant II (Southeast), which is 56.44% or 21.95 km². It was concluded that the direction of urban development was oriented toward the southeast.

Pathways to enhance the efficiency of forestry ecological conservation and restoration: empirical evidence from Heilongjiang Province, China

This paper proposes a theoretical framework for assessing ecological protection and restoration from the perspective of ecological efficiency. We applied the super-efficiency Slack-based measure model to examine the social and economic impacts of ecological resource consumption transformation in Heilongjiang Province, China. Additionally, a convergence analysis was used to evaluate and test the impact of the standard deviation ellipse method on regional sustainability. The results indicated that the land use structure was unstable; the conversion rate of resource consumption was low; and the average Ecological efficiency was only 0.343 in terms of the land use structure. Funds for forest ecological restoration have a significant impact on the effectiveness of ecological resource transformation. Implementing the Chinese ecological restoration project improves the ecological efficiency level of the communities. The center of gravity of ecological efficiency moved greatly in the years when forestry investment increased. Technological transfer and diffusion, experience imitation in environmental regulation, and eventually convergent steady-state levels of the ecological efficiency of different regions are necessary to improve the economic and social development level of regions with low environmental quality efforts should be made to reduce resource consumption intensity, increase fund utilization efficiency, and form a comprehensive and systematic system of ecological environment governance through reasonable enhancement of regional environmental regulations, increased investment in technological advancement, and funds for ecological protection and restoration.

Research on the spatiotemporal evolution and non-stationarity effect of urban carbon balance: Evidence from representative cities in China

Accelerating the attainment of carbon balance in Chinese cities has become pivotal in addressing global climate change and promoting green, low-carbon development. This study, encompassing 277 prefecture-level and above cities from 2007 to 2020, reveals a positive overall trend in China's urban carbon balance index. The evolution unfolds in two stages, demonstrating a distinct “tiered development” pattern across the eastern, central and western regions. Moreover, significant spatial agglomeration characteristics characterize China's carbon balance hot and cold spots throughout the study period, with their spatial agglomeration degree remaining stable. The standard deviation ellipse analysis confirms these hot and cold spots' alignment with China's economic development level and population distribution. The GTWR test results highlight the pronounced non-stationary characteristics of different driving factors in space and time, exhibiting variations in strength and direction among regions. Consequently, enhancing China's urban carbon balance requires tailored measures based on different areas' unique conditions and development characteristics, emphasizing a hierarchical and classified approach to leverage distinct driving factors and foster a green development system in China.

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage (FVC), Wetness (WET), Evapotranspiration (ET), Land Surface Temperature (LST) and Normalized Difference Bare Soil Index (NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of “high in the east and low in the west, multi-point development”. (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73 % in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

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.

Spatial-temporal evolution and influencing factors of ecological resilience in urban agglomerations: a case study of Shanxi section of the Yellow River Basin

Scientific assessment of the development status and factors influencing the urban ecological resilience of the Yellow River Basin (YRB) is highly significant for promoting its development. This study constructed an evaluation index system for urban ecological resilience considering the four dimensions of pressure, state, response, and innovation. The spatiotemporal ecological resilience of the urban agglomeration (UA) in the Shanxi section of the YRB from 2012 to 2021 was studied using kernel density estimation, Dagum Gini coefficient, and standard deviation ellipse, and the influencing factors of urban ecological resilience were analyzed using a geographic detector. This research revealed that (1) the ecological resilience of cities in the Shanxi section of the YRB experienced a fluctuation process of rise—fall—rise. The urban ecological resilience generally reflected a gradient decreasing spatial pattern of Central Shanxi UA > South Shanxi UA > North Shanxi UA, and gradually changed from the dual core of “Taiyuan—Jincheng City” to the single core of Taiyuan City. (2) The migration trajectory of urban ecological resilience center of gravity fluctuated in the direction of “northwest-southeast,” and moved 12.63 km to the southeast overall. (3) The water supply per ten thousand Yuan GDP, occupied area of construction land per ten thousand Yuan GDP, green coverage rate in built-up areas, ratio of research and experimental development funds (R&D) to GDP, proportion of science and technology expenditure in local fiscal expenditure, and patent licensing quantity index have a high influence on urban ecological toughness at all stages. This influence was further strengthened by the interaction between factors. This study provides an important scientific basis for shaping high-quality development advantages in the YRB and creating a resilient and livable environment.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China

Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and neutrality. This study examines carbon emissions and economic and social problems data from 89 counties in Zhejiang Province. It employs analytical techniques such as LISA time path, spatio-temporal transition, and standard deviational ellipse to investigate the trends of carbon emissions from 2002 to 2022. Furthermore, it utilizes the GTWR model to evaluate the factors that influence these emissions on a county scale. The findings reveal the following: (1) The LISA time path analysis indicates a pronounced local spatial structure in the distribution of carbon emissions in Zhejiang Province from 2002 to 2022, characterized by increasing stability, notable path dependency, and some degree of spatial integration, albeit with a diminishing trend in overall integration. (2) The LISA spatio-temporal transition analysis indicates significant path dependency or lock-in effects in the county-level spatial clustering of carbon emissions. (3) Over the period 2002–2022, the centroid of carbon emissions in Zhejiang’s counties mainly oscillated between 120°55′15″ E and 120°57′01″ E and between 29°55′52″ N and 29°59′11″ N, with a general northeastward shift forming a “V” pattern. This shift resulted in a stable “northeast–southwest” spatial distribution. (4) Factors such as population size, urbanization rate, and economic development level predominantly accelerate carbon emissions, whereas industrial structure tends to curb them. It is crucial to customize carbon mitigation plans to suit the circumstances of each county. This study provides insight into the spatial and temporal patterns of carbon emissions at the county level in Zhejiang Province. It offers crucial guidance for developing targeted and practical strategies to reduce carbon emissions.

Ecological Vulnerability Assessment and Spatiotemporal Characteristics Analysis of Urban Green-Space Systems in Beijing–Tianjin–Hebei Region

The evaluation and analysis of the ecological vulnerability of urban green-space systems are conducive to the sustainable development of urban green-space systems. Taking the urban green-space system in the Beijing–Tianjin–Hebei region in 2010, 2015, and 2020 as the research object, an ESSR model was first constructed, and a total of ten types of impact factors were integrated into the four dimensions of “Exposure, Sensitivity, State and Response”. The weight of the impact factors was objectively determined via spatial principal component analysis, and the ecological vulnerability of the urban green-space system was evaluated via superposition analysis; the evaluation’s results were graded. Moreover, the transfer matrix, center-of-gravity migration model, standard deviation ellipse, and spatial autocorrelation analysis were used to study the temporal and spatial variation characteristics of the evaluation results; then, the driving force of impact factors was analyzed based on a geographical detector. Finally, the rationality of the evaluation results was verified using the changing trend of the remote sensing ecological index (RSEI). The results show that the ecological vulnerability of the urban green-space system in the Beijing–Tianjin–Hebei region has decreased year by year for the past ten years. The distribution law of “Hebei surrounds Beijing and Tianjin” is presented in this space, and 2015 is the mutation node. In 2010, the moderately and severely vulnerable areas of Hebei surrounded the mildly vulnerability areas of Beijing and Tianjin. Moreover, in 2015 and 2020, the mildly vulnerable areas of Hebei surrounded the severely vulnerable areas of Beijing and Tianjin. Vulnerability expands slightly in the east–west direction and shrinks slightly in the north–south direction, and the center of gravity shifts towards Beijing year by year along the northeast direction. Moreover, the spatial distribution of vulnerability shows significant positive spatial autocorrelations and exhibits very obvious agglomeration. In addition, vulnerability is the result of the combined effect of various factors, and education degree, human disturbance index, and annual average precipitation are the dominant factors. The analysis results provide a reference for the effective application and sustainable development of urban green-space ecological functions.

Measurement, Spatial-Temporal Evolution, and Optimization Path of the Level of Coordinated Development of Ecological Civilisation: The Case of China

Based on the energy–economy–environment–society (3E1S) system theory, this paper constructed a comprehensive evaluation system for the coordinated development of China’s ecological civilisation construction using panel data from 31 provinces and cities from 2005 to 2020, which is specifically divided into four sub-systems, namely, energy, economy, environment, and society, and covers 10 ordinal covariates and 40 ordinal variable indicators. In terms of measurement and evaluation methods, this paper adopted the composite system synergy model to measure the level of coordinated development of ecological civilisation construction of the country as a whole and 31 provinces and cities and adopted Arcgis maps, standard deviation ellipses, kernel density curves, and Theil index methods to explore the spatial-temporal evolution characteristics and regional differences in the level of coordinated development of ecological civilisation construction. It was found that the coordinated development level of China’s ecological civilisation construction increased year by year from 2005 to 2020, with the most obvious upward trend from 2011 to 2015, with an average increase of 26.5%. Secondly, the spatial distribution pattern of the coordinated development level of China’s ecological civilisation construction was relatively stable, basically showing a dominant southwest–northeast direction, but the spatial centre of gravity shows a trajectory shifting towards the southwest. Finally, there were large regional differences in the level of coordinated development of China’s ecological civilisation, with an overall decreasing regional pattern of “Central-Northeast-East-West”. However, the overall differences among the four regions showed a decreasing trend. The overall differences among the four regions were mainly intra-regional, with the western region having the greatest intra-regional differences and contributing to the overall differences. Although this paper takes China as a case study, its research methodology and conclusions can provide references for the construction of ecological civilisation and green sustainable development in other countries.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience, this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects. A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020. The spatial-temporal evolution characteristics were analyzed using Kernel density estimation, standard deviation ellipse, and spatial Markov chain analysis, and the spatial Tobit model was introduced to discover the influencing factors. The results indicate the following: ① Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend, with the center of gravity moving to the southwest, and the polarization phenomenon intensifying. ② The urban resilience level in a region has certain spatial and geographical dependence, while the probability of urban resilience transfer differs in adjacent cities with different resilience levels. ③ Urban centrality, economic scale, openness level, and financial development promote urban resilience, whereas government scale significantly inhibits it. Finally, this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Tracing human settlements: analyzing the spatio-temporal distribution of Buddhist temples in Nakhon Si Thammarat, Thailand

Buddhist temples offer invaluable historical insights into the movement of people across different historical periods and their interactions with the environment. This study focuses on the examination of temple locations in Nakhon Si Thammarat Province, with two primary objectives: analyzing the spatial distribution characteristics of temples across periods to trace the development of temples and settlements, and exploring the relationships between temple locations in each period and physical environmental factors/landforms. To achieve these objectives, GIS tools, including average nearest neighbor (ANN) analysis, kernel density estimation (KDE), and standard deviational ellipse (SDE) analysis, are employed. The study reveals significant patterns. Temples exhibit varying concentrations and shifts over periods, primarily along the ancient trans-peninsular route, with the area around Wat Phra Mahathat Woramahawihan emerging as a prominent center. Over time, temples were built at higher elevations and moving away from waterways. The preference for constructing temples on flat terrains is predominant. In the Sukhothai period (prior to 1349 CE), temples were often situated on flood-resistant sandbars. In the Ayutthaya period (1350 CE – 1767 CE), temples were strategically positioned on plains near waterways and trade routes. During the Thonburi period (1768 CE – 1781 CE), temples were primarily located at higher elevations. From Rattanakosin to present (1782 CE – present), temples expanded to higher elevations due to economic crop cultivation, population growth, and road network development. This study sheds light on the dynamic development of temples and their relationship with environmental factors, contributing to our understanding of historical human settlements and interactions with the landscape.

Navigating the Efficiency Landscape: A Data Envelopment Analysis of Tourist Resorts in Jiangsu Province for Optimized Socio-Economic Benefits

Tourist resorts stand out as a focal point in the academic discourse on tourism, garnering significant attention within the tourism academic community. Assessing the efficiency of these resorts serves as a crucial tool for steering their management strategies, optimizing resource allocation, and contributing to regional economic development. This study centers on tourist resorts in Jiangsu Province, employing the data envelopment analysis method to gauge their tourism efficiency. The research delves into the impact of decomposing the efficiency of tourist resorts and investigates the spatiotemporal dynamic patterns of various efficiencies. Key findings indicate that: (1) The overall tourism efficiency of tourist resorts in Jiangsu Province registers as low, with an average of only 0.119, signaling ample room for improvement towards optimal levels. Among different efficiencies, scale efficiency exhibits the highest average value, followed by pure technical efficiency, with comprehensive efficiency ranking the lowest. (2) The comprehensive efficiency of tourist resorts in Jiangsu Province is influenced by the combined effects of various decomposition efficiencies. Notably, pure technical efficiency plays a more substantial role in overall efficiency compared to scale efficiency. (3) Spatial differentiation in efficiency values is evident among tourist resorts in Jiangsu Province. High-efficiency areas, particularly the southern Jiangsu region, display concentrated clusters, emphasizing a pronounced agglomeration of scale efficiency. In contrast, the central and northern regions of Jiangsu witness a rising number of tourist resorts demonstrating pure technical efficiency and high overall efficiency. (4) Over the research period, the focus of various efficiency factors in tourist resorts shifted towards the north, albeit without significant deviation. Simultaneously, the standard deviation ellipse area of various efficiencies exhibits a general trend of expansion. Drawing from these research outcomes, the article recommends practical measures such as enhancing the diversity of vacation resort services, establishing interactive mechanisms, and attracting management talent. These suggestions aim to provide actionable guidance for the development of tourist resorts, contributing to their sustained growth and success.