With the proposal of the Beijing–Hangzhou Grand Canal Cultural Belt, the Beijing–Hangzhou Grand Canal has become an indispensable component of China’s economic development pattern. Despite relatively high regional-economic-development levels along the canal, there still exists a common imbalance in regional development which will hinder the high-quality collaborative development of the region, highlighting the importance of measuring this disparity. This study adopts the district and county as spatial scales and selects five dimensions: economic growth, livelihood benefits, green ecology, innovation drive, and food guarantee in order to construct a comprehensive socio-economic index. Entropy weight method, Moran index, and Theil index analyses are used to examine the recent ten-year evolution laws in regional socio-economic spatial patterns along the Beijing–Hangzhou Grand Canal. Results indicate that socio-economic indicators designed for each dimension effectively measure both individual districts’ and counties’ economic situations as well as those of entire regions. In 2021, limited significance was observed regarding the level of regional economic agglomeration along the Grand Canal region, suggesting a tendency towards fragmented development rather than forming a well-coordinated model. Over the decade from 2011 to 2021, there was an increase in social and economic agglomeration. From a city unit perspective, developmental disparities among districts and counties within cities follow three distinct patterns. Cities at different stages of development can learn from each other’s models to enhance integration cooperation and collectively promote high-quality economic advancements in the Grand Canal region.

Taking the mausoleums in the main urban area of Nanjing city as the study object, with the support of GIS technology, this paper extracts eight influencing factors (surface roughness, elevation, surface cutting depth, surface peaks, outfall, river system, slope, and aspect) corresponding to geomantic elements (“Long”, “Sha”, “Shui”, and “Xue”) in the geographical environments of mausoleums. The AHP is applied in the correlation weight method to calculate the weights of the eight influencing factors, while the information content method is applied in the grading method to calculate the numerical index of each factor. The feng shui (geomantic) suitability of the mausoleums in the main urban area of Nanjing is evaluated using the AHP-weighted information content method and by combining expert knowledge and experience with mathematical statistical techniques. According to the feng shui (geomantic) suitability evaluation results obtained for the mausoleums in the study area, the relatively low-suitability area accounts for 14.55% of the entire study area, the low-suitability area accounts for 25.40%, the suitable area accounts for 29.13%, the relatively high-suitability area accounts for 22.00%, and the high-suitability area accounts for 8.92%. Finally, through a sensitivity analysis of the model, a verification analysis applied using random mausoleums and a feng shui (geomantic) analysis of the Ming Xiaoling Mausoleum, the feng shui (geomantic) suitability evaluation results obtained for the mausoleums analyzed herein are found to be consistent with the actual distribution of the mausoleum sites. The research shows that the AHP-weighted information method based on GIS is suitable for evaluating the feng shui (geomantic) suitability of mausoleums.

As an exquisite asset of Chinese traditional culture, traditional opera occupies a place of high esteem within the world’s cultural and artistic treasury. The impact of emerging cultures has threatened the future of traditional opera culture, necessitating a thorough examination of the historical context of the Grand Canal and traditional opera. There is insufficient research on the spatial evolution of the traditional opera culture along the Grand Canal; thus, this study takes ancient opera stages, a representative cultural relic of traditional opera, as an entry point and employs methods such as kernel density analysis and standard deviation ellipse analysis to analyze the spatial and temporal distribution patterns of the traditional opera culture along the Grand Canal. The results showed that: (i) Nationwide, opera stages in the areas along the Grand Canal exhibit a significant clustering characteristic. (ii) The changes in the number and locations of opera stages in the areas along the Grand Canal are closely related to the rise and fall of the Canal. The opera stages emerged along the Canal, gradually prospered with the development of the Canal, and finally clustered in a band-like cluster along the Grand Canal. (iii) From the Ming Dynasty to the founding of the People’s Republic of China, the opera stages in the areas along the Grand Canal spread in the “southeast–northwest” direction, which was consistent with the main direction of the Grand Canal, indicating its driving influence. (iv) On the centennial scale, from the 14th century to the 20th century, the evolution characteristics of the distribution centroid of opera stages in the areas along the Grand Canal were closely related to the key time nodes of Grand Canal construction and basin expansion. This study reveals the relationship between the Grand Canal and the spatial pattern evolution of traditional opera culture, aiming to promote the construction of the Grand Canal cultural belt.

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

Innovation is the main driver of regional economic development. Exploring the spatial patterns of regional innovation can elucidate the regional differences in innovation development. Nearly all the researches on spatial patterns of innovation take the administrative divisions of provinces, cities and counties as spatial units, and portray the spatial patterns of regional innovation from the macroscopic overall, but lack the spatial patterns within the regions. This paper employed patents of Jiangsu province, China in 2019 as a sample, divided the spatial units based on geographic coordinates of patent data, calculated the technological innovation capability index of within the spatial units, overcoming the drawbacks of the research method that fails to reveal the internal pattern of cities by using provinces, cities and counties as the research scale, and analysed the spatial patterns of technological innovation capability in Jiangsu province by using spatial autocorrelation analysis and standard deviation ellipse. The results show that: (1) In terms of distribution, the spatial pattern of technological innovation capability in Jiangsu province is obviously “one core and one belt” in southern Jiangsu, with Nanjing as the core and Suzhou, Wuxi and Changzhou as a southeast-northwest belt of high innovation capacity, while multi-point scattered in northern Jiangsu. (2) From the perspective of aggregation mode, each city has the largest number of H-H agglomeration units, relatively presenting a zonal distribution in the Suzhou-Wuxi-Changzhou area and Nanjing, and less distribution in other places. (3) Industrially speaking, the development of technological innovation capability of the secondary industry in many cities is better and centralized, while secondary and tertiary industries move hand in hand around the city centre in Nanjing, and the tertiary industry dominates comparably in Suzhou and Wuxi.