As an important type of linear cultural heritage and a waterfront landscape that integrates both artificial and natural elements, heritage canals provide the public with a multidimensional perceptual experience encompassing aesthetics, culture, and nature. There remains a lack of refined, micro-level studies on heritage canal landscapes from a multidimensional perspective of visual preference. This study focuses on a typical segment of the Grand Canal in China, specifically the ancient canal section in Yangzhou. We employed SegFormer image semantic segmentation techniques to interpret features from 150 panoramic images, quantitatively identifying the waterfront environmental characteristics of the heritage canal. Four perceptual dimensions were constructed: aesthetic preference, cultural preference, natural preference, and hydrophilic preference. Through a questionnaire survey and various statistical analyses, we revealed the relationships between visual preferences for the waterfront landscape of heritage canals and environmental characteristics. The main findings of the study include the following: (1) Aesthetic preference is positively correlated with cultural, natural, and hydrophilic preferences, while natural preference shows a negative correlation with cultural and hydrophilic preferences. (2) Aesthetic preference is influenced by a combination of blue-green natural elements and artificial factors. Natural preference is primarily affected by increased vegetation visibility, cultural preference is associated with a higher proportion of cultural facilities and high-quality pavements, and hydrophilic preference is linked to larger water surface areas, fewer barriers, and better water quality. (3) There are spatial differences in canal waterfront landscape preferences across different urban areas, with the old city exhibiting higher aesthetic, cultural, and hydrophilic preferences than the new city and suburban areas. Finally, this study proposes strategies for optimising and enhancing the quality of waterfront landscapes of heritage canals, aiming to provide sustainable practical guidance for the future planning and management of these heritage sites.

Community gardens offer broad research opportunities and analytical resources encompassing urban planning to environmental sustainability, food systems, and social capital. However, little is known about the knowledge structure and research development related to community gardens. This study presents an in-depth bibliometric performance analysis and visual scientific mapping analysis of the literature on community gardens by examining 487 published papers selected from the Web of Science database. The results indicated a considerable rise in research papers in this subject area from 2012 onwards, with most contributions from the United States. Studies from high-income countries accounted for 93.22%, and 38 countries have cooperated 167 times in this field. “Health” is the most frequent keyword, and the terms “ecosystem services” has been gaining popularity over the last five years. A combination of co-citation clustering and keyword co-occurrence clustering analysis identified three major research themes in the field of community gardens: “ecosystem services and disservices”, “multidimensional association”, and “sustainable garden systems”. The development of ecosystem value assessment frameworks, the establishment of region-wide soil monitoring databases, accounting for the cost-effectiveness of nature-based solutions, the integration of garden systems into smart cities, and the integration of water management into regulation will be important future research directions regarding community gardens. Overall, this study provides scholars with a systematic and quantitative understanding of community gardens.

Jiangsu Province, situated in the Yangtze River basin, has rich traditional village resources and a prominent position in economic development and cultural integration. This study focuses on the analysis of the variation distribution pattern of traditional villages in Jiangsu Province using six batches of traditional village directories with data until 2023 as research samples. By employing ANN, Voronoi graph analysis, and Moran’s I index, the researchers determined the spatial distribution characteristics of rural settlements. Additionally, kernel density and spatial autocorrelation techniques were used to further examine the spatial distribution patterns, and geographic detector detection was introduced. The results showed the following: (1) The spatial distribution of traditional village settlements in Jiangsu Province showed a significant clustering distribution that is mainly concentrated in central Jiangsu Province. (2) The driving factors reflected a strong symbiotic relationship of “air–water–soil–man”. The spatial distribution of traditional villages was mainly driven by the annual mean temperature and soil type. The interaction between factors was dominated by the enhancement relationship between the two factors. (3) According to the detection results of risk areas in the region, the average annual temperature was 17~17.6 °C, the annual precipitation was 133.0~145.7 billion m³, the average annual wind speed was 0.549~0.565 m/s, the GDP was 85,100~204,000 CNY/km⁻², and the population density was 2.32~3.91 thousand/km⁻². Arable land was the main type of area and was conducive to the gathering of traditional villages. The preservation of rural settlements should take into account the complex and diverse factors that affect their distribution. Additionally, it is crucial to tailor protection strategies to specific local conditions and conduct flexible research.

Historic districts face persistent challenges balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study develops a multi-source data-driven evaluation system coupling spatial quality and urban vitality, focusing on China’s Republican-era historic districts with Nanjing’s Yihe Road as a case study. Integrating field surveys and big data (street view imagery, POI data, heatmaps), we quantitatively assess environmental quality and vitality. Key findings reveal a distinct spatial pattern: “high-quality concentration internally” and “high-vitality concentration externally,” where core areas exhibit functional homogenization and low vitality, while peripheries show high pedestrian activity but lack spatial coherence. Clustering analysis categorizes streets into four types based on quality and vitality levels, highlighting contradictions between static conservation and adaptive reuse. The study deepens understanding of spatial differentiation mechanisms and reveals universal patterns for sustainable development strategies. A multi-tiered governance strategy is proposed: urban-level flexible governance harmonizes cross-departmental policies via adaptive planning, district-level differentiated governance activates spatial value through functional reorganization, and street-level fine-grained management prioritizes incremental micro-renewal. The research underscores the critical need to balance heritage preservation with contemporary functional demands during urban renewal, offering a practical framework to resolve spatial conflicts and reconcile conservation with regeneration.

Constructed wetlands for effluent treatment (CW-ET) play a vital role in the degradation of pollutants, the purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands. In this context, numerical simulations aid in optimizing vegetation selection and placement in these systems, thereby enhancing their overall efficiency. In this study, the MIKE21 hydrodynamic (HD) module was coupled with the advection–dispersion (AD) module to simulate the Yingtai CW-ET in Hai’an. Accordingly, key parameters involved in effective water purification were calibrated and the system’s performance in treating effluent from wastewater treatment facilities was evaluated. The findings demonstrated significant removal efficiencies for chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia (NH₃-N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited high accuracy, with hydrodynamic predictions deviating by under 5% and water quality approximations by under 15%. The treated water quality met the requirements for Class IV surface water standards. Utilizing numerical simulations offers valuable insights for the design and performance evaluation of future constructed wetlands.

Urban waterfront areas (UWAs), which are essential natural resources and highly perceived public areas in cities, play a crucial role in improving the quality of the urban environment. While numerous studies have delved into the visual perception of urban environments, little attention has been paid to understanding how the visual perception of urban riverfront streets (URSs) differs with various aspects within their unique spatial environment. This study took the Gusu District in Suzhou, China, as a case study, applying deep learning to street-view images to identify urban riverside landscape elements and evaluate their visual attention, aesthetic preference, and distinctiveness through eye-tracking technology and questionnaires. Subsequently, a multidimensional assessment was conducted to analyze how landscape elements influence visual perception in the urban riverfront street. This study concludes that (1) riverfront streets in the Gusu District present balanced visual attention, with high aesthetic preference but limited distinctiveness, and only a few roads in the ancient city score highly for distinctiveness. (2) Greenery, traditional-style buildings, water, and riverfronts positively impact visual perception, while buildings have a negative impact, and backgrounds such as the sky and roads exhibit minimal influence. This study validated the scientific accuracy, appropriateness, and precision of assessments of visual attention, aesthetics, and distinctiveness to quantitatively evaluate the multidimensional human perception of URSs.