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Altitude differentiation has a substantial effect on the synergistic control of PM2.5 and O3 pollution. This study targets the Fenwei Plain, which is affected by mountain range blockage, divided into different altitude scales, and employs the methods of correlation analysis and geographical detector to explore the spatiotemporal heterogeneity of PM2.5 and O3 between different altitude zones and to identify the key controlling factors of pollutants between different altitude areas. The results showed that PM2.5 showed a significant decreasing trend from 2014 to 2023, whereas O3 exhibited an opposite trend. The concentrations of both pollutants decreased with increasing altitude, particularly for PM2.5, which showed significant altitudinal differentiation under the influence of topography. PM2.5 was negatively correlated with gross domestic product (GDP) and precipitation, and positively correlated with SO2. In contrast, the correlation of O3 with these factors was opposite to that of PM2.5. For spatial differentiation, NO2 and SO2 were the main factors influencing the spatial differentiation of PM2.5 and O3 at different altitudes. The explanatory power of the spatial divergence of PM2.5 and O3 was greatly increased by the interactions between the two precursors and between the precursors and meteorological factors. Furthermore, the explanatory power of the PM2.5 dominant factor increased with elevation, while the explanatory power of the O3 dominant factor was relatively high across low, middle, and high altitudes. This study serves as a guide for reducing air pollution in the Fenwei Plain and offers a novel perspective for the study of PM2.5 and O3 influenced by terrain. [ABSTRACT FROM AUTHOR]

As one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing the exposure factors and studying the environmental background that affects the incidence can provide the basis for accurate prevention and control and promote the healthy and stable development of society. Based on the county scale, this study determined the high-incidence areas through hot spot analysis and selected nine districts and counties covering meteorological stations and air monitoring stations. The explanatory power of each factor to the incidence of pulmonary tuberculosis was analyzed by geographical detector, and the main influencing factors were explored. The results show that the following: (1) The number and incidence of pulmonary tuberculosis in Gansu Province declined from 2020 to 2022. (2) The influence of meteorological conditions such as temperature, precipitation and air pressure on pulmonary tuberculosis in different regions shows significant regional differences. Although the meteorological influence in adjacent regions shows certain convergence, the change in wind speed has no significant influence on the risk of pulmonary tuberculosis. (3) PM10, altitude, temperature, population density and GDP per capita have strong explanatory power to the incidence of tuberculosis, and the interaction between any two factors exceeds the effect of a single factor in explanatory power, showing the characteristics of two-factor enhancement and nonlinear enhancement. [ABSTRACT FROM AUTHOR]

Virtual tourism network attention is an important reflection of the influence of virtual tourism. Based on the Baidu index of virtual tourism in 31 provinces (cities and districts) in China, this study uses the seasonal concentration index, geographic concentration index, variation coefficient, primacy degree and geographic detector to analyze the spatial-temporal characteristics and influencing factors of network attention of virtual tourism. The results show that : (1) on the whole, the attention of virtual tourism networks shows a steady development trend, reaching a peak in 2016 and the second small peak in 2020. The seasonal variation of virtual tourism network attention is large. March, April, May, June and October are the peak seasons of virtual tourism network search. The public tends to pay more attention to virtual tourism in spring and autumn, and the seasonal and inter-annual differences are further expanding. (2) From the perspective of overall spatial evolution, the spatial evolution pattern of attention in virtual tourism networks is relatively stable, presenting an obvious distribution characteristic of "Hu Line". From the regional point of view, the attention of virtual tourism networks shows a trend of decreasing from east to middle to west. The spatial distribution difference of attention in virtual tourism networks is the largest in the western region, and the eastern and central regions are relatively balanced. The difference has a narrowing trend in the eastern region, and a further widening trend in the central and western regions. (3) The level of virtual tourism network attention is influenced by the level of economic development, population size, education development, tourism development and network development. Among them, the level of tourism development has a certain explanatory power to the difference of attention, but it is not a strong factor. In addition, policy support, typical virtual tourism projects and hot events will also affect the attention of the virtual tourism network. [ABSTRACT FROM AUTHOR]

Altitude differentiation has a substantial effect on the synergistic control of PM2.5 and O3 pollution. This study targets the Fenwei Plain, which is affected by mountain range blockage, divided into different altitude scales, and employs the methods of correlation analysis and geographical detector to explore the spatiotemporal heterogeneity of PM2.5 and O3 between different altitude zones and to identify the key controlling factors of pollutants between different altitude areas. The results showed that PM2.5 showed a significant decreasing trend from 2014 to 2023, whereas O3 exhibited an opposite trend. The concentrations of both pollutants decreased with increasing altitude, particularly for PM2.5, which showed significant altitudinal differentiation under the influence of topography. PM2.5 was negatively correlated with gross domestic product (GDP) and precipitation, and positively correlated with SO2. In contrast, the correlation of O3 with these factors was opposite to that of PM2.5. For spatial differentiation, NO2 and SO2 were the main factors influencing the spatial differentiation of PM2.5 and O3 at different altitudes. The explanatory power of the spatial divergence of PM2.5 and O3 was greatly increased by the interactions between the two precursors and between the precursors and meteorological factors. Furthermore, the explanatory power of the PM2.5 dominant factor increased with elevation, while the explanatory power of the O3 dominant factor was relatively high across low, middle, and high altitudes. This study serves as a guide for reducing air pollution in the Fenwei Plain and offers a novel perspective for the study of PM2.5 and O3 influenced by terrain.

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of "land use carbon emissions-spatial and temporal patterns-influencing factors," and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 × 104tC in 2010-23597.68 × 104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of "northwest--southeast." The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions. [ABSTRACT FROM AUTHOR]

The relationship between land use changes and regional carbon storage is closely linked. Identifying evolving trends concerning and influencing factors on carbon storage under future scenarios is key in order to achieve the "dual carbon" goals. Using Chongqing as a case study, this study integrated the advantages of the PLUS model, InVEST model, and a geographic detector model. It conducted simulations of land use type data under scenarios of natural development (ND) and ecological protection (EP), and identified evolving trends and influencing factors regarding carbon storage. The results were as follows: (1) the PLUS model demonstrated excellent simulation performance, with a Kappa coefficient above 0.85 and an overall accuracy above 0.90. During the study period, significant changes occurred for cultivated land, forested land, water bodies, and construction, which were closely related to carbon storage; (2) carbon storage in Chongqing showed a decreasing trend, with a decrease of 10.07 × 106 t C from 2000 to 2020. Under the ND scenario, carbon storage was projected to decrease by 10.54 × 106 t C in 2030 compared to 2020, and it was expected to stabilize from 2030 to 2050. At the county level, Youyang, Fengjie, and Wuxi had the highest carbon storage, while Nanchuan, Jiangbei, and Dadukou had the lowest; (3) the spatial distribution of carbon storage presented an "eastern hotspot western cold spot aggregation" pattern. The proportions of regions with a decreased, unchanged, and increased aggregation of carbon storage in Chongqing during 2000–2010 and 2010–2020 were 2.99%, 95.95%, 1.06%; and 4.39%, 92.40%, 3.21%, respectively. The trend indicated a decrease in the aggregation of carbon storage, and future carbon storage was expected to stabilize; (4) elevation, terrain fluctuation, NDVI, annual average temperature, annual average precipitation, and nighttime light index had influence values of 0.88, 0.81, 0.61, 0.86, 0.77, and 0.81 on carbon storage, respectively, with different combinations of influencing factors having a greater impact. In the future, ecological priority and green development concepts should be followed, and comprehensive improvement of regional development conditions should be pursued to enhance carbon storage, thereby promoting the achievement of the "dual carbon" goals. This study provided an analytical path and data support for formulating optimized carbon storage policies at the regional level. [ABSTRACT FROM AUTHOR]

Freeze–thaw erosion is one of the three major soil erosions in China, including wind erosion and hydraulic erosion, which leads to the destruction of the natural environment, the imbalance of economic development, a threat to personal safety, and irreversible disaster to the country and people. China's permafrost area accounts for about one-fifth of the country's land area, and the seasonal permafrost area accounts for half of China's land area, mainly concentrated in the Qinghai–Tibet Plateau, Xinjiang Province, Heilongjiang Province, Gansu Province, and other regions. In order to establish an evaluation system for the ecological function reserve of the Greater Hinggan Mountains, nine evaluation indicators were selected from the perspectives of topography, climate, soil, and vegetation conditions. GIS technology, a multiple collinearity test, and principal component analysis were used to comprehensively evaluate the freeze–thaw erosion in the ecological function reserve of the Greater Hinggan Mountains. This study categorized the evaluation results into five intensity levels, from micro to severe. Finally, the degree of influence of different influencing factors on freeze–thaw erosion and the interactions between the factors were analyzed using a geographic detector. The results showed the following: (1) The intensity of freeze–thaw erosion in the study area gradually increased from west to east, and the comprehensive evaluation results were −0.2552 to 0.7581. Overall, moderate, severe, and mild erosion accounted for 29.83%, 25.9%, and 21.54% of the total area of the freeze–thaw zone, respectively. (2) Soil moisture content and the annual range in temperature were the main factors contributing to freeze–thaw erosion. The degree of influence of the two effects on freeze–thaw erosion (q = 0.5997) was better than that of the single-factor effect. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[ObjectiveH Landscape ecological risk is an effective indicator to identify regional ecological risks and measure regional ecological security. Based on the construction of impervious surface, the environmental response of human activities to the formation and exacerbation of risks can be intuitively determined. Regional landscape ecological risk assessment and the influencing factors of the aggravated risk areas are analyzed, which has scientific significance and practical value for curbing the emerging and spreading of risk exacerbation zones. Methods] Nanchang City was taken as an example, the information of impervious surface was obtained to construct a landscape ecological risk index. The spatiotemporal evolution characteristics of landscape ecological risk were analyzed. The influencing factors of landscape ecological risk in the study area was measured by using geographic detectors. --Results] (1) From 201 0 to 2020, the area of impervious surface in Nanchang City increased dramatically, from 648.63 km' to 1 ()75.12, and the resources of agricultural land and ecological land continued to decline. The impervious surface was mainly converted from landscapes such as cultivated land, forest land, and water areas, and the degree of interference from human activities increased. (2) From 201 0 to 2020, the spatial distribution of Nanchang's landscape ecological risk index was presented as low in the middle and high on both sides. The average value of landscape pattern index decreased as a whole. Tn 2()1(): 2015, and 2020, the areas of high-risk areas of landscape ecology in Nan ch ang City were 519.73, 324.82 km2, and 365.04 km2, respectively. The area of landscape ecological high-risk areas first decreased and then increased. The areas were dominated by landscape ecological medium-high-risk areas and medium-risk areas. (3) The influencing factors of landscape ecological risk in Nan ch ang City in 2010, 2015 and 2()2() were dominated by socio-economic factors and distance factors. The interaction effects of the influencing factors were all larger than the explanation degree of a single factor. The distance factor had a positive effect on natural climate factors and socioeconomic factors. (4) The landscape ecological risk exacerbation zone in Nan ch ang City was dominated by landscape types such as forest land, water area and cultivated land. Exacerbation zones were caused by multiple factors such as natural environment, socioeconomics, and accessibility, but the impact of human activities was more prominent. Conclusion] The landscape ecological status of Nanchang City has been improving. The impacts of natural environment, social economy, distance and other factors on the landscape ecological risk index and risk exacerbation areas are different and interactive. These research results are of great significance for maintaining regional ecological security and sustainable development. [ABSTRACT FROM AUTHOR]

The relationship between land use changes and regional carbon storage is closely linked. Identifying evolving trends concerning and influencing factors on carbon storage under future scenarios is key in order to achieve the “dual carbon” goals. Using Chongqing as a case study, this study integrated the advantages of the PLUS model, InVEST model, and a geographic detector model. It conducted simulations of land use type data under scenarios of natural development (ND) and ecological protection (EP), and identified evolving trends and influencing factors regarding carbon storage. The results were as follows: (1) the PLUS model demonstrated excellent simulation performance, with a Kappa coefficient above 0.85 and an overall accuracy above 0.90. During the study period, significant changes occurred for cultivated land, forested land, water bodies, and construction, which were closely related to carbon storage; (2) carbon storage in Chongqing showed a decreasing trend, with a decrease of 10.07 × 106 t C from 2000 to 2020. Under the ND scenario, carbon storage was projected to decrease by 10.54 × 106 t C in 2030 compared to 2020, and it was expected to stabilize from 2030 to 2050. At the county level, Youyang, Fengjie, and Wuxi had the highest carbon storage, while Nanchuan, Jiangbei, and Dadukou had the lowest; (3) the spatial distribution of carbon storage presented an “eastern hotspot western cold spot aggregation” pattern. The proportions of regions with a decreased, unchanged, and increased aggregation of carbon storage in Chongqing during 2000–2010 and 2010–2020 were 2.99%, 95.95%, 1.06%; and 4.39%, 92.40%, 3.21%, respectively. The trend indicated a decrease in the aggregation of carbon storage, and future carbon storage was expected to stabilize; (4) elevation, terrain fluctuation, NDVI, annual average temperature, annual average precipitation, and nighttime light index had influence values of 0.88, 0.81, 0.61, 0.86, 0.77, and 0.81 on carbon storage, respectively, with different combinations of influencing factors having a greater impact. In the future, ecological priority and green development concepts should be followed, and comprehensive improvement of regional development conditions should be pursued to enhance carbon storage, thereby promoting the achievement of the “dual carbon” goals. This study provided an analytical path and data support for formulating optimized carbon storage policies at the regional level.

BACKGROUND Selenium is one of the essential trace elements for humans. Eating agricultural products containing selenium is an important way for humans to obtain selenium. It is of great significance for the utilization of selenium-enriched soil resources and the development of selenium-enriched agricultural products to investigate the content characteristics of soil selenium, delimit selenium-enriched soil areas, and determine the influencing factors of soil selenium spatial distribution, migration and enrichment. OBJECTIVES To discuss the content, distribution characteristics and influencing factors of soil selenium in the study area. METHODS Tangchang Town in the Pidu District of Chengdu City was chosen as the study area. 788 surface soil samples (0-20cm) and 46 profile soil samples (100-150cm) were collected. The pH values and contents of Se, As, Hg, Cu, Pb, Zn, Ni, Cr, CD, TP, TK, TN and TOC in soil were determined by AFS, XRF and ICP-MS/OES. Geostatistical analysis, variance analysis, correlation analysis and geographic detectors were applied to systematically determine the content, distribution characteristics and influencing factors of soil selenium. RESULTS (1) Results showed that surface soil selenium content of Tangchang Town ranged from 0.082 to 1.10mg/kg, with an arithmetic mean value of 0.337mg/kg, which was 1.47, 4.02 and 1.16 times the average Se content of soil in Chengdu, Sichuan and China, respectively. (2) The Se-sufficient and Se-enriched topsoil reached 99.2% of Tangchang Town. In the profile soil samples, the soil selenium content decreased with increasing depth. In the spatial distribution, high selenium concentrate areas generally showed a northwest zonal distribution trend in Zhanqi Village, Baimu Village, Dayun Village, and Zhulin Village. (3) Correlation analysis showed that lead, chromium, nickel, cadmium, mercury, arsenic, total potassium, total nitrogen, pH, TOC and altitude were factors affecting spatial distribution variability of the soil selenium but varied in extent (P < 0.01). (4) The factor detection showed that TOC was the highest in the effect, explaining 0.29 of the variability, followed by Zn, which was 0.18, and the third was Cd, which was 0.16. Interaction detection showed the interpretation degree of the interaction between soil TOC and 17 influencing factors was more than 0.30, which was the dominant factor of spatial differentiation of selenium in topsoil. CONCLUSIONS The town is characterized by selenium-enriched soil resources; it is suggested to organically combine the selenium-enriched area with the characteristic industries of chives in Tangyuan to build an ecological chain of selenium-enriched industry, and further carry out collaborative analysis on the influencing factors of selenium migration and enrichment in soil crops.

It is of great significance to carry out research on the evolution of urban resilience and the detection of influencing factors to promote urban safety management and guide urban planning. This paper constructs urban resilience evaluation indicators based on Scale-Density-Morphology, adopts the “polyhedron method” to evaluate the urban resilience of the Chengdu-Deyang-Mianyang Economic Belt from 2010 to 2020, analyzes the temporal and spatial changes in urban resilience, and explores the impact of natural, social, economic and other factors on urban resilience, to provide a basis for regional sustainable development. The results show that 1) The overall level of urban resilience is not high, showing a clear downward trend and imbalance. Urban resilience decreased from 0.296 in 2010 to 0.213 in 2020. The proportion of districts and counties with high urban resilience is relatively small and continues to decline. The differences in urban resilience in the region are significant and gradually increasing, and there is an obvious imbalance in urban resilience. 2) Urban resilience presents the spatial distribution characteristics of continuous expansion of low-resilience and lower-resilience urban, and shrinkage of the distribution of high-resilience and higher-resilience urban. The types of evolution are mainly manifested as rapid decline and fluctuation decline. 3) The influencing factors affecting the spatial differentiation of urban resilience are different. Ecological factors represented by per capita ecological land area have gradually become the primary factor. The influence of factor interactions is greater than that of individual factors, including both non-linear enhancement and two-factor enhancement.

Based on the POI data of logistics enterprises in Xinjiang in 2012, 2016, and 2020, the ArcGIS spatial analysis technique, geographic detector, and other methods were used for the quantitative analysis of the spatial and temporal distributions of logistics enterprises in Xinjiang during 2012–2020 and the influencing factors. The following findings were obtained in the present study: (1) there was a significant difference in the distributions of logistics enterprises in Xinjiang at different development stages, with unbalance among areas; further, there was a higher number of logistics enterprises in Northern Xinjiang compared with Southern Xinjiang; (2) the spatial distribution of logistics enterprises in Xinjiang was generally characterized by a "northeast–southwest" trend; there was a periodic shift in the distribution center from northeast to southwest; the distribution center remained in Bayingolin Mongol Autonomous Prefecture in 2012 and 2020, and shifted to Changji Hui Autonomous Prefecture in 2016, close to the junction of the two areas; (3) the agglomeration of logistics enterprises in Xinjiang was positively correlated with the scale; the kernel density analysis results revealed that there was obvious spatial differentiation characterized by "multi-center development with core agglomeration and patch distribution at the edge", and the hotspot areas of logistics enterprises were distributed in major cities, with small variations; the Tianshan Mountain North Slope Economic Belt was the main agglomeration area of logistics enterprises; (4) the results from the geographic detector show that the regional GDP, regional total retail sales of consumer goods, regional utilization of foreign direct investment, and regional fixed assets investment were factors that influenced the spatial distribution of logistics enterprises in Xinjiang, thereby significantly promoting the stable and rapid development of logistics enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rock & Mineral Analysis is the property of Editorial Board of Rock & Mineral Analysis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the spatial distribution characteristics and influencing factors of modern agricultural parks is an important prerequisite for identifying the development center of modern agricultural parks and promoting regional agricultural modernization. Our research takes Guangxi's modern characteristic agricultural demonstration area as an example. Based on the data of 282 autonomous regionlevel demonstration areas from 2014 to 2019, we use geographic information system (GIS) to analyze its spatial distribution characteristics and explore the factors affecting the distribution of the park through geographic detectors. The results show that: (1) The spatial distribution type of the demonstration area is agglomerated type, but the degree of agglomeration is average, and the distribution is uneven at the city scale, showing a spatial pattern of "small agglomeration and large dispersion"; (2) the demonstration area The distribution is mainly affected by multiple factors such as regional economic development, agricultural foundation, consumption level, technical level, infrastructure, etc., and economic development has the strongest force; (3) The distribution of demonstration areas is more inclined to rely on the comprehensive advantages of the region. Factors related to regional agricultural foundation, economic development, and consumption level have a positive interaction on its distribution. [ABSTRACT FROM AUTHOR]

Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor.

Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China's sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China's sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of "depending on the city, near the scenery" and "large concentration, small dispersion". (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people's living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor. [ABSTRACT FROM AUTHOR]