The industry of intangible cultural heritage (ICH) tourism continues to grow, and social media can serve as an essential tool to promote this trend Although ICH tourism development is outstanding in China, the language structure and restricted use of social media render ICH difficult for non-Chinese speakers to understand Using content analysis, this study investigates the structure and relationships among cognitive elements of ICH tourism based on 9074 blogs posted between 2011 and 2020 on Weibocom, one of the most popular social media platforms in China The main analysis process consisted of matrix construction, dimension classification, and semantic network analysis Findings indicated that the cognitive image of ICH tourism on social media can be divided into seven dimensions: institutions, ICH and inheritors, tourism products, traditional festivals and seasons, tourism facilities and services, visitors, and regions This network vividly illustrates ICH tourism and depicts the roles of organizers, residents, inheritors, and tourists Among these elements, institutions hold the greatest power to regulate and control ICH tourism activities, and folklore appears to be the most common type of ICH resource that can be developed into tourism activities Practically, the results offer insight for policymakers regarding ways to better balance the relationships among heritage protection, the business economy, and people’s well-being Such strategies can promote the industrialization of ICH tourism In addition, through content analysis, this paper confirms the effectiveness of social media in providing a richer understanding of ICH tourism

https://www.mdpi.com/2220-9964/10/4/240/pdf

Using Content Analysis to Probe the Cognitive Image of Intangible Cultural Heritage Tourism: An Exploration of Chinese Social Media

Background During uncertainties associated with the COVID-19 pandemic, effectively improving people's health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles. Methods This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy. Results The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years. Conclusions A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.

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Trends of global health literacy research (1995-2020): Analysis of mapping knowledge domains based on citation data mining.

Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents’ occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both “hardware facilities” (land use mixture, accessibility) and “soft facilities” (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city.

Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

The urban environment is a complex system featuring interactions between nature and humans. Scant research on urban areas has analyzed and assessed urban heat islands (UHIs) from the perspectives of nature and humans. This article analyzes the spatio-temporal characteristics of UHIs in Hangzhou in China in the context of the relationship between nature and humans based on the standard deviational ellipse, Pearson correlation analysis, and principal component analysis (PCA). The results show the following: First, from 2000 to 2020, the coverage of UHIs in the main urban area of Hangzhou decreased, but areas with subhigh temperatures and high temperatures increased. Second, from 2000 to 2020, the main axis of the spatial development of UHIs persisted along the northwest–southeast direction. The spatial pattern of UHIs in Hangzhou was closely related to the direction of urban development. Third, the results of PCA show that socio-economic activities, surface coverage and topography were the major factors influencing UHIs in Hangzhou. Finally, natural factors have slowed the development of UHIs while human factors have aggravated their formation, where the former contributed more significantly to them than the latter. For each unit of change in nine factors that were considered, the surface temperature in the study area decreased by 0.166 units.

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Examining Spatio-Temporal Characteristics of Urban Heat Islands and Factors Driving Them in Hangzhou, China

High-resolution temporal and spatial evolution of carbon emissions from building operations in Beijing

The accurate identification of urban functional areas is of great significance for optimizing urban spatial structure, rationally allocating spatial elements, and promoting the sustainable development of the city. This paper proposes a method to precisely identify urban functional areas by coupling Open Street Map (OSM) and Point of Interest (POI) data. It takes the central urban area of Hangzhou as a case study to analyze the spatial distribution characteristics of the functional areas. The results show that: (1) The central urban areas of Hangzhou are divided into 21 functional areas (6 single functional areas, 14 mixed functional areas and 1 comprehensive functional area). (2) The single functional areas and the mixed functional areas show the geographical distribution characteristics of the looping stratification, which means "Core-periphery" differentiation is obvious, and the comprehensive functional area is relatively scattered. (3) The mixed degree of regional function with ecological function and production function is low while comprehensive functional areas are usually associated with higher potential and vitality. (4) The identification results are in great agreement with the actual situation of Hangzhou central urban area, and the method is feasible. Therefore, this paper can provide a reference for urban development planning and management.

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Identification and analysis of urban functional area in Hangzhou based on OSM and POI data.

Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field.

https://www.mdpi.com/2220-9964/9/11/632/pdf

Ziyi Wang

Papers

Identification and analysis of urban functional area in Hangzhou based on OSM and POI data.

Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace