Spatial Pattern Characteristics Research Articles

An EEG channel selection method for motor imagery based on Fisher score and local optimization

Objective. Multi-channel electroencephalogram (EEG) technology in brain–computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness. Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection. Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%. Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

The Spatial Pattern of Polluting Enterprises and the Effects of Local Regulation in the Guanzhong Plain Urban Agglomeration

In the context of actively undertaking the transfer of domestic and foreign industries in the central and western regions of China, local regulatory behaviors influence the spatial distribution of polluting enterprises (PEs). This study examined the Guanzhong Plain urban agglomeration (GPUA), the largest urban agglomeration in the northwest region of China and one of the main regions that undertakes industrial transfer, using kernel density estimation and geographically and temporally weighted regression to explore the spatial pattern characteristics and evolution of PEs and reveal the effects of local regulatory behaviors, including environmental regulation (ER) and local protection (LP). The results indicate that (1) The distribution of the PEs tended towards energy and mineral resources and agglomerated along the development axes, aligning with the strategic positioning of the major function-oriented zones. Agglomerated areas gradually concentrate in key development zones. Major agricultural production zones exit high-density areas, and those adjacent to high-density areas often become secondary agglomeration core areas. Key ecological functional zones do not form high-density areas. (2) Both ER and LP have a positive impact on the distribution of PEs, and the dominant influence shifts from ER to LP. Counties with strict ER have increased the tendency of PEs to exhibit a certain layout due to better pollution treatment facilities and more mature pollution control technologies. The “pollution haven effect” has not yet formed within the GPUA. (3) The role of LP was more prominent in key development zones and major agricultural production zones, whereas the role of ER was more evident in key ecological functional zones. (4) RE and LP have mutually reinforcing effects on the distribution of PEs; the “innovation compensation effect” gradually manifests, but an increase in ER leads to a decrease in regional industrial clustering. This study provides a reference value for understanding the impact of government regulation on the distribution of PEs in underdeveloped areas.

Monitoring the Landscape Pattern Dynamics and Driving Forces in Dongting Lake Wetland in China Based on Landsat Images

Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km2/a); sharp decrease until 2014 (−19.44 km2/a); and steady increase (22.93 km2/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km2/a), then decreased (−35.89 km2/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.

Adaptability Evaluation of Human Settlements in Chengdu Based on ArcGIS

This study establishes the evaluation index system of Chengdu’s habitat suitability based on three dimensions (the ecological livability environment, economic development environment, and social security environment) and quantitatively investigates the habitat suitability of Chengdu using the Analytic Hierarchy Process (AHP) and ArcGIS10.8 tools. Additionally, it analyzes the spatial pattern characteristics of Chengdu’s habitat suitability to provide insights into the rational optimization of Chengdu’s habitat system, and show that (1) the adaptability index of Chengdu’s human settlement is between 18.39 to 92.35, and the habitat suitability exhibits a high spatial distribution in the central area and a low spatial distribution in the surrounding regions, with obvious differences between hot spots and cold spots. (2) According to the suitability index from high to low, the habitat of Chengdu is divided into five regions: the most suitable area (895.62 km2, 6.25%), highly suitable area (2136.82 km2,14.91%), moderately suitable area (5755.80 km2,40.15%), low-suitability area (4580.61 km2, 31.95%) and the unsuitable area (966.15 km2, 6.74%). (3) The spatial distribution of habitat suitability in Chengdu demonstrates a certain coupling relationship with the city’s circular social development model. Moreover, the spatial distribution characteristics of each area exhibit good consistency with population density, natural environment, economic conditions, and social conditions. (4) The influence of each indicator factor shows spatial heterogeneity, with variations in different subregions. Additionally, different regions have their own advantages and disadvantages. The results show that there are obvious regional characteristics with the suitability of human settlements in Chengdu; the main urban area exhibits the highest degree of habitat suitability, while the western Longmen Mountain area shows relatively lower suitability in its habitat. Considering these spatial characteristics, future development should explore corresponding development modes for each region based on local conditions, aiming to reduce spatial differences and promote the integrated development of urban and rural areas.

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models.

Spatial pattern characteristics and optimization policies of low-carbon innovation levels in the urban agglomerations in the Yellow River Basin

This paper refines a comprehensive index of urban low-carbon innovation based on urban low-carbon innovation concept and an urban low-carbon innovation community structure and function. This index is complemented by an indicator reflecting equity in terms of urban-rural income gap, some indicators of low-carbon knowledge and technology innovation, as well as an indicator of the characteristics of a carbon circular economy, and a further refinement of the indicator of low-carbon innovation system. We use the entropy value method, kernel density analysis, gravity model, and social network analysis to study the low-carbon innovation spatial pattern in urban agglomerations (UAs) of the Yellow River Basin (YRB). Innovations in the findings are: (1) the low-carbon innovation level in the UAs in the YRB decreases in spatial distribution from the downstream to the midstream to the upstream UAs. (2) Reveal the spatial pattern laws of low-carbon innovation levels in different types of UAs in the YRB. The spatial pattern in regional level UAs is more complex and hierarchical than that in area level UAs; but there are still gaps in the hierarchy of spatial patterns of low-carbon innovation levels between UAs at the regional level, as well as between UAs at the area level. The spatial pattern of two regional-level UAs in the YRB is transitioning from a low-carbon innovation point-axis to a low-carbon innovation network structure, and the spatial pattern of one regional-level UAs is at the embryonic stage of the low-carbon innovation point-axis structure. The spatial pattern of one area-level UA is at the embryonic stage of a low-carbon innovation point-axis structure, and the spatial pattern of three area-level UAs is at the stage of low-carbon innovation pole. The spatial pattern of low-carbon innovation becomes more complex and hierarchical as we move from upstream to midstream to downstream UAs. (3) Use social network analysis and the study show that the low-carbon innovation network is dominated by the internal correlations among the UAs in the upstream, midstream, and downstream. The low-carbon innovation linkages between upstream, midstream and downstream UAs are weak. Finally, we put forward corresponding policy recommendations.

Characteristics and prediction of agricultural ecological efficiency for coordination between economy and environment.

Agricultural ecological efficiency is an important tool with which to measure the coordination of the sustainable development of agricultural economies and ecological environments. In this paper, a super-efficiency slacks-based measures model was used to measure the agricultural ecological efficiency in Hebei Province. The characteristics of spatial and temporal evolution patterns were explored using a spatial Markov transfer matrix. The results showed that (i) based on measurements, the agricultural ecological efficiency in Hebei Province showed regional differences in four regions (eastern, northern, central and southern Hebei) and 141 counties; (ii) from the perspective of evolutionary characteristics of agricultural ecological efficiency, the overall development of in Hebei Province was good, with more concentrated spatial distribution and more obvious direction, while the type of transfer of agricultural ecological efficiency in Hebei Province showed strong stability that was significantly affected by geographical neighborhood conditions and the club convergence phenomenon; (iii) from the perspective of the long-term evolutionary trend of agricultural ecological efficiency, the areas adjacent to counties with low efficiency had limited potential for improvement, and the areas adjacent to counties with high grade had great potential. However, it was difficult to achieve large-scale improvement in agricultural ecological efficiency in Hebei Province, whether the impact of geospatial backgrounds was considered or not.

Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region

Based on the “Production-Living-Ecological Space” evaluation system, we hereby form its functional index and take the results of corresponding functional measurement to analyze the spatial pattern, functional evolution, and coupling characteristics of the “Production-Living-Ecological Space” of the Yangtze Delta Region. The results show that: (1) From the spatial pattern, the production space is mainly distributed in the plain areas. The living space is concentrated in the dense urban areas along the Yangtze River, the East Sea, and the East Jiangsu–Gansu Line. The ecological space is concentrated in mountainous hill areas. (2) From the spatial transformation, production space expands slightly, living space continues to expand, and ecological space shrinks significantly. (3) The functionality of “Production-Living-Ecological Space” exhibits a high level in hilly mountainous areas and a low level in plains, with an overall downward trend. Notably, the core cities within the Yangtze Delta Region have experienced the most significant decline in functionality. (4) The natural environment is the decisive factor for the overall pattern of “Production-Living-Ecological Space”, while economic and social development is the core driving force of the evolution of the spatial pattern, and regional integration is the catalyst of the evolution of the spatial pattern. (5) The coupling coordination of “production-ecology” is exceptionally strong, with the living function holding an overwhelmingly dominant position.

Spatial Pattern of Large-Scale Agricultural Land and Spatial Heterogeneity of Influencing Factors in the Mountainous Areas of Western China—Wuling Mountains as an Example

The scaling of agricultural land is a trend in land use transformation and is important for modernizing agriculture. Therefore, the reasons for large-scale agricultural land formation should be explored. The spatial distribution of large-scale agricultural land and the factors for its formation vary between different regions. Currently, the exploration of the formation mechanism of large-scale agricultural land from the perspective of heterogeneity is not yet sufficient. Therefore, the main objectives of this article are as follows: first, analyze the spatial pattern characteristics of large-scale agricultural land; second, explore the spatial heterogeneity characteristics of influencing factors from both global and local perspectives; third, explore the mechanism of the formation of large-scale agricultural land from the perspective of heterogeneity. The results indicate the following: (1) The large-scale agricultural land distribution pattern in the Wuling Mountains area was high in the east and low in the west. (2) Natural conditions, production factors, and location conditions all significantly impacted large-scale agricultural land, but with differences in their degree of influence. From a local perspective, the influences of various factors in different regions also exhibited spatial heterogeneity. These two types of heterogeneity can be attributed to the differences in regional development stages. (3) Natural conditions, location conditions, and production factors had negative, positive, and positive effects on the agricultural land scale, respectively, but the influence of the first two decreased with the improvement in the regional development stages. The influence of different factors on production factors was related to the regional development stage, and production factors that were suitable for the regional development stage had a greater impact. The conclusion can provide differentiated policy support for regional land use in practice.

Study on the coupling and coordination relationship between the digital economy and green development: evidence from Guangdong province in China

The digital economy city and green development of China are important engines with important backgrounds. This paper takes 21 prefecture-level cities in Guangdong Province as the research object. By constructing the index system of the digital economy and green development, this paper discusses the external spatial pattern and internal temporal evolution characteristics of the coordinated development of the digital economy and green development from the two perspectives of intercity and inner city. The results show that: 1) From the perspective of intercity, there is a large gap between the development level of the digital economy and the green development level of all cities, only Guangzhou and Shenzhen have the digital economy development level ahead of the green development level. The coupling level of the two systems presents an unbalanced pattern of “centre-edge,” and “Guangzhou-Foshan-Shenzhen-Dongguan” becomes the core pole of the coupling development of the two systems in the province. The coordinated horizontal cascade distribution of the two systems is obvious, showing a spatial pattern of decreasing step by step from the first circle to the third circle. 2) From the perspective of inner city, the digital economy level and green development level of all cities showed an upward trend from 2013 to 2019. The coupling level shows a two-stage development trend: the stable development stage from 2013 to 2016, and he accelerated development stage from 2016 to 2019, and the internal interaction of the two systems is deepening. The coordination level of all cities improved significantly in 7 years, and in 2019, 21 cities all reached the level of moderate coordination or high coordination. Based on above research conclusions, in order to promote the positive interaction between digital economy and green development, this paper proposes to optimize resource allocation based on regional differences, and adhere to innovation-driven policy proposals to activate the potential of green development.

한국의 지역안전지수 공간패턴 변화와 분포특성 연구 : 2015년과 2020년 차이를 중심으로

한국의 지역안전지수 공간패턴 변화와 분포특성 연구 : 2015년과 2020년 차이를 중심으로

Analysis of the Temporal and Spatial Pattern and Convergence Characteristics of High-Quality Sustainable Economic Development of Urban Agglomeration

Based on the sample data of 149 cities in ten major urban agglomerations from 2004 to 2019, the entropy method, Dagum Gini coefficient, and three-dimensional kernel density estimation method are used to calculate and describe the spatial pattern of the high-quality, sustainable economic development of these ten major urban agglomerations. We then use the spatial econometric model to estimate the β convergence trend within the urban agglomerations and among the urban agglomerations at different levels. Our main findings include the following: First, the urban agglomeration tends to develop a high-quality economy, but the gap between the urban agglomerations can be large. Second, although the gap within the overall group of urban agglomerations is expanding, the gap between high-quality individuals and the average is constantly shrinking; the gap between groups is still the leading cause of the spatial gap, with a contribution rate of 70.51%. Third, all urban agglomerations have an absolute and conditional β convergence trend, and the convergence speed presents the characteristics of “high level slow, low level fast”. Government intervention, financial development, urbanization, and human capital contribute to the high-quality, sustainable economic development of each urban agglomeration. There is a heterogeneous influence; there is also absolute and conditional β convergence among urban agglomerations at all levels, and the convergence rate presents a gradient characteristic of “third level > second level > first level”, and by balancing the financial relationships between city groups within each level, development differences can promote the dynamic coordination of high-quality, sustainable economic development rates.

Analysis of Spatial Pattern and Geographical Divergence Characteristics of Traditional Villages Oriented to Digital Landscape Technology

Abstract In order to study the spatial pattern and geographical differentiation characteristics of traditional villages, this paper combines digital landscape technology to analyze the influencing factors of the distribution of traditional villages. Based on multi-source data, this paper reveals the spatial pattern and geographical differentiation characteristics of traditional villages with the help of GIS, RS and mathematical and statistical methods and systematically explores the inner mechanism and laws. It also combines digital landscape technology, which is in line with the trend of the times, and designs an image visual optimization model based on digital landscape technology so as to make the experimental analysis of the distribution of traditional villages more convenient. It is obtained that the 3D modeling errors using digital landscape technology are all lower than 1.12%, the 3D modeling errors of the overhead GIS 3D modeling method are between 3.21% and 4.58%, and the 3D modeling errors of the digital photography 3D modeling method are all higher than 4.82%. The distance from the urban center, population density, ethnic distribution, and average annual temperature was the main influencing factors, all of which exceeded the explanatory power of 0.4. Average annual rainfall, administrative center, and GDP per capita were the secondary influencing factors, with explanatory power over 0.15. Based on this study, the spatial distribution density of traditional socio-economic villages was more influenced by socio-economic factors, followed by physical geographic factors.

Robust Spatial Durbin Model Untuk Pemodelan Tingkat Pengangguran Terbuka di Provinsi Jawa Tengah

Unemployment is still a very crucial problem faced by the Indonesian state. The Central Bureau of Statistics noted that the number of unemployed in Indonesia in 2019 was 4.44 percent, an increase of 2.04 percent in 2020. The purpose of this study was to find out the characteristics of the spatial pattern and modeling the open unemployment rate in Central Java Province. Removing outliers in spatial analysis can change the composition of the spatial effects on the data. One method of solving due to outliers in the spatial regression model is using spatial robust regression. Robust Spatial Durbin Model method which is one of the methods used when spatial outliers occur. The results showed that there are spatial pattern characteristics and modeling for each dependent and independent variable which are different from the independent variables which are mutually correlated. In addition, the RSDM model is a better model than the OLS and SDM models in modeling the Open Unemployment Rate in Central Java Province in 2021 because it has an R2 value of 49.85% and the smallest AIC value of 22,563.

Impact of Hydrological Changes on Wetland Landscape Dynamics and Implications for Ecohydrological Restoration in Honghe National Nature Reserve, Northeast China

Marsh wetlands are significant ecosystems located between land and water bodies which can both protect species diversity and provide habitats. Changes in the hydrological situation of marsh wetlands as a result of climate change and human activities have led to the degradation of wetland landscapes. Taking the Honghe National Nature Reserve (HNNR) in Heilongjiang Province, China, as an example, this paper gathered information on the reserve’s wetland landscape before and after dam construction. The information was obtained using field survey data and a random forest classification method based on Landsat data powered by the Google Earth Engine (GEE) cloud platform. Then, on the basis of the water level data and the digital elevation model, the wetland landscape dynamics of HNNR under three water level conditions were simulated. The findings were as follows: (1) From 1998 to 2008, the area of marsh and meadow had a downward trend, while the area of forest, farmland and water showed a gradual and upward trend; a marked rise in the area of marsh and a continued rise in the area of forest, farmland and water, and a sharp decline in the area of meadow during 2008 to 2018 was observed. (2) There was a significant increase in the area of marsh under the 20 and 40 cm water level simulation scenarios, with a decrease in the number of patches, and an increase in the aggregation index with rising water levels; in contrast, when the water level rose to 60 cm, the area of marsh and the number of patches decreased, but the aggregation index continued to increase. (3) The correlation between wetland landscape and the water level was a nonlinear one. The area of marsh increased and then decreased with increasing water level, reaching a maximum at the 40 cm water level; therefore, 40 cm was the optimal water level regulation scenario. Hydrological processes are the most fundamental ecological processes in marsh wetlands. Understanding the scientific pattern of the spatial pattern characteristics of species as a function of water level environment is important for scientifically guiding the restoration of marsh vegetation.

Study on the Characteristics and Influencing Factors of Land Use Changes in the Metropolitan Fringe Area: The Case of Shenzhen Metropolitan Area in China

With the development of the regional economy, the metropolitan area has gradually shifted from the rapid development stage of concentrating on the central city to the stage of coordinated and integrated regional development. This trend has brought new development opportunities to the metropolitan fringe area; however, due to the differences in resource endowment and the complex relationship between different levels of government, the metropolitan fringe area inevitably has contradictions and imbalances in economic and social development. There has been extensive research on land use and urban governance in the metropolitan area, but less attention has been paid to the metropolitan fringe area, and it is difficult to quantitatively characterize the complex interactions between various forces in this area. This paper summarizes the spatial pattern and spatiotemporal characteristics of construction land use in the fringe area of Shenzhen metropolitan area based on the 30 m resolution land cover dataset from 2000 to 2020, and analyzes the driving factors affecting the changes of construction land use through logistic regression. The results show that the overall land use in the study area is shifting from cropland and forest land to urban and rural construction land. With different stages of development, the rate of land change varies greatly from one period to another. Three factors, population density, lighting index and distance to highway, have a significant correlation with the changes in built-up land across the region, while the boundary effects of administrative boundaries show some variation in the impact of urban land expansion at different economic levels. Finally, we put forward that reducing the negative impact of administrative divisions on the integration of resources in different cities is important for realizing the synergistic development of the Shenzhen metropolitan area.

Spatial distribution characteristics and influencing causes of CE in the YREB1

A variety of measurement methods were used comprehensively in this paper to conduct empirical research on the CE, spatial pattern characteristics and influencing causes of the YREB. Both the total factor CE and the single factor CE were discovered relatively stable. Starting around 2013, the development of the tail cities of the YREB has gradually improved. In addition to Sichuan, Hunan, Chongqing, Hubei and other places where the increase in CE had exceeded that of other cities, the development of CE in Shanghai has also increased significantly. The CE of the YREB had increased with a significant positive spatial autocorrelation and a significant spatial aggregation effect between 2006 and 2017. The spatial spillover effect of CE in the YREB is mainly transmit through factors such as economic growth, energy structure, industrial structure, government intervention, population density, foreign direct investment and the level of marketization, and the spatial interaction between each factor and CE cannot be ignored.

Spatial Pattern Evolution of the Manufacturing Industry in the Yangtze River Economic Belt and Its Impact on PM2.5

Instead of being merely an important embodiment of regional productivity, the manufacturing industry also serves as a significant sector of economic operation and the supply chain system that is highly dependent on resources and the environment. Studying the spatial pattern of the manufacturing industry and its environmental effect is extremely significant for optimizing the spatial layout of urban industry, allocating production factors in a rational manner, and promoting the green transformation of industry. In this regard, this study aimed to further reveal the spatial pattern characteristics of the regional manufacturing industry and its impact on PM2.5. Using data from micro-enterprises in the manufacturing industry in the Yangtze River Economic Belt, its spatial pattern characteristics are explored and an econometric model is constructed to analyze the impact of the manufacturing industry on PM2.5 by comprehensively applying approaches including kernel density estimation, nearest proximity index, and Dagum Gini coefficient decomposition. Three research conclusions were drawn: (1) an obvious “core-edge” feature is present in the spatial distribution of the manufacturing industry in the studied area showing an apparent pattern of “high in the east and low in the west”. The core density of the manufacturing industry in the central cities is significantly higher than that in the surrounding cities. (2) In the manufacturing industry and its subdivisions, the characteristics of spatial agglomeration are unveiled, while the agglomeration and spatial differences are diminished during the study period for the spatial equilibrium of the manufacturing industry. (3) A significantly positive impact is exerted on PM2.5 pollution that is not limited to local cities by the manufacturing industry, which, due to the development differences within the study region, is also heterogeneous. In view of this, policy proposals for aspects such as forging a green manufacturing cluster area, establishing an industrial integration development platform, giving play to regional advantages and technological potential, etc., are put forward in this study, so as to provide a useful reference for optimizing the industrial pattern and promoting the green transformation of industries.

Towards differentiated development policies for mountainous areas: A spatial analysis of mountain distribution and characteristics in a geographic region

AbstractScientifically defining the scope of mountain distribution and understanding the spatial patterns of mountainous areas in China is critical for formulating development policies tailored to the diverse needs of different regions and promoting rural revitalization in mountainous areas. This study aims to scientifically define the scope of mountain distribution and understand the spatial pattern characteristics of mountainous areas in China to aid in the formulation of differentiated development policies and support the rural revitalization strategies of mountainous areas. Using measurements of the areas of mountains and hills in Mainland China, and drawing on European scholars' definitions of mountain cities, a classification system of mountainous areas was constructed based on the proportion differences between hills and mountains within counties. The results showed that hills and mountains were widely distributed across 81.52% and 85.35% of the 2847 total counties in China, respectively. Moreover, 14 provinces accounted for over 90% of the mountainous area, and 5 provinces accounted for 100%. These findings indicate that China's provinces have obvious mountainous characteristics, which need to be considered in policy‐making for regional development.

Spatiotemporal patterns of evapotranspiration in the Xiangjiang River Basin from 2000 to 2020

Abstract Evapotranspiration (ET) is a crucial parameter in the process of the hydrological cycle. It is vital for water resource management in the Xiangjiang River Basin (XRB) within Hunan Province of China to explore the spatial and temporal dynamic characteristics of ET. Based on MOD16, this study revealed the characteristics of spatiotemporal patterns of ET in the XRB from 2000 to 2020. We first applied land use data and change rate for overall trend analysis on ET. Then, we obtained migration routes of ET through standard deviation ellipse (SDE). Furthermore, we demonstrated the effects of monsoon and urban expansion on ET change. The results showed: (1) while the ET of artificial surfaces decreased the change rate in most regions of the XRB was 8.83%, indicating that the overall trend of ET in the XRB was increasing. (2) At 10-year intervals, the SDE center of ET all migrated in a clockwise direction. (3) The correlation between precipitation and ET is more obvious than that between temperature and ET. (4) With the influence of monsoon on precipitation in East Asia, the changes in precipitation are consistent with the ET change.