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The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country?

Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis.

The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021.

The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism.

This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes.

Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

本文旨在探寻疫情影响下中国大陆城市游客规模演化规律, 具体而言, 疫情影响下, 全国及七大地理分区的国内游客量规模变化如何？七大地理地区的变化有何差异？以及疫情影响下, 全国国内游客空间规模的热点区域和空间集聚程度有何变化？

利用2018-2021年中国大陆337各城市的国内游客量数据, 借助极差、标准差、变异系数、赫芬达尔指等指标分析全国及七大地理分区国内游客规模的绝对差异和相对差异; 借助核密度分析、空间自相关分析等ArcGIS分析工具, 探寻疫情影响下全国国内游客空间规模的热点区域和空间集聚程度的变化情况。

①绝对差异方面, 七大地理分区的绝对差异均持续增大。西南地区的游客量的绝对差异巨大, 国内游发展极不均衡。西北地区、东北地区绝对差异相对较小, 在吸引国内游客方面发展较为均衡。②相对差异方面, 西南地区的国内游发展相对差异较大, 发展不均衡趋势明显; 东北地区、西北地区、华东地区的国内游发展相对差异较小, 发展较为均衡。③热点区域变化方面, 京津冀地区、长三角地区的国内旅游热度持续下降, 在2021年有所回升; 内陆西南地区在2021年成为新的国内游热点区域。④2018年至2021年城市国内游客量规模均呈现出显著的空间正相关的关系, 存在着空间集聚现象, 但部分区域集聚类型在2018到2021年间发生变化。

①理论意义：明晰了疫情对中国国内旅游人次的数量规模和空间规模的影响, 弥补了当前疫情前后国内旅游业变化对比的研究; 阐明了疫情前后中国城市国内游客空间格局的变化, 拓展了研究情景, 丰富了中国旅游业时空变化的相关研究。②实践意义：明晰了疫后不同地区国内旅游人次的数量规模和空间规模变化情况, 以及国内旅游市场热点变化和游客空间偏好变化, 有利于各地区城市对症下药, 制定符合各省份实际情况的国内旅游业振兴策略, 促进中国旅游业内循环。

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

The digital economy has become a key force supporting the high-quality development of tourism. This paper discusses the coupling coordination relationship and spatiotemporal evolution path of digital economy and tourism in China's provinces.

This paper uses the entropy method to measure the development level of digital economy and tourism, and establishes coupling coordination model and spatial autocorrelation model to study the interaction between the two industries.

Results show that the development levels of the two industries are rising, which spatially show a progressively decreasing pattern of east-middle-northeast-west. The coupling coordination degrees of the two industries have increased steadily, but the overall level is still near maladjusted. Spatially, the positive correlation is increasing, but the incongruity of spatial agglomeration is still significant. The coupling coordination evolution path in the provinces shows differentiated characteristics. The migration path is mainly concentrated in Zones I and II. The eastern region has an obvious trend of extending to Zone III, where the tourism industry was the most affected by the pandemic.

The study helps clarify the industrial coupling and coordination relationship in various regions and formulate regional tourism digital transformation strategies to promote the high-quality development of China's tourism industry.

This paper enriches the research on the relationship between digital economy and tourism from the perspective of industrial integration. The development commonality of China's tourism digital transformation summarized provides theoretical reference and demonstration for the coordinated development of China's tourism.

The paper seeks to investigate the changing and challenging spatial nature of the rising knowledge cities' knowledge precincts.

The paper reviews the literature on recent knowledge precinct developments within the frame of innovation and urban economic competitiveness. The methodology develops a typological investigation and searches for useful insights for better understanding the fundamentals of knowledge precincts. The study exemplifies cases from Australia as well as other global best practices.

The paper sheds light on the contemporary knowledge production of rising knowledge cities, and points out the changing spatial agglomeration of knowledge‐intensive industries and the formation of new types of knowledge precincts as the spatial core of knowledge‐based urban development.

The paper provides an in‐depth discussion on the changing spatial concepts of knowledge precincts and their vital role for the knowledge‐based urban development of cities.

The purpose of this paper is to explore the heterogeneity and correlations of agricultural greenhouse gas (GHG) emissions among provinces in China, and then policy implications are proposed.

After agricultural GHG accounting and a pre-analysis of inter-provincial heterogeneity, improved gravity model and the Social Network Analysis (SNA) methods are introduced to construct the network, being carried out from three aspects of the whole network, individual provincial characteristics and cluster analysis.

(1) There are significant regional variations in agricultural GHG scale among provinces owing to the layout of agricultural production, and the temporal trends show that the direction and speed of agricultural GHG scale change vary among provinces; (2) In terms of inter-provincial correlations, there exists a complex spatial network of agricultural GHG among provinces, which tends to be more complex, intensive and stable, while the status of the provinces in the network also has gradually become more balanced. All provinces played their respective roles in the four clusters of the network with agricultural layout and comparative advantages, and the distribution has continuously optimized.

The inter-provincial network characteristics of agricultural GHG emissions and its evolution have practical implications for differentiated and coordinated agricultural GHG reduction policies at the provincial levels.

This paper innovatively study inter-provincial agricultural GHG correlations in China with the SNA methods used to study economic and social connections in the past. There is some originality in the introduction of network theory and application of the SNA methods, which can provide some reference for researches in similar fields.

In recent years, the term “smart city” has attracted a lot of attention from policy makers, business leaders and citizenship in general. Although there is not a unique definition of what a smart city is, it is generally accepted that “smart” urban policies refer to local governments’ initiatives that use information and communication technologies in order to increase the quality of life of their inhabitants while contributing to a sustainable development. So far, “smart city” approaches have generally been related to top-down processes of technology diffusion. The purpose of this paper is to present a broader view on “smart” initiatives to analyze both top-down and bottom-up dynamics in a smart city. The authors argue that these two perspectives are complementary and its combination can reinforce the collaboration between different city stakeholders. Top-down and bottom-up initiatives are not opposed forces but, on the contrary, can have a synergistic effect on the innovation capacity of the city. Both perspectives are illustrated by providing examples of different “smart” aspects in the city of Barcelona: smart districts, open collaborative spaces, infrastructures and open data.

To illustrate the arguments, the authors analyze the case of the city of Barcelona providing examples of top-down and bottom-up initiatives in four different smart city aspects: smart districts, open collaborative spaces, infrastructures and open data. The research method is based on a case study (Yin, 1984). The primary data consisted on interviews to city council representatives as well as managers of local public institutions, like economic development offices, and local organizations like for instance coworking spaces. The authors interviewed also specialists on the innovation history of the city in order to validate the data. In addition, the authors used secondary data such as reports on the 22@, and documentation on the Barcelona innovation policies, as well as doing a compilation of press articles and the online content of the institutional webpages. All together, the authors have followed a data triangulation strategy to seek data validation based on the cross-verification of the analyzed data sources.

The analysis suggests that the top-down and bottom-up perspectives are complementary and their combination can reinforce the collaboration between different city stakeholders. Top-down and bottom-up initiatives are not opposed forces but, on the contrary, can have a synergistic effect on the innovation capacity of the city. Both perspectives are illustrated by providing examples of different “smart” aspects in the city of Barcelona: smart districts, open collaborative spaces, infrastructures and open data.

Nevertheless, the analysis has its limitations. Even if the authors have emphasized the importance of the bottom-up initiatives, citizens do not have often the resources to act without governmental intervention. This is the case of services that require high-cost infrastructures or regulatory changes. Also, as it usually happens in the case of disruptive technology, it is hard for citizens to understand the possibilities of its use. In these cases, firms and institutions must play an important role in the first phases of the diffusion of innovations, by informing and incentivizing its use. It is also important to note that some of the emerging usages of technology are confronted to legal or regulatory issues. For instance, distributed and shared Wi-Fi networks might be in opposition to economic interests of internet providers, that often difficult its expansion. It is also the case of services of the sharing economy that represent a menace to established institutions (like the tensions between Uber and taxi companies, or Airbnb and hotels). In these cases, city halls like it is the case in Barcelona, tend to respond to these emergent uses of technology by regulating to ensure protection to existing corporate services.

In conclusion, the transformational process that leads a city to become a smart city has to take in consideration the complexity and the plurality of the urban reality. Beyond considering citizens as being users, testers or consumers of technology, local administrations that are able to identify, nourish and integrate the emerging citizens’ initiatives would contribute to the reinforcement of a smart city reality.

The contribution of the paper is to go beyond the generalized technologic discourse around smart cities by adding the layer of the citizens’ initiatives.

In the context of today's knowledge economy, development policies that are mainly based on knowledge as a primary source of wealth and added‐value are increasingly adopted by cities and countries. This tendency is becoming much more common due to the global financial crisis. Thus, the knowledge‐based development field has been in a state of important progress during the last decade. This paper seeks to discuss and highlight the major issues of knowledge‐based development. By doing so the paper aims to present an agenda for future research directions for the present decade of 2010‐2020.

The paper presents an agenda for future research by co‐instantaneously presenting the status in relation to the major pending issues of the knowledge‐based development field. This is accomplished through a wide‐scope literature review and qualitative scoring of knowledge‐based development (KBD) categories.

The main finding is that research is needed in four main directions: development of holistic and unified approaches for the practical formulation of citizen‐centric knowledge‐based development strategies; knowledge‐based urban planning; knowledge‐based development assessment and metrics; practical aspects of implementation of knowledge‐based development approaches.

Research in the paper is based on a wide literature review of the most important and added‐value available resources in the KBD field and in qualitative scoring of research categories. Use of empirical components such as citation analysis, field surveys, knowledge network analysis as well as for a specific approach regarding the scoring system would be needed in future research.

The discussion and issues presented in the paper should be of value not only to researchers and practitioners but also to cities and countries willing to design efficient and effective knowledge‐based development strategies.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

Land transactions are a key indicator of urban sustainable development and urban space expansion. Therefore, this paper aims to study the spatial correlation of different types of land transactions.

Based on the big data of land micro transactions in Yangtze River Delta urban agglomeration, this paper uses the generalized forecast error variance decomposition (GFEVD) method to measure the correlation level of urban land markets. Also, social network analysis (SNA) is used to describe spatial correlation network characteristics of an urban agglomeration land market. In the meantime, the factors that influence the spatial correlation of urban land markets are investigated through a quadratic assignment procedure (QAP).

The price growth rate of urban residential land was higher than that of industrial land and commercial land. The spatial relevance of urban residential land is the highest, while the spatial relevance of the urban commercial land market is the lowest. The urban industrial land market, commercial land market and residential land market all present a typical network structure. Population distance (POD) and Engel coefficient distance (EGD) are negatively correlated with the correlation degree of the urban residential land network; traffic distance (TRD) and economic distance (ECD) are negatively correlated with the correlation degree of the urban industrial land network and commercial land network.

This paper uses a systematically-integrated series of problem-solving models to better explain the development path of urban land markets and to realize the integration of the interdisciplinary methods of geography, statistics and big data analysis.