Kernel Density Research Articles

Progress in Digital Climate Governance in China: Statistical Measurement, Regional Differences, and Dynamic Evolution

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China’s regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy.

Early Holocene inundation of Doggerland and its impact on hunter-gatherers: An inundation model and dates-as-data approach

Sea-level rise was a key factor changing environments during the Early Holocene in Northwest Europe. It affected Mesolithic hunter-gatherer communities by inundating large areas in the current North Sea, commonly referred to as Doggerland. In this paper we present novel inundation models for the southern North Sea providing visualisations of lateral inundation driven by sea-level rise and relate it to frequency analysis of radiocarbon dates from archaeological sites. These improve on previous studies that relied on bathymetric data, which includes post-inundation overprints of Holocene sedimentation and erosion, and thus significantly underestimates the timing of inundation in some areas.We constructed a paleoDEM (a composite elevation grid of the top of the Pleistocene) for the eastern part of the southern North Sea; and sea level surfaces that combine relative sea-level curves from glacio-isostatic adjustment models optimised for Britain and southern Scandinavia respectively. We corrected our paleoDEMs for tectonic background basin subsidence, and in the inundation modelling account for pre-compaction elevation of peat in coastal areas. We evaluated the impact of these model components on our results and describe the possible inundation history of Doggerland. We suggest earlier inundation than predicted by previous models, showing significant area loss around 10.5–10 ka cal BP.Palaeogeographic changes are compared with archaeological radiocarbon data using a dates-as-data approach. Composite Kernel Density Estimate and permutation tested Summed Probability Distributions are used as a proxy for the visibility, nature and intensity of human activity. Results indicate key periods of growth and decline recorded in the dataset, as well as regional differences in growth rate, some correlating with inundation phases. Chiefly, we find elevated growth rates around 10.5–10 ka in northwest Germany and the Netherlands, contemporaneous with the abovementioned phase of extensive area loss, and moreover, with changes in culture and practices on Early Mesolithic sites.However, the spatiotemporal distribution of archaeological data is significantly influenced by accessibility and preservation of sediments of a certain age. We discuss the importance of inundation modelling and sediment data in understanding how landscape taphonomy affects archaeological patterning, especially in dates-as-data approaches.

Spatial distribution, influencing factors and innovative development countermeasures of farmer cooperatives in Hunan Province, China

China’s rural reform is reliant on farmers' cooperatives as a key organization vehicle. It plays an important role in promoting rural revitalization. Such as, realizing the organic connection between small farmers and agricultural modernization. This study used the nearest neighbor index and kernel density estimation to analyze the spatial distribution and structural characteristics of farmer cooperatives in Hunan Province. It revealed the spatial differentiation law of cooperatives. Also using geographical detectors to figure out the main factors that affect the spatial distribution. The results show that: ① Hunan Farmers Cooperatives show significant spatial agglomeration. The overall spatial distribution shows the spatial characteristics of "one core, one circle, and multiple points". And the spatial distribution pattern of "large agglomeration, finger-shaped radiation distribution". Among them, the distribution of provincial demonstration cooperatives is relatively balanced. ② Cooperatives in six types of industries, such as planting, forestry, and feeding, showed agglomeration distribution. Different industrial cooperatives spread out in different ways, such as in an anti-"L" shape, a dual-core shape, or a one-center area. ③ The 24 influence factors selected from the five dimensions of the natural environment, social economic basis, production basis, output capacity, and facility basis have high, general, and weak influence on cooperatives' spatial distribution. The development and distribution of cooperatives mainly depend on resource endowment, social and economic development level, and market dependence. The spatial distribution heterogeneity of different professional cooperatives is mainly affected by slope, per capita disposable income of rural residents, road density, and other factors. ④ The progression of farmer cooperatives in Hunan Province should prioritize high-quality development, emphasizing the need for innovative approaches and transformative strategies within rural industrial organizations. It is imperative to optimize the spatial distribution of cooperatives, strategically contributing to the establishment of a novel framework for modern agricultural development in Hunan Province. Additionally, there is a critical emphasis on spearheading collaborative initiatives among cooperatives of varied industrial types, aimed at fostering the integrated development of rural primary, secondary, and tertiary industries.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

International comparison of China's digitalization level and its enlightenment.

The global digital wave has flourished in recent years, and the digital technology revolution has emerged. Digitalization plays an undeniable role in promoting modern economic and social development in multiple aspects such as economy, society, innovation, public services and sustainable development, China's digitalization application is also developing rapidly. In order to better measure the current situation of China's digitalization development level, this paper constructs a comprehensive evaluation index system of digitalization development from four dimensions of talents in the digital field, digital infrastructure construction, digitalization innovation ability and international competitiveness, and tests the index system. The entropy method is used to measure the level of digitalization development between China and the United States, the United Kingdom, France and other major developed countries in the world, and the coefficient of variation method, kernel density estimation and Dagum Gini coefficient method are used to analyze the temporal and spatial characteristics and regional differences of digitalization development level of seven countries. This paper makes a comparative analysis between China and major developed countries from the historical perspective of the evolution of the techno-economic paradigm. With a view to summarizing and exploring from it and drawing on the advanced experiences accumulated by the developed countries over a long period of time, so as to provide China with useful insights and bases for further enhancing its digitalization development level.

Study on the distribution of average wind speeds at a mountainous bridge site for structural durability design

Conventional wind speed distribution methods (e.g., Rayleigh distribution and Weibull distribution) may not adequately capture complex characteristics of wind fields in mountainous areas. To address this problem, this study proposes a semi-parametric mix method for modeling the distribution of average wind speeds based on the combination of nonparametric Kernel Density Estimation (KDE) and Generalized Pareto Distribution (GPD). In the proposed method, KDE focuses on capturing the distribution in the main part of average wind speeds, while GPD aims at performing the distribution in terms of those in the extreme part. The segment point (i.e., the threshold) between KDE and GPD distributions is determined based on the combination of conditional mean excesses criterion and empirical rule. Meanwhile, the selection of modeling parameters should ensure that the mix distribution model is continuous and differentiable at the identified threshold point. Then, the commonly-used conditional probability model is further introduced to describe the wind direction distribution. Finally, a case study based on the measured 10-min average wind speeds at a mountainous bridge site is employed to demonstrate the effectiveness of the proposed method. The results indicate that: (1) the distribution of omnidirectional average wind speeds in the mountainous bridge site exhibits an obviously single-peak characteristic, while those considering wind directionality present a certain bimodal characteristic; (2) the proposed method can effectively describe wind speed distributions with different statistical characteristics, and the fitting accuracy outperforms the frequently-employed Weibull distribution model.

A window into the forest: post‐release behaviour of rehabilitated Bornean sun bears Helarctos malayanus euryspilus in Sabah, Malaysia

As the number of threatened wildlife species increases globally, captive rehabilitation and release of wild caught animals plays an increasingly significant role in conservation efforts, particularly where poaching threatens wild populations. Sun bear Helarctos malayanus populations are decreasing globally, with habitat loss and fragmentation and poaching as key threats to the species. This study aimed to assess post‐release behaviour of rehabilitated sun bears (n = 12) using GPS‐collar data to inform ongoing efforts to rehabilitate wild caught bears. We assessed spatial patterns of movement using kernel density estimates and utilisation distributions. We utilised an information theory (IT) and generalised linear modelling approach to assess temporal patterns of sun bear movement (n = 9). Post‐release home range estimates (mean = 56.68 km2, range:1.9–202.4) and behaviour varied widely between bears, with no significant difference in home range size between males (n = 3) and females (n = 6). Home ranges of most bears stabilised within the first month. Bears that were rehabilitated and released together did not spend a significant amount of time together following release. Bears were most active during daytime hours and most established a post‐release home range encompassing the riverside. The ultimate success of the releases is unknown, due to collar malfunction and damage, and the inaccessibility of the study terrain. We find that future releases of sun bears would benefit from soft‐release and in situ rehabilitation techniques. This study provides important insights into the complexity of bear reintroductions informing future rehabilitation and releases for threatened bear species.

A simple method for mapping winter recreational fishing ecosystem services supply in lakes. A contribution to mapping freshwater ecosystem services

Although urban areas negatively impact the environment, they supply a wide range of ecosystem services (ES), mainly cultural ones. Recreation near urban green areas is widespread, including fishing. In northern latitudes, during the winter, lakes are frozen, and several urban dwellers practice ice fishing. Although this activity is well known, no attempts were made to assess and map winter recreational fishery ES supply in lakes. In this work, we developed a methodology to map this ES, taking an urban lake in Vilnius (Lithuania) as an example. A standardized protocol was developed using an unmanned aerial vehicle (proximal sensing), further georeferencing and correcting the gathered images, vectorizing the fishing ice holes, and mapping them using two different methods: Kernel and Point Density. The method developed in this work can be applied in northern areas to identify recreational fishing ES during the winter.•A novel method was developed to map winter recreational fishery ES supply in lakes;•High-resolution images were taken from an unmanned aerial vehicle to identify fishing ice holes in an urban lake.•The method maps a cultural ES, which is trendy in northern latitudes.

Spatiotemporal Mechanisms of the Coexistence of Reintroduced Scimitar-Horned Oryx and Native Dorcas Gazelle in Sidi Toui National Park, Tunisia.

Examining the distribution patterns and spatiotemporal niche overlap of sympatric species is crucial for understanding core concepts in community ecology and for the effective management of multi-species habitats within shared landscapes. Using data from 26 camera-traps, recorded over two years (December 2020-November 2022), in Sidi Toui National Park (STNP), Tunisia, we investigate habitat use and activity patterns of the scimitar-horned oryx (n = 1865 captures) and dorcas gazelle (n = 1208 captures). Using information theory and multi-model inference methods, along with the Pianka index, we evaluated the habitat characteristics influencing species distribution and their spatial niche overlap. To delineate daily activity patterns, we applied kernel density estimation. Our findings indicate minimal spatial overlap and distinct environmental factors determining suitable habitats for each species. Furthermore, we found significant temporal niche overlaps, indicative of synchrony in daily activity patterns, with both species showing peak activity at dawn and dusk. Our results indicated that oryx and gazelle differ in at least one dimension of their ecological niche at the current density levels, which contributes to their long-term and stable coexistence in STNP.

Big data analytics for quality variation over work shifts in manufacturing systems

ABSTRACT In the manufacturing industry, mass production enables manufacturers to produce parts with high precision and lower costs. Multiple shifts operate production processes to maximize efficiency. Several researches have been conducted on the impact of shift work on labor’s health and habits. However, there have been few studies on the influence of shift work on the consistency of product quality. This paper provides a methodology to analyze the impact of different work shifts in a real electronic board manufacturing industry. The study uses big data and analytics to assess product quality from data. Non-parametric kernel density estimation is used to approximate the distribution of good products in each work slot. Then several metrics are used to measure the dissimilarity between the estimated densities. The approach can be leveraged in various problems related to process performance and quality. The obtained results show that there is no significant difference in terms of product quality between work shifts. These prove the consistency of the manufacturing processes and the homogeneity of performance across work shifts in the studied factory. Compared to the literature, the paper presents the first quantitative analysis to compare production process performance and product quality over shift works.

Assessing the environmental factors affecting the sustainability of Aini Falaj system.

This study investigates the spatial distribution patterns and environmental factors influencing the Aini Falaj system in a specific study area. The research findings are presented through the lens of the following four categories: collinearity diagnostics, spatial autocorrelation analysis, kernel density (KD) findings, and multivariate geographically weighted regression (MGWR) analysis. The collinearity diagnostics were applied to examine the interrelationships among 18 independent environmental variables. The results indicate the absence of significant multicollinearity concerns, with most variables showing values below the critical threshold of five for variance inflation factors (VIFs). The selected variables indicate minimal intercorrelation, suggesting that researchers should be confident utilizing them in subsequent modelling or regression analyses. A spatial autocorrelation analysis using Moran's Index revealed positive spatial autocorrelation and significant clustering patterns in the distribution of live and non-functional Aini Falajs. High concentrations of live or dead Falajs tended to be surrounded by neighbouring areas with similar characteristics. These findings provide insights into the ecological preferences and habitat associations of Aini Falajs, thereby aiding conservation strategies and targeted studies. The kernel density (KD) analysis depicted distribution patterns of live and dry Aini Falajs through hotspots and cold spots. Specific regions exhibited high-density areas of live Falajs, indicating favourable environmental conditions or historical factors contributing to their concentrated distribution. Identifying these high-density zones can enhance our understanding of the spatial patterns and potential factors influencing the prevalence and sustainability of Aini Falajs. The multivariate geographically weighted regression (MGWR) models revealed strong associations between the live or dead status of Aini Falajs and environmental factors. The precipitation, topographic wetness index (TWI), aspect and slope exerted positive impacts on the live status, while evaporation, solar radiation, distance to drains and drain density exerted negative influences. Similar associations were observed for the dead status, emphasising the importance of controlling evaporation, shading mechanisms, proper drainage planning and sustainable land-use practices. This study provides valuable insights into the spatial distributions and factors influencing the live and dead status of Aini Falajs, thereby contributing to our understanding of their ecological dynamics and guiding conservation efforts and management strategies.

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.

Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience.

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.

Exploring the impact of seasonal forest landscapes on tourist emotions using Machine learning

This study investigates the emotional changes of tourists experiencing natural forest landscapes in different seasons, which plays a crucial role in the sustainable development of forest landscape systems and forest tourism. However, existing research has limited focus on the temporal impact of detailed elements of forest landscapes on tourists' emotions. We integrated a research framework utilizing methods such as Object Semantic Attention Network (OSANet), High-Resolution Net (Hrnet), EXtreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP), thereby uncovering the nonlinear impact of detailed forest landscape indicators on tourist emotions. The results revealed(1) In the past three years, the emotional score assessments of tourists' experiences in natural forest landscapes showed a prevalence of positive emotions at 85.7%, with negative emotions constituting 14.3%. The emotional distribution characteristics varied significantly across seasons, with kernel density peak values of 4.16 in spring, 2.43 in summer, 5.33 in autumn, and 1.74 in winter. (2) The landscape indicators with the most significant impact on emotions across different seasons were Openness in spring, summer, and autumn (with contribution values exceeding 0.16, 0.13, and 0.175, respectively) and Ground exposure in winter (with a contribution value greater than 0.2). The SHAP waterfall charts indicated that the indicators providing a stable positive effect varied by seasonTrees in spring (0.361), Openness in summer and autumn (0.276 and 0.24, respectively), and Mountains in winter (0.145). This study provides a data foundation for the sustainable development of forest landscapes and tourism, aiding planners and managers in understanding the nonlinear impact of detailed elements of forest natural landscapes on tourists' emotions across different seasons. It also broadens the methodologies and perspectives for researching seasonal forest landscape preferences.

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin.

Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)

Abstract The determination of suitable habitats and roadkill hotspots allows for the prioritization of areas of conservation and the identification of road sections that require appropriate planning and development. Understanding the factors that influence the species’ presence and those contributing to its road-related fatalities is crucial. This study demonstrates the use of MaxEnt modeling to map the potential distribution of the striped hyaena (Hyaena hyaena) in Batna province (East Algeria) using occurrence records and a set of environmental variables. The results indicated that the two primary environmental variables that influence the distribution of the species are the shrubland, and the slope with a contribution of 37.5 and 30.2 %, respectively, followed by built-up areas (12 %) and distance to roads (7.2 %). The species may occupy steep terrain with shrubland near anthropized areas. Road collisions represent a threat to the species, with 28 documented casualties from 2010 to 2020 in Batna. The kernel density estimation revealed an important roadkill hotspot along the national roads 3 and 28. This study represents a preliminary step for the use of wildlife ecological niche modeling and road management in Algeria.

Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.

Spatio-temporal evolution and driving factors of green innovation efficiency in the Chinese urban tourism industry based on spatial Markov chain

Green innovation in the tourism industry is a sustainable development concept for resource conservation and environmental optimization. The effective measurement of green innovation efficiency in the tourism industry and an accurate understanding of its spatial relationship was significantly important for promoting its sustainable development. Using the SBM-undesirable model, kernel density estimation, and a spatial Markov chain, we explored the spatio-temporal evolution characteristics and influencing mechanisms of urban tourism green innovation efficiency (TGIE) in China between 2000 and 2020. We found that (1) the temporal and spatial changes of TGIE were generally at a lower than medium level and fluctuated throughout country, with a transition in the east, collapse in the middle, and stagnation in the northeast. (2) The dynamic evolution of TGIE always exhibited polarization, but regional coordination was gradually enhanced with strong stability, although it was difficult to achieve leap-forward development. The cities with spatial upward transfer were concentrated mainly in the central and western region and while there were few cities with a downward adjustment, there were obvious asymmetrical spatial spillover effects. (3) The driving factors of TGIE were the overall economic level, industrial structure, government regulation, and education level. These factors had a significant positive relationship with TGIE, while the degree of opening up to the outside world has no significant effect, but the degree of influence, mechanism, and conditions of each factor were strongly regional.

Spatial Distribution Characteristics and Influencing Factors of Geographical Indication Agricultural Products in Shaanxi Province

Geographical indication agricultural products are of great significance to promote the high-quality development of rural revitalization due to their unique quality and brand value. Based on the geographical concentration index, the nearest neighbor index and the kernel density estimation method, this paper empirically studies the spatial distribution characteristics and influencing factors of 117 geographical indication agricultural products in Shaanxi Province. The results show that the spatial distribution of geographical indications in Shaanxi is extremely uneven, showing the characteristics of ' dense in the south and sparse in the north, regional agglomeration '. From the perspective of density distribution characteristics, the overall spatial distribution of geographical indications in Shaanxi shows a distribution pattern of ' three high and three times ', and the three high-density areas show a agglomeration distribution pattern of ' pin-shaped '. The difference between the north and the south is obvious, and the overall distribution pattern is ' cold in the north and hot in the south '. The spatial distribution pattern of geographical indications in Shaanxi is affected by many factors. Natural factors are the basis, social and economic factors strengthen the spatial distribution pattern of geographical indications, and government-led and policy factors play a regulatory and guiding role. It is suggested to optimize the spatial distribution pattern of geographical indications in Shaanxi, rely on local social resources, give full play to the leading role of the government, and actively promote the industrialization development of geographical indication products.