Search Results (36)

This paper provides a brief review of the genus Trichorondonia Breuning, 1965. A new species, Trichorondonia wenkaii sp. nov. (文凯毛郎氏天牛), is described and illustrated. Trichorondonia kabateki Viktora, 2024 is newly recorded in Hubei province, with the first description of the male. Additionally, photographs of the holotypes of three previously described species are presented. A key to the four species is given. The new species differs from T. pilosipes and T. hybolasioides in having elytra with rounded lateral apical angles and a vertex with blackish-brown pubescence medially behind the eyes. The new species can also be easily distinguished from T. kabateki by the antennae being ventrally fringed with sparse hairs only on segments 1–8, the greyish-yellow pubescence on the pronotum being unevenly distributed and particularly sparse in the posterior half, the elytra having rather thin greyish-yellow pubescence and hardly visible greyish-white pubescence, elongated blackish-brown spots on the elytral longitudinal carinae, and a small tuft of black setae at the centre of the elytral base where there is no obvious tubercle. The type specimen of the new species was collected in Dianping village, Xinhua town, Leye county, Guangxi Zhuang Autonomous Region of China, and deposited at Insect Collection, College of Agriculture, Yangtze University, Jingzhou, Hubei, China (ICYZU). Full article

Tomato is a widely cultivated vegetable crop worldwide. It is susceptible to various phytopathogens, including fungi, bacteria, viruses, and nematodes. In 2024, an unknown leaf spot disease outbreak, characterized by distinct brown necrotic lesions on leaves, was observed in tomato plants in Yunnan Province, China. Through rigorous pathogen isolation and the fulfillment of Koch’s postulates, it was proved that the fungal isolate could infect tomato leaves and cause typical symptoms. The pathogen isolated from tomato leaves was identified as Nigrospora oryzae based on its morphology and using a multilocus sequence analysis method with the internal transcribed spacer gene (ITS1), beta-tubulin gene (TUB2), and translation elongation factor 1-alpha gene (TEF1-α). This represents the first documented case of N. oryzae infecting tomatoes in the world. Given the damage caused by N. oryzae to tomato plants, we explored biocontrol methods. Through a dual-culture assay on PDA plates, Bacillus velezensis B31 demonstrated significant biocontrol potential, exhibiting strong antagonistic activity toward N. oryzae. In addition, we developed a polyethylene glycol (PEG)-mediated transformation system that successfully introduced pYF11-GFP into the protoplasts of N. oryzae. This achievement provides a foundation for future genetic manipulation studies of N. oryzae. Full article

Historical blocks are a vital component of urban cultural heritage, serving as a link for regional cultural inheritance and a carrier for showcasing urban charm. Enhancing the quality of cultural tourism experiences in these areas can activate the endogenous momentum of cultural tourism industries and foster a virtuous cycle of cultural heritage conservation and utilization. Currently, research on the relationship between historical block spaces and cultural tourism experiences remains deep, and related theoretical gaps also constrain sustainable revitalization practices. Therefore, in this study, 20 representative historic districts with distinct regional cultural characteristics and well-developed cultural tourism in the Huizhou area were selected as research objects. By integrating multi-source data such as geographic information and Dianping reviews and applying the Partial Least Squares Regression (PLSR) statistical method, this study measures the correlation between the spatial morphology of Huizhou historic districts and cultural tourism experience indicators, identifying spatial influencing factors affecting cultural tourism experiences. The results show a significant correlation between the spatial form characteristics of historic districts and the quality of tourists’ cultural tourism experiences. Specifically, the regression coefficients of architectural space, transportation space, landscape space, and facility space in relation to the quality of cultural tourism experiences are significant at the p < 0.01 level. This paper innovatively conducts research from the perspective of urban design, employing a combined quantitative and qualitative analytical approach. The study fills existing gaps in quantitative analysis and empirical research on the spatial forms of historic districts and cultural tourism experiences and breaks through the limitations of qualitative research on traditional cultural tourism. It provides practical references for the organic protection of historical district buildings in the context of sustainable urban renewal. Full article

Streetscapes in old urban areas are not only an important carrier to show regional economies and city style, but also closely correlate to urban residents’ everyday life and the hustle and bustle in which they live. Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. Furthermore, the impact of spontaneous streetscape features on diversified social sensing has rarely been examined. This paper combined the mobile collection of street view images (SVIs) and a machine learning algorithm to calculate eight types of spontaneous streetscape elements and integrated two online platforms (Dianping and Sina Weibo) to map the distribution of economic vitality and social media perception, respectively. Then, through comparing multiple regression models, the impacts of the spontaneous streetscape characteristics on social sensing were revealed. The results include the following two aspects: (1) overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity; and (2) specifically, the spontaneous streetscape elements can be divided into three categories, given the differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results. For example, proper use of open-interface storefronts, ads, and banners is consistent with the common suggestions, while the excessive pursuit of interface diversity and the use of cultural elements may bring an ambiguous effect. This paper provides a transferable analytical framework for mixed and data-driven sensing of streetscape regeneration and can potentially inspire related decisionmakers to adopt a more refined and low-cost approach to enhance urban vitality and sustainability. Full article

From the perspective of social media data, this study investigates the coupling relationship between tourists’ spatial perception and tourism image in traditional old urban areas. Using Nanxun Ancient Town as a case study, this paper reveals the interaction and mutual influence between tourists’ perception of space and tourism image in the development of traditional ancient town tourism. We employed Python 3.13.0 to gather 10,789 valuable comments from tourists from Dianping 11.35.3, Ctrip 8.78.4, and Mafengwo 11.2.6. Mini Tag Cloud software is used to analyze the text data, systematically classify the cognitive image of tourists, and identify negative emotional factors. This paper constructs a four-dimensional landscape spatial perception evaluation system centered on “high-frequency words”, “perceptual dimensions”, “semantic networks”, and “emotional tendencies”. The key findings are as follows: (1) Tourists’ spatial perception exhibits pronounced characteristics of subjective preference and emotional attachment influenced by emotional factors. Overall, tourists exhibited positive emotional perceptions, with 59.51% positive emotions, 21.16% neutral emotions, and 19.33% negative emotions. (2) The perception of Nanxun Ancient Town’s tourism image can be summarized into four dimensions. Here are the dimensions in order of how important they are: historical culture and folk heritage (34.18%), perceptions of natural landscape and architectural style (31.03%), perceptions of tourism services and facilities (18.37%), and psychological identity and emotional interaction (16.42%). (3) Tourism image reciprocally influences tourists’ spatial perception. A positive tourism image is anticipated to encourage tourists to explore the spatial details of the ancient town more deeply, enhancing their positive spatial perception and experience. There exists a coupling relationship between tourists’ spatial perception and tourism image. (4) Key aspects of tourists’ perception of Nanxun Ancient Town include its historical and cultural significance, as well as commercialization. Future studies could focus on tourists’ spatial perception and tourism destination brand image building, and tourism policy makers should pay attention to tourists’ perception of Nanxun Ancient Town’s history, culture and commercialization, and use the coupling of the two to improve development and service policies. Full article

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks. Full article

Social media has rapidly intervened in the interaction between urban consumers and commercial space, further reshaping the structure of urban commercial space. This study employed the social, spatial, and subjective dimensions of geographies of consumption as the theoretical framework. Based on the data from five social media platforms, including Douyin, REDnote, Weibo, Dianping, and Baidu Index, we constructed a multi-level evaluation method of “attention level–activity degree–experience quality” and applied it to measure the dynamics of the shopping malls in Shanghai to investigate their mechanism of generating urban commercial space vitality. The findings indicate that the “core + core–periphery + multi-center + circle structure, agglomeration, and balance” is the primary pattern of urban commercial space in Shanghai. The differences in business formats, consumer positioning, and consumption culture revealed by the social media data are conducive to clarifying the scale of the regional consumption space and the logic of urban commercial evolution. The main contribution of this study is the demonstration that this evaluation method rooted in social media has the potential to generalize the measurement of urban commercial space in major cities in China. We also propose corresponding countermeasures and suggestions for developing urban commercial space in Shanghai. Full article

This study integrates space syntax and big data from the catering industry to explore the impact of grid and organic street patterns on the spatial distribution of restaurants from the perspective of urban morphology. Space syntax is a set of theories and techniques for the analysis of spatial configurations. Focusing on five areas of the Macau Peninsula, this study models urban forms using space syntax. Syntactic parameters and Dianping data are analyzed through geographic visualization, correlation analysis, and descriptive statistics. The results reveal that grid-patterned streets provide a relatively equitable commercial environment through a structured hierarchy, whereas organic-patterned streets foster commercial diversity via more complex accessibility patterns. Additionally, at the local network level, a “cultural layer network” mechanism is revealed in organically shaped streets, supporting the stable distribution of different types of restaurants within specific accessibility ranges. For the first time, this study employs high precision (street-level accuracy), multidimensional analysis (number of restaurants and number of reviews), and a systematic methodology (“form-function” research framework) within the same space syntax model to uncover the effects of different urban morphologies on restaurant distribution. Collectively, these findings highlight street morphology’s key role in shaping vibrant commercial street networks in rapidly urbanizing contexts, reveal the morphological–socioeconomic synergy underpinning local catering ecosystems, and offer robust empirical guidance for integrated urban renewal, planning, and design strategies. Full article

The COVID-19 pandemic highlighted the role of urban parks as green spaces in mitigating social isolation and supporting public mental health. Research in this area is limited due to the lack of large-scale datasets. Moreover, timely studies are indeed necessary under pandemic conditions. This study employs quantitative methods to analyze the temporal and spatial changes in social interaction in 160 urban parks before, during, and after the COVID-19 pandemic, and assesses their correlation with the built environment. Social media data from the Dianping platform were collected for this purpose. A two-step analytical approach was employed: first, machine learning-based keyword analysis identified review data related to social interaction, leading to the construction of two indicators: social interaction intensity and social interaction recovery rate. Second, we applied regression models to explore the correlation between the two indicators in urban parks and 18 characteristics of the built environment. The built environment characteristics associated with social interaction intensity varied across different periods, with seven factors, including natural landscapes, perceptual experience, building density, and road intersections, showing significant correlations with the recovery of social interaction capabilities in the post-pandemic era. Based on these findings, it is recommended that urban planners consider integrating more flexible design element, such as adding greenery and enriching the audio-visual experience for visitors. Furthermore, enhancing the quality and accessibility of park amenities can foster social interaction, thereby contributing to public health resilience in future crises. This research recommends that urban park design should not only support communities’ immediate needs but also prepare for unforeseen challenges. Full article

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations and development. To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). Taking Shanghai as our case study, we utilized over 1.5 million points of interest data from Amap Visiting Vitality Values (VVVs) from Dianping and Shanghai’s administrative area map. We analyzed and compiled data for 344 sites, each containing 39 infrastructure data sets and one visit vitality data set for the ANN model input. The model was then tested with untrained data to predict VVVs based on the 39 input data sets. We conducted a multi-precision analysis to simulate various scenarios, assessing the model’s applicability at different scales. Combining GA with our approach, we predicted vitality improvements. This method and model can significantly contribute to the early planning, design, development, and operational management of CMPBs in the future. Full article

With the development of ecological civilization construction, urban planning and development in China have entered a phase in which optimizing and constructing ecological spaces is required. As a national livable city, Xuchang has experienced rapid economic development in recent years, leading to significant urban expansion that has impacted the layout of ecological space networks in the central urban area and its surroundings. Therefore, identifying and optimizing the spatial layout of ecological corridors in Xuchang City are crucial for ecological development and park city construction. This study utilizes multisource geospatial data to identify and extract ecological corridors in the central urban area of Xuchang City. Ecological resistance and gravity models are employed to identify and verify that the primary ecological corridor pattern in Xuchang City is situated in Weidu District, which is a central urban area. Finally, 11 main ecological corridors in the central urban area are delineated. In response to the identification of ecological corridors, this study integrates spatial analysis methods and text analysis methods to evaluate the characteristics of urban ecological corridors. The results indicate that Xudu Park extends outward, serving as the hub of the ecological network, and that West Lake Park and Luming Lake Park form the core of the urban park system. Finally, based on the spatial relationships, ecological benefits, and citizen experience of each ecological corridor and the green parks it traverses, strategies for optimizing the layout of urban ecological corridors are proposed. Full article

User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common method. It is hard to fully render nuances, emotions, and complexities depending only on text by superficial emotional tendencies alone. Numerical representations may lead to misleading conclusions and undermine public participation. In addition, the use of physiological test data does not reflect the subjective reasons for the comments made. Therefore, it is essential to deeply parse the text and distinguish between segments with different semantic differences. In this study, we propose a perceptual psychology-based workflow to extract and visualize multifaceted views from user-generated data. The analysis methods of FCN, LDA, and LSTM were incorporated into the workflow. Six areas in Fuzhou City, China, with 12 city parks, were selected as the study object. Firstly, 9987 review data and 1747 pictures with corresponding visitor trajectories were crawled separately on the Dianping and Liangbulu websites. For in-depth analysis of comment texts and making relevant heat maps. Secondly, the process of clauses was added to get a more accurate representation of the sentiment of things based on the LSTM sentiment analysis model. Thirdly, various factors affecting the perception of landscapes were explored. Based on such, the overall people’s perception of urban parks in Fuzhou was finally obtained. The study results show that (1) the texts in terms of ‘wind’, ‘temperature’, ‘structures’, ‘edge space (spatial boundaries)’, and ‘passed space’ are the five most representative factors of the urban parks in Fuzhou; (2) the textual analyses further confirmed the influence of spatial factors on perception in the temporal dimension; and (3) environmental factors influence people’s sense of urban parks concerning specificity, clocking behavior, and comfort feelings. These research results provide indispensable references for optimizing and transforming urban environments using user-generated data. Full article

Investigating urban vitality and comprehending the influence mechanisms of the built environment is essential for achieving sustainable urban growth and improving the quality of life for residents. Current research has rarely addressed the nonlinear relationships and synergistic effects between urban vitality and the built environment at the neighborhood scale. This oversight may overlook the influence of key neighborhoods and overestimate or underestimate the influence of different factors on urban vitality. Using Guangzhou’s central urban area as a case study, this research develops a comprehensive urban vitality assessment system that includes economic, social, cultural, and ecological dimensions, utilizing multi-source data such as POI, Dazhong Dianping, Baidu heatmap, and NDVI. Additionally, the XGBoost-SHAP model is applied to uncover the nonlinear impacts of different built environment factors on neighborhood vitality. The findings reveal that: (1) urban vitality diminishes progressively from the center to the periphery; (2) proximity to Zhujiang New Town is the most critical factor for neighborhood vitality (with a contribution of 0.039), while functional diversity and public facility accessibility are also significant (with contributions ranging from 0.033 to 0.009); (3) built environment factors exert nonlinear influences on neighborhood vitality, notably with a threshold effect for subway station accessibility (feature value of 0.1); (4) there are notable synergistic effects among different built environment dimensions. For example, neighborhoods close to Zhujiang New Town (feature value below 0.12) with high POI density (feature value above 0.04) experience significant positive synergistic effects. These findings can inform targeted policy recommendations for precise urban planning. Full article

This study investigates the dynamics of passenger satisfaction and sustainable urban mobility within the context of multimodal railway hubs, focusing on travel behaviors at major stations in China. Against the backdrop of rapid urbanization and the nation’s initiatives to improve transportation efficiency, this research employs social media data analysis to assess passenger sentiment across six key transportation hubs in Eastern China. Utilizing methodological approaches such as keyword frequency analysis and semantic categorization of 39,061 Dianping reviews, supplemented by network visualizations with Gephi, this study reveals insights into factors influencing passenger satisfaction beyond travel efficiency. Signage quality, facility availability, queueing, and crowding emerge as significant determinants of passenger behavior. The study underscores the importance of strategic improvements in station design, navigational aids, and facility management, grounded in real-time data analytics and passenger feedback, to enhance overall passenger satisfaction and promote sustainable urban mobility. This research contributes to advancing understanding of passenger behavior and informs efforts aimed at improving urban transportation systems to meet the evolving needs of passengers and cities. Full article

The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the community level, fostering quality through a socially sustainable perspective (SSP) is a pivotal strategy for people-oriented urban regeneration. Nonetheless, explorations of community quality assessments grounded in an SSP have been notably scarce in recent scholarly discourse. This study pioneers a multidimensional quantitative model (MQM) for gauging community quality, leveraging diverse geospatial data sources from the SSP framework. The MQM introduces an evaluative framework with “Patency, Convenience, Comfort, and Safety” as primary indicators, integrating multi-sourced data encompassing the area of interest (AOI), Point of Interest (POI), Weibo check-ins, and Dianping data. The model’s efficacy is demonstrated through a case study in the Gusu district, Suzhou. Furthermore, semantic analysis of the Gusu district’s street view photos validates the MQM results. Our findings reveal the following: (1) AI-based semantic analysis accurately verifies the validity of MQM-generated community quality measurements, establishing its robust applicability with multi-sourced geospatial data; (2) the community quality distribution in Gusu district is notably correlated with the urban fabric, exhibiting lower quality within the ancient town area and higher quality outside it; and (3) communities of varying quality coexist spatially, with high- and low-quality communities overlapping in the same regions. This research pioneers a systematic, holistic methodology for quantitatively measuring community quality, laying the groundwork for informed urban regeneration policies, planning, and place making. The MQM, fortified by multi-sourced geospatial data and AI-based semantic analysis, offers a rigorous foundation for assessing community quality, thereby guiding socially sustainable regeneration initiatives and decision making at the community scale. Full article