Search

Due to advancements in information technology and growing eco-tourism demand, National Park Network Attention (NPNA) has emerged as a novel indicator of tourism appeal and ecological value recognition. Utilizing Baidu search index (accessed in 2023) data from 2013 to 2022, this study employs time series analysis, index analysis, and spatial statistics to measure and differentiate the spatial and temporal aspects of NPNA across 31 provinces, regions, and municipalities in mainland China, while systematically assessing the impact of various factors from both source and destination perspectives. Over the period of 2013 to 2022, NPNA has increased annually, peaking around holidays and during spring and autumn, demonstrating pronounced seasonality and precursor effects, while exhibiting volatility due to external events. Influenced by factors from both source and destination perspectives, the spatial distribution of NPNA displays a trend of being "high in the east and low in the west" and "high in the south and low in the north", though regional disparities are diminishing. The population size in the source areas remains the dominant factor influencing NPNA, while the concept of national parks is not yet widely recognized. The destination's tourism resource endowment, media publicity, accessibility, and level of informatization are significant influences. An effective integration of resources and marketing is essential for boosting NPNA. The findings provide valuable insights for optimizing the spatial layout of national parks, enhancing the tourism service system, innovating communication and promotional strategies, and improving national park governance effectiveness. [ABSTRACT FROM AUTHOR]

An increasing number of people are gathering travel information online prior to their trips as a result of the Internet's rapid expansion. The amount of network attention receives can indicate how many people are looking for something. The orderly development of Chinese resort hotels can be guided by research on the spatiotemporal characteristics of their network attention. Using Chinese resort hotels as the study subject, everyday information about resort hotels in 31 Chinese provinces was gathered via the Baidu Index platform between 2018 and 2022, and mathematical statistics and other methods were used to study the spatiotemporal distribution characteristics and influence of Chinese resort hotels network attention. Findings reveal that, from 2018 to 2022, network attention to resort hotels across the country fluctuated significantly across seasons, and there was a “precursor effect” reaction before the week of network attention. Moreover, the spatial distribution of network attention to Chinese resort hotels was uneven, showing an overall trend of “east–central–west” decline. Level of economic development, degree of network development, leisure time, and population size are the main factors affecting the spatiotemporal distribution of Chinese resort hotels network attention.

The tourism network attention as a reflection of tourism demand is closely related to the tourism flow, the differences between the two has become an important criterion for judging the efficiency of destination tourism demand conversion, as well as a manifestation of the balance and coordination of destination tourism industry. Against the background of insufficient release of tourism demand in China, research on the development differences between tourism network attention and tourism flow can provide a basis for demand-side management and high-quality development. Based on the theory of spatial mismatch, this research analyzes the spatial development difference between the tourism network attention and the tourism flow in Shanghai from 2012 to 2021 using methods such as center of gravity model, spatial mismatch index, and two-dimensional combination matrix. The results show: (1) According to the analysis of the center of gravity model, there was a shift of the center of gravity of tourism network attention with the direction of ''south-north'', while the tourism flow shifted ''west-east''; the center of gravity between tourism network attention and tourism flow began to diverge from 2012 to 2016, gradually converged from 2016 to 2019, and then gradually deviated again after 2020. (2) According to the spatial mismatch index, the spatial mismatch types between tourism network attention and tourism flow in various Districts of Shanghai are mainly negative and low mismatch, with high mismatch areas mainly distributed in the eastern and southwestern parts of Shanghai. (3) Combining the two-dimensional combination matrix, it can be observed that the spatial development difference between tourism network attention and tourism flow in Shanghai show a characteristic of ''enlarging-shrinking-enlarging''. From 2012 to 2016, the spatial development difference between tourism network attention and tourism flow in Shanghai continuously expanded; from 2017 to 2019, the spatial development difference continuously shrank; and from 2020 to 2021, the spatial differences expanded again. (4) The analysis results of the panel data model show that the development of tourism resources and the level of tourism services have a positive promoting effect on the evolution of spatial mismatch, while the social basic development environment has a negative effect. The research results not only meet the needs of evaluating the high-quality development of the tourism industry in the current economic restructuring, providing direction for the high-quality development of the regional tourism industry, but also enrich the research content of network attention as a tourism element participating in the evaluation of tourism industry development quality, and deepen the relationship research between network attention and tourism flow.

【Objective/Meaning】Under the background of the state vigorous promoting the construction of ecological civilization and the implementation of afforestation action in Guangdong Province, the temporal and spatial evolution law of forest tourism in Guangdong Province were explored in this paper, in order to provide references for the prediction of forest tourism market demand and publicity and marketing.【Methods/Procedures】With the help of Baidu index, the spatial and temporal characteristics and influencing factors of the network attention of 31 provinces in China to Guangdong's forest tourism were analyzed by using the mathematical models and econometric tools.【 Results/Conclusions】 The results showed that: In terms of time, the network attention generally showed an “S” type fluctuation, which was obvious in the off and peak seasons, and in summer > autumn > spring > winter. The precursor effect of golden week holiday was significant but had a decreasing trend. At the spatial level, the tourist market has presented the development trend of “dual core in Guangdong Province and Jiangsu Province, decreasing to the surrounding provinces” and “secondary market in the majority, tending to be balanced”. Through the grey correlation analysis, it was found that the economic development level, educational level, degree of traffic convenience, Internet development level and festival activities were the main factors affecting the network attention [ABSTRACT FROM AUTHOR]

Based on the geo-spatial distribution and rich social media data of many important scenic tourist places (high-level scenic spots in China), this study presents a quantitative analysis using GIS technology and several spatial statistical tools to examine the geographical distribution and network attention of these spots. It is found that there is a clear geographical imbalance in the spatial distribution of these scenic spots in China, primarily concentrated in the lower-lying and densely populated eastern regions. Using spatial autocorrelation methods to assess the degree of match between these two spatial patterns, it is observed that the spatial network attention and geographical distribution of hotspots are mutually correlated only in major coastal cities. The results enhance our understanding of effective tourism network marketing instruments and provide further insight into the geographical layout of scenic spots in the country.

Due to advancements in information technology and growing eco-tourism demand, National Park Network Attention (NPNA) has emerged as a novel indicator of tourism appeal and ecological value recognition. Utilizing Baidu search index (accessed in 2023) data from 2013 to 2022, this study employs time series analysis, index analysis, and spatial statistics to measure and differentiate the spatial and temporal aspects of NPNA across 31 provinces, regions, and municipalities in mainland China, while systematically assessing the impact of various factors from both source and destination perspectives. Over the period of 2013 to 2022, NPNA has increased annually, peaking around holidays and during spring and autumn, demonstrating pronounced seasonality and precursor effects, while exhibiting volatility due to external events. Influenced by factors from both source and destination perspectives, the spatial distribution of NPNA displays a trend of being “high in the east and low in the west” and “high in the south and low in the north”, though regional disparities are diminishing. The population size in the source areas remains the dominant factor influencing NPNA, while the concept of national parks is not yet widely recognized. The destination’s tourism resource endowment, media publicity, accessibility, and level of informatization are significant influences. An effective integration of resources and marketing is essential for boosting NPNA. The findings provide valuable insights for optimizing the spatial layout of national parks, enhancing the tourism service system, innovating communication and promotional strategies, and improving national park governance effectiveness.

Urban tourism network attention is important for measuring the competitiveness of the urban tourism industry, tourism attraction, and cultural soft power. In this study, we explored the spatiotemporal patterns and factors influencing network attention in the tourist source market and discussed how tourism cities can increase network attention, thus improving the competitiveness of urban cyberspace and developing soft power. Taking Chengdu as a research case, we obtained data on its tourism network attention from 31 provinces (autonomous regions and municipalities) between 2011 and 2021. We measured the spatiotemporal characteristics of network attention using the inter-annual change index, seasonal concentration index, potential tourists' concentration coefficient, and ESDA model and analyzed the factors affecting spatiotemporal changes in network attention using the geographic weighted regression (GWR) model. The results revealed that from 2011 to 2021, the network attention of Chengdu tourism showed an overall "M"-type fluctuation trend, with significant seasonal differences and disequilibrium and significant differences in space, signifying an overall "∩"-shaped distribution trend. This suggested a weak negative spatial correlation. Further, the number of mobile Internet users, people in higher education per 100,000 people, per capita gross domestic product, urbanization rate, and passenger throughput are important factors that affect the network attention of Chengdu tourism. Thus, these results can be used by cities in western China to optimize the network attention rating system of urban tourism, strengthen the promotion of urban image, build a sustainable city, and transform network traffic into effective economic growth. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science 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.)

In order to promote the sustainable development of Changdao Island tourism, this paper collected the data of Changdao Island tourism network attention from 2013 to 2019 based on Baidu Index, analyzed the temporal and spatial evolution characteristics and influencing factors of Changdao Island tourism network attention by using seasonal concentration index and geographical concentration index, and put forward some suggestions. The results showed that the overall attention of Changdao Island tourism network was high and showed an upward trend, with large seasonal differences, which was basically consistent with the trend of the actual passenger flow of Changdao Island tourism, and there would be abnormal peaks due to special events and other factors. The regional distribution of Changdao Island tourism network attention was uneven, mainly distributed in nearby cities, economically developed cities and inland cities, but it had decentralized development potential. Changdao Island should vigorously develop tourism and increase publicity, maintain online attention, expand influence and improve popularity.

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.)

Based on the network attention data of China’s agricultural listed companies from 2012 to 2020, this paper uses the IV model to measure and investigate the impact of network attention on the carbon dioxide emission performance of China’s agricultural listed companies and its mechanism. The findings are as follows: 1) The carbon dioxide emission intensity of listed agricultural companies in China is generally decreasing year by year and the carbon dioxide emission performance is improving; 2) The increasing network attention has significantly reduced the carbon dioxide emission intensity of agricultural listed companies and brought about better carbon dioxide emission performance; 3) The relationship between network attention and carbon dioxide emission performance of agricultural listed companies has network, regional and property heterogeneity; 4) The investment in environmental protection has strengthened the inhibition effect of network attention on the unit carbon dioxide emissions of agricultural listed companies. The research conclusion enriches the literature on “network concern - environmental governance”, and also provides ideas for developing countries to exert the environmental governance effect of network concern in the process of carbon neutrality.

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University 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.)

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance. [ABSTRACT FROM AUTHOR]

An increasing number of people are gathering travel information online prior to their trips as a result of the Internet's rapid expansion. The amount of network attention receives can indicate how many people are looking for something. The orderly development of Chinese resort hotels can be guided by research on the spatiotemporal characteristics of their network attention. Using Chinese resort hotels as the study subject, everyday information about resort hotels in 31 Chinese provinces was gathered via the Baidu Index platform between 2018 and 2022, and mathematical statistics and other methods were used to study the spatiotemporal distribution characteristics and influence of Chinese resort hotels network attention. Findings reveal that, from 2018 to 2022, network attention to resort hotels across the country fluctuated significantly across seasons, and there was a "precursor effect" reaction before the week of network attention. Moreover, the spatial distribution of network attention to Chinese resort hotels was uneven, showing an overall trend of "east-central-west" decline. Level of economic development, degree of network development, leisure time, and population size are the main factors affecting the spatiotemporal distribution of Chinese resort hotels network attention., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In this paper, Baidu Index is used to obtain the relevant data of network attention to Sanhe ancient town from 2015 to 2019. Combined with weekly distribution skewness index and geographic concentration index, the spatial and temporal distribution characteristics of network attention are measured, and the regression analysis method is used to study the spatial and temporal distribution characteristics of network attention to Sanhe ancient town. This research can be concluded as below: the network attention to Sanhe ancient town shows a downward trend in recent years; the annual distribution of network attention presents the feature of “double-peaks”; during the national day holiday, the network attention to Sanhe ancient town becomes more and more concentrated, showing an inverted “V” shape; although the network attention to Sanhe ancient town still shows the characteristics of centralized distribution in space, the concentration degree is gradually decreasing; climate comfort, national vacation system, festival activities and other social factors have a significant impact on the time distribution of network attention; and economic connection, economic development and spatial distance have a significant impact on the spatial distribution of network attention to Sanhe ancient town. [ABSTRACT FROM AUTHOR]

In recent years, network attention based on the effect of Internet traffic has played an increasingly obvious role in promoting the development of enterprises. This paper matches the data of China's A-share listed companies from 2011 to 2019 with the Internet search index, and empirically analyzes the impact of network attention on enterprise growth from the perspective of enterprise life cycle. The research results show that the increase in network attention can significantly promote the growth of enterprises. However, from the perspective of enterprise life cycle, the effect of Internet traffic on the promotion of enterprise growth is only significant in the growth period. At the same time, the effect of network attention on the growth of enterprises shows heterogeneous effects by enterprise types, geographical characteristics and industries. Further research shows that alleviating early financing constraints and reducing corporate defaults are intermediary channels through which network attention affects corporate growth. Enterprises should actively integrate into the business model bred by the Internet and promote corporate growth with the help of network attention spillover effects. [ABSTRACT FROM AUTHOR]

The tourism network attention as a reflection of tourism demand is closely related to the tourism flow, the differences between the two has become an important criterion for judging the efficiency of destination tourism demand conversion, as well as a manifestation of the balance and coordination of destination tourism industry. Against the background of insufficient release of tourism demand in China, research on the development differences between tourism network attention and tourism flow can provide a basis for demand-side management and high-quality development. Based on the theory of spatial mismatch, this research analyzes the spatial development difference between the tourism network attention and the tourism flow in Shanghai from 2012 to 2021 using methods such as center of gravity model, spatial mismatch index, and two-dimensional combination matrix. The results show: (1) According to the analysis of the center of gravity model, there was a shift of the center of gravity of tourism network attention with the direction of "south-north", while the tourism flow shifted "west-east"; the center of gravity between tourism network attention and tourism flow began to diverge from 2012 to 2016, gradually converged from 2016 to 2019, and then gradually deviated again after 2020. (2) According to the spatial mismatch index, the spatial mismatch types between tourism network attention and tourism flow in various Districts of Shanghai are mainly negative and low mismatch, with high mismatch areas mainly distributed in the eastern and southwestern parts of Shanghai. (3) Combining the two-dimensional combination matrix, it can be observed that the spatial development difference between tourism network attention and tourism flow in Shanghai show a characteristic of "enlarging-shrinking-enlarging". From 2012 to 2016, the spatial development difference between tourism network attention and tourism flow in Shanghai continuously expanded; from 2017 to 2019, the spatial development difference continuously shrank; and from 2020 to 2021, the spatial differences expanded again. (4) The analysis results of the panel data model show that the development of tourism resources and the level of tourism services have a positive promoting effect on the evolution of spatial mismatch, while the social basic development environment has a negative effect. The research results not only meet the needs of evaluating the high-quality development of the tourism industry in the current economic restructuring, providing direction for the high-quality development of the regional tourism industry, but also enrich the research content of network attention as a tourism element participating in the evaluation of tourism industry development quality, and deepen the relationship research between network attention and tourism flow., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

With only coarse labels, weakly supervised learning typically uses top-down attention maps generated by back-propagating gradients as priors for tasks such as object localization and semantic segmentation. While these attention maps are intuitive and informative explanations of deep neural network, there is no effective mechanism to manipulate the network attention during learning process. In this paper, we address three shortcomings of previous approaches in modeling such attention maps in one common framework. First, we make attention maps a natural and explicit component in the training pipeline such that they are end-to-end trainable. Moreover, we provide self-guidance directly on these maps by exploring supervision from the network itself to improve them towards specific target tasks. Lastly, we proposed a design to seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing convolutional neural networks to improve their generalization performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Physical Education Institute / Shanghai Tiyu Xueyuan Xuebao is the property of Shanghai Physical Education Institute 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.)

Red tourism network attention degree is a typical method for measuring the performance level of red tourism development promotion, and it is also an important reflection of the influence of red tourism promotion level. This paper takes the "red tourism network attention degree" of 31 provincial- level regions as the research object, and uses the Moran index and panel vector autoregressive model to analyze the spatio-temporal characteristics and the influencing factors of the red tourism network in China from 2011 to 2018. The results show: (1) The network attention of the national red tourism shows a fluctuant growth trend in the study period, and its seasonal difference is significant. (2) The national red tourism network attention shows a decreasing trend from the eastern to central and western regions, but the trend of attention in the red tourism 5A-level tourist attractions is prominent in the western region. (3) The attention of red tourism networks in the 31 provincial- level regions has significant global spatial autocorrelation. The phenomenon of "high-high" and "low-low" agglomerations is concentrated in the eastern region, and central and western region. (4) The contribution of the factors affecting red tourism network attention was ranked as follows: internet penetration rate > per capita GDP > tourism information index > regional media attention > red tourism classic scenic network attention. [ABSTRACT FROM AUTHOR]

The temporal evolution and spatial distribution characteristics of network attention of Tengwang Pavilion, a 5A tourist attraction in Nanchang City from 2011 to 2019 were analyzed by using Baidu index search platform. The results showed that network attention of Tengwang Pavilion in China was increasing year by year, but the annual growth rate was different. There were two peak periods of network attention in a year. They were in April and October respectively. From a weekly point of view, the network attention of Tengwang Pavilion was the lowest on Friday, the highest on Saturdays, and higher on Saturdays and Sundays than on weekdays. From the point of view of geographical distribution, the province that paid the most attention to Tengwang Pavilion on network was Jiangxi Province and the largest city was Nanchang. Tengwang Pavilion scenic spot should pay more attention to the network attention and distribution characteristics of tourists, grasp the potential needs to better guide the development and marketing of tourism products, ensure the safety of tourist attractions, and promote the sustainable development of scenic spots. [ABSTRACT FROM AUTHOR]

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University 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.)

Based on the data of climate and Baidu Index, the temporal and spatial variation of climate comfort and tourists' network attention in Inner Mongolia was analyzed, and the effect of climate comfort on tourists' network attention. The results showed that: ① Inner Mongolia had a summer-comfortable tourism climate, and it was uncomfortable to visit Inner Mongolia in winter. With the decrease of latitude, the climate comfort index gradually rose in Inner Mongolia, with a distribution pattern of "low in the east and high in the west". There were three types of distribution of the climate comfort index: M-shaped, inverted U-shaped, and inverted V-shaped. ② Tourists' network attention had certain dependence on the development level of tourism in various regions. The degree of network attention of regions with a high level of tourism development was also relatively high, and its distribution was more uniform. Monthly indexes of the tourists' network attention had three types: M-shaped, inverted U-shaped, and inverted V-shaped. ③ On the whole, climate comfort had a positive impact on the degree of network attention, but with the improvement of the level of tourism development, the impact of climate comfort on the degree of attention of visitors would be weakened. ④ The impact of climate comfort on the tourists' network attention was not significant in Alxa League. [ABSTRACT FROM AUTHOR]

Traditional villages are the carriers of tangible cultural heritage and intangible cultural heritage, as well as China's non-renewable cultural resources and valuable tourism resources. With Sina travel blogs as the data platform, taking the number of page views as an evaluation indicator of network attention, the dynamics of network attention to the 91 traditional villages in Hunan Province that have been included in the first three batches of Chinese Traditional Village List were analyzed from two aspects of time and space. Based on this, development strategies were put forward for tourism of Chinese traditional villages so as to promote the development of the tourism economy in Hunan Province. [ABSTRACT FROM AUTHOR]

The Internet has become a platform for tourism management agencies to pass tourism information, promote destination and carry out travel consultation, and a transaction interface for online travel services. In the process of tourists retrieving travel information, tourism security information is one of the important elements that need to be focused on, and even play a decisive role for the entire decision-making. The network attention to tourism security in general has been on the upswing from 2009 to 2013. The network attention to tourism safety refers to the network users' or residents' (potential tourists') concerns about tourism safety index, it is a typical performance and measurements of concerning about the tourism security situation by the internet, as well as a reflection of the tourism security consciousness level. Based on Baidu index, the network attention data of tourism security of the provinces, autonomous regions and municipalities directly under the central government in China from 2009 to 2013 were retrieved, using coefficient of variation coefficient (VC), Herfindahl index (H), primacy index (P), geographic concentration index (G) and the seasonal concentration index (S), in order to conduct a comparative analysis of the spatial and temporal characteristics of the network attention to tourism security. The analysis found that: (1) The national network attention to tourism security in general shows a growing trend in 2009-2013, but from March to June, and from August to December, there are obvious fluctuation and significant differences. Among them, the highest of the network attention to tourism security is in April, while network attention to tourism security in May, September, October, and December is also very high. From the monthly fluctuation, it is found that every year the network attention to security in April is higher than that in May, September being higher than October, and December higher than January, indicating that network attention to tourism security has obvious "precursor effect" . This study also found that there are seasonal variations in the annual network attention to tourism security, especially when it is close to the Golden Week. The higher the network attention to tourism security, the more obvious of the time difference between the concentrations. (2) The national network attention to tourism security has significant differences across regions. A decreasing trend runs from East China, to Central and West China. The time concentration indices are higher than those from 2009 to 2013, in which seasonal differences are highlighted. In the regions/ areas of network attention to tourism security, Guangdong, Shandong, Beijing, Zhejiang, Fujian, Jiangsu, Henan, and Hebei represent a sequence characteristic of the regional network attention to tourism security of low degree of concentration with a focus on relative dispersion. From 2009 to 2013 in various regions of the coefficient of variation of about 0.6, the in-between difference is larger, but there is a slowly declining trend. (3) There are differences in national network attention to tourism security amongst the three regions. But the difference is small in 2010; the overall in-between differences slowly decrease; Herfindahl coefficient is gradually reduced; the network attention to tourism security concentration degree increases; and the network users or potential tourists are increasingly concentrated in certain areas. The regional primacy goes down obviously; the gap is large in 2010 and 2012; and the regional network attention to tourism security focuses on a certain area. (4) Spatial-temporal differences exist within the three big regions. The coefficient of variation showed a corresponding decrease trend from 2009 to 2013. The western region is higher than central and eastern regions. Herfindahl coefficient differences are less, close to 0.1. With the three regional network attention of users or potential tourists to tourism security between 32 to 37, the geographic concentration is more dispersed. First degree is relatively stable, basically stable at around one, the distribution is more balanced. (5) Further analysis shows that the network attentions to tourism security were influenced by regional economic development, the network developed, the number of incidents, and socio-demographic characteristics such as educational level and age structure. [ABSTRACT FROM AUTHOR]

Studying the attention of "artificial intelligence + education" in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of "artificial intelligence (AI) + education" in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public's attention to "AI + education" differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas. [ABSTRACT FROM AUTHOR]