Modeling Analysis Research Articles

Modeling the determinants of circular procurement adoption for sustainable construction: a fuzzy logic-based evaluation approach

PurposeTraditional procurement approaches are no longer sufficient amidst dire environmental challenges caused by the construction sector. Yet, it is unclear from existing studies what factors could encourage alternatives such as circular procurement (CP). Thus, this study aims to investigate the determinants of CP adoption in the global construction industry.Design/methodology/approachThe study employs a mixed-method approach involving focus group interviews with circular experts and engaging 194 international construction professionals using a questionnaire survey. Multiple statistical techniques, including exploratory factor analysis and fuzzy synthetic modelling, were employed to analyze the data.FindingsThe study identified fourteen key benefits that drive the adoption of CP practices in construction, with “Waste management throughout the construction lifecycle,” “Integration of supply chains” and “Opportunities from new business models” as the three most significant. However, a deeper analysis of the interconnections between these drivers revealed four clusters of principal benefit drivers (PBDs). The four principal groups were “lifecycle cost optimization,” “climate impact mitigation,” “circular capability and waste reduction” and “supply chain collaboration.” Through fuzzy synthetic modeling, it was predicted that three out of four PBDs exerted high levels of influence and should be prioritized for developed economies, while all four PBDs had significant levels of influence in developing regions.Practical implicationsThe findings provide a useful reference point for policymakers, practitioners and researchers in formulating strategies to accelerate the adoption and implementation of CP practices in different construction industries.Originality/valueThe study provides a novel perspective on CP adoption through understanding and promotion of its benefits. This offers a valuable framework for decision-making in two regional contexts.

Modeling and numerical analysis of concrete specimen under dynamic and thermal loading taking into account non-linearity

In this study, we propose a model describing the mechanical behavior of basalt aggregate concrete under high temperatures. Basalt aggregate concrete is formulated. The bulk density, residual mechanical (compressive strength, tensile strength and elastic modulus) and microscopic properties of basalt aggregate concrete specimens are analyzed. The damage basalt aggregate concrete, the geometry and nonlinear behavior of the material is investigated. Based on Newton’s second law, the investigation is conducted on the simultaneous effects of mechanical loading and thermal treatment. The finite difference method is used for the numerical solution of the problem to determine the peak stress and strain of normal basalt aggregate concrete as a function of temperatures and time. It follows from the results obtained that, after [3–5 min] the critical temperature at which material damage, for both stress and strain, is observed to be 350 °C at the center, corresponding to a 30% reduction in peak stress on the value initially obtained experimentally; And 300 °C at the free and fixed end of basalt aggregate concrete specimen respectively, corresponding to a 38 and 30% reduction in stress in the z-axis direction. Beyond these temperatures, it is observed that the amplitude of the deformations continues to increase whatever the position announcing an imminent collapse of the material, probably due to the loss of stiffness of the cement paste which significantly reduces the mechanical performance of the normal basalt aggregate concrete observed experimentally.

Analysis on Flexural Performance of Prestressed Steel-Reinforced UHPC Beams

The flexural performance of prestressed steel-reinforced ultra-high-performance concrete (UHPC) beams was investigated through finite element (FE) modeling and nonlinear numerical analysis. Two FE models were established for prestressed steel-reinforced concrete beams and prestressed UHPC beams, respectively, and the accuracy was validated by comparing with the experimental results. On this basis, a comprehensive analysis of load–deflection behavior, load–strain relationships, and failure modes was conducted under varying parameters, including reinforcement ratio, prestress level, steel web thickness, and steel flange thickness. The results indicate that an increase in reinforcement ratio from 1.17% to 1.90% enhanced the ultimate load by 12.05%, while increasing the prestress level from 0.61 to 0.76 improved the cracking load by 114.88% and the ultimate load by 53.73%. Moreover, the beams exhibited superior ductility and cracking resistance compared to conventional concrete structures. A formula for predicting the flexural capacity of prestressed steel-reinforced UHPC beams was derived, showing an average error of less than 3% when compared with FE simulations. This formula provides a reliable basis for the structural design and optimization of high-performance composite beams in engineering practice.

Assessing the Role of Machine Learning in Climate Research Publications

Climate change is an aspect in our lives that presents urgent challenges requiring innovative approaches and collaborative efforts across diverse fields. Our research investigates the growth and thematic structure of the intersection between climate change research and machine learning (ML). Employing a mixed-methods approach, we analyzed 7521 open-access publications from the Web of Science Core Collection (2004–2024), leveraging both R and Python for data processing and advanced statistical analysis. The results reveal a striking 37.39% annual growth in publications, indicating the rapidly expanding and increasingly significant role of ML in climate research. This growth is accompanied by increased international collaborations, highlighting a global effort to address this urgent challenge. Our approach integrates bibliometrics, text mining (including word clouds, knowledge graphs with Node2Vec and K-Means, factorial analysis, thematic map, and topic modeling via Latent Dirichlet Allocation (LDA)), and visualization techniques to uncover key trends and themes. Thematic analysis using LDA revealed seven key topic areas, reflecting the multidisciplinary nature of this research field: hydrology, agriculture, biodiversity, forestry, oceanography, forecasts, and models. These findings contribute to an in-depth understanding of this rapidly evolving area and inform future research directions and resource allocation strategies by identifying both established and emerging research themes along with areas requiring further investigation.

Exact Moments of Residuals of Independence

The diagnosis of residuals of independence is critical in association analysis and loglinear modeling of two-way contingency tables. Most residual diagnostics depend on large-sample methods, and diagnostic results become dubious when sample sizes are small or data are sparse. In such cases, statistical inference based on non-asymptotic theory or exact inference is desirable. This paper explicitly derives the first four moments of the residuals of independence in a two-way contingency table under a multinomial model. These exact moments are necessary tools for studying the analytical features of the distribution of residuals of independence, such as skewness and kurtosis. Higher-order moments can be found similarly, but the results are more complicated.

Influence of Green Marketing Strategy and Brand Image on Purchase Decision with Mediation of Purchase Intention (Case Study on Healthy Food Products Matoh Cassava Chips in Bojonegoro Regency)

This study aims to describe the application and influence of Green marketing strategy and Brand image on purchase decision through purchase intention and the right strategy for marketing Matoh Cassava Chips. The method used is quantitative with a sample of 80 consumers of Matoh Cassava Chips in Bojonegoro Regency, the sample is determined by accidental sampling, data obtained using a questionnaire. The data analysis technique in this study uses descriptive analysis and Structural Equation Modeling - Partial Least Square (SEM-PLS) analysis to test the hypothesis and determine the influence between variables. The results of the analysis show that the implementation of green marketing strategies and overall brand image has a high category, there is a significant influence of exogenous variables on purchasing decisions, purchase intention significantly mediates the influence of both on purchasing decisions, and makes marketing strategies that combine these two factors an effective approach in influencing consumer behaviour. So from these results it can be concluded that Green marketing strategy and Brand image have a significant effect on purchase decisions, as well as purchase intention which is able to significantly mediate this influence.

Selective Self-Assembly of Supramolecular Helices and Macrocycles Directed by Halogen Bonding.

In this study, several hydrogen-bonded arylamide foldamers (compounds 1-5) with the same degree of polymerization were designed and synthesized. The polyfluoroiodobenzene or iodoethynyl polyfluoroiodobenzene segment was modified as a halogen donor at the end of the monomer, and pyridine or pyridine oxynitride served as the corresponding halogen acceptor segment. The crystal structure of compound 1 indicates that the supramolecular double helices were constructed by stacking a P helix and an M helix in an antiparallel manner in the direction of intermolecular I⋅⋅⋅-O-N+ halogen bonding. The zig-zag-shaped linear structures of compounds 2 and 3 were generated under the induction of intermolecular I⋅⋅⋅N and I⋅⋅⋅-O-N+ halogen bonds. [1+1] near-planar bimolecular supramolecular macrocycles in compounds 4 and 5 were formed by multiple groups of effective intramolecular three-center hydrogen bonding and strong I⋅⋅⋅N and I⋅⋅⋅-O-N+ halogen bonds. Hirshfeld surface analysis and theoretical modelling of the halogen bonding energy were performed. The noncovalent interactions on the surface of the monomers and the halogen bonding energy were investigated in detail.

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

The stability and bioavailability of xanthohumol loaded by sophorolipid‐based microemulsions in functional beer

AbstractXanthohumol (XN) exhibits numerous physiological activities, yet its instability and proneness to degradation limit its use. Microemulsions, noted for effective drug solubilization and delivery, have been employed to enhance XN's stability and bioavailability in functional beers, using sophorolipids (SL) and Tween 80 (T80) as surfactants. Our development of T80‐ME and T80‐SL‐ME stabilized XN, as confirmed by reduced degradation in high‐performance liquid chromatography analysis and kinetic modeling. We established in vitro release and in vivo pharmacokinetic models, demonstrating improved outcomes with XN‐enriched beer. Our study also assessed total phenols, flavonoids, and antiaging effects in the beer. The formulation minimally affected beer's physicochemical properties, including pH, turbidity, and bitterness. Notably, XN's bioavailability in beer improved 1.79‐ to 7.33‐fold, underscoring microemulsion technology's role in enhancing XN absorption and metabolism. We analyzed the sensory flavor impact of SL biosurfactants in beer, finding them acceptable to consumers. This study highlights microemulsions' potential in elevating functional beer's nutritive value and applications.

Analysing Online Reviews Consumers’ Experiences of Mobile Travel Applications with Sentiment Analysis and Topic Modelling: The Example of Booking and Expedia

This study aims to analyse consumer experiences, purchase behaviours, and emotional responses through Booking and Expedia’s mobile applications. The 2000 user reviews collected from Google Play were subjected to a comprehensive sentiment analysis, text mining, and topic modelling process to identify the key elements that shape consumers’ emotional experiences and purchase decisions. According to the results of text mining and sentiment analysis performed with Python’s WordNet library, 81.9% of Booking.com reviews are positive, 8.4% are negative, and 11.3% are neutral, whereas 55.8% of Expedia reviews are positive, 37.8% are negative, and 8.0% are neutral. In the topic modelling analysis, Booking.com emphasised ease of booking, while Expedia emphasised difficulties in cancellation and refund processes. These findings provide valuable insights into how consumers’ emotional states and purchasing behaviours are reflected in their experiences with mobile applications. The study enables the development of strategic recommendations for marketing management to better analyse consumers’ expectations and experiences.

Artificial intelligence-driven brand strategy: Impact on awareness, image, equity, and loyalty

The study investigates the impact of artificial intelligence (AI)-powered chatbots on brand dynamics within the banking sector, focusing on the interrelationships between AI implementation and key brand dimensions, including awareness, equity, image, and loyalty. Using structural equation modeling (SEM) analysis on data collected from 520 banking customers, the study tests eight hypotheses to explore the direct and indirect effects of AI-driven interactions on brand development. The findings reveal that AI chatbots significantly enhance brand awareness in banking services, demonstrating moderate positive effects on both brand equity and brand image. Notably, while brand awareness exerts a strong influence on brand image, it does not have a significant direct effect on brand loyalty. Instead, the study shows that brand loyalty is primarily developed through the mediating effects of brand equity and image, with brand image exerting a particularly strong influence on brand equity. For banking practitioners, these insights suggest a need to integrate AI chatbots within a comprehensive brand strategy that merges technological innovation with traditional relationship-building approaches. Limitations of the study and potential directions for future research are also discussed, providing avenues for further exploration of AI’s role in brand management.

Genome-Wide Identification and Evolutionary Analysis of Functional BBM-like Genes in Plant Species

Background/Objectives: BABY BOOM (BBM), a transcription factor from the APETALA2 (AP2) protein family, plays a critical role in somatic embryo induction and apomixis. BBM has now been widely applied to induce apomixis or enhance plant transformation and regeneration efficiency through overexpression or ectopic expression. However, the structural and functional evolutionary history of BBM genes in plants is still not well understood. Methods: The protein sequences of 10 selected plant species were used to locate the branch of BBM-Like by key domain identification and phylogenetic tree construction. The identified BBML genes were used for further conserved motif identification, gene structural analysis, miRNA binding site prediction, cis-acting element prediction, collinear analysis, protein–protein interaction network construction, three-dimensional structure modeling, molecular docking, and expression pattern analysis. Results: A total of 24 BBML proteins were identified from 10 representative plant species. Phylogenetic relationship analysis displayed that BBML proteins from eudicots and monocots were divided into two clusters, with monocots exhibiting a higher number of BBMLs. Gene duplication events indicated that whole genome/segmental duplication were the primary drivers of BBML genes’ evolution in the tested species, with purifying selection playing a key role during evolution processes. Comparative analysis of motif, domains, and gene structures revealed that most BBMLs were highly evolutionarily conserved. The expression patterns of BBML genes revealed significant tissue specificity, particularly in the root and embryo. We also constructed protein–protein interaction networks and molecular docking models to identify functional pathways and key amino acid residues of BBML proteins. The functions of BBMLs may differ between monocots and eudicots, as suggested by the functional enrichment of interacting proteins. Conclusions: Our research delved into the molecular mechanism, evolutionary relationships, functional differentiation, and expression patterns of BBML genes across plants, laying the groundwork for further investigations into the molecular properties and biological roles of BBMLs.

The Role of Training in Big Data Analytics Adoption: An Empirical Study of Auditors Using the Technology Acceptance Model

This study investigates the impact of training on auditors' intention to adopt Big Data Analytics (BDA) in auditing processes, using the Technology Acceptance Model (TAM) as a theoretical framework. This study seeks to fill the gap in research on the impact of training in the adoption of BDA in audit procedures. While most existing studies have concentrated on the general benefits and challenges of BDA in auditing and other business sectors, they have largely overlooked the specific influence of training as an external factor on the use of BDA in auditing processes. Moreover, there is a significant research gap concerning the application of BDA in developing countries, including Palestine. A census survey of 94 auditors from Big Four accounting firms in Palestine was conducted, with an 86% response rate. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis revealed that training positively influences perceived usefulness (β = 0.658, p < 0.001) and perceived ease of use (β = 0.616, p < 0.001) of BDA tools. Perceived usefulness significantly affects behavioral intention to adopt BDA (β = 0.532, p < 0.001), while perceived ease of use does not. Behavioral intention positively impacts actual use of BDA tools (β = 0.481, p < 0.001). Based on these findings, audit firms should focus on strategies to translate positive intentions into actual usage. This can be accomplished through ongoing support and resources, such as regular training programs and showcasing success stories that highlight the practical advantages of BDA tools. By fostering an environment that actively supports and encourages the use of BDA, audit firms can ensure that their auditors not only intend to use these tools but also integrate them into their daily auditing practices. This paper contributes to understanding BDA adoption in auditing, particularly in developing countries, and provide insights for audit firms in designing effective training programs to enhance BDA adoption.

Organic Amendments Enhance Peanut Nodulation by Influencing Interactions Between Rhizobia and Arbuscular Mycorrhizal Fungi in the Peanut Rhizosphere

The primary mechanism through which organic amendments enhance the nodulation ability of leguminous crops involves increasing the content of available phosphorus (AP) in the soil. Despite this, the role of plant rhizosphere microbial communities has not been fully appreciated. This study investigated the impact of different fertilization treatments, including a no-fertilizer control group (CK), a chemical nitrogen–phosphorus–potassium treatment group (NPK), and a group treated with both organic manure and chemical NPK (NPKM), on the structure of microbial communities in the peanut rhizosphere and their nodulation ability using high-throughput sequencing technology, co-occurrence network analysis, and structural equation modeling. The peanut nodule count increased significantly on organic manure plus NPK application, ranging from 2.26 to 2.50 times that of the CK treatment group. Under NPKM treatment, the diversity of bacterial and rhizobial communities in the peanut rhizosphere significantly improved. Co-occurrence network analysis showed that the peanut rhizosphere microbial co-occurrence network under NPKM treatment was more complex. Additionally, the ecological importance of rhizobial groups within the network significantly increased. Further analysis of the interaction patterns between different microbial groups revealed that under NPKM treatment, the positive correlation ratio between arbuscular mycorrhizal fungi (AMF) and rhizobia (RPAR) was significantly higher than that in the CK and NPK treatment groups. Structural equation modeling (SEM) analysis demonstrated that the RPAR significantly enhanced peanut nodulation ability, an effect that was positively modulated by AP. In summary, this study highlights the potential key role of the synergistic action between rhizobia and AMF in influencing the nodulation process of leguminous plants and provides a scientific basis for the effective management of plant microbial communities.

Exploring Satisfaction and Trust as Key Drivers of e-Government Continuance Intention: Evidence from China for Sustainable Digital Governance

With the rapid popularization of digital technology, e-government has become an essential tool for modern governments to improve governance capacity and enhance public trust. Especially in the post-pandemic era, the continued use of e-government plays a crucial role in promoting public participation, enhancing government trust, and fostering sustainable governance. The purpose of this study is to explore the behavioral drivers of e-government continuance intention to use (CIU), focusing on the roles of satisfaction (SA) and trust (TR) as mediators. Guided by the Expectation Confirmation Theory (ECT), this study collected data from 393 valid questionnaires and employed quantitative analysis and structural equation modeling (SEM) to examine how perceived usefulness (PU) and expectation confirmation (EC) influence CIU through SA and TR. The findings indicate that perceived usefulness and expectation confirmation significantly and positively affect public satisfaction and trust in e-government, which in turn drive the public’s intention to continue using these platforms. This study extends the application of ECT in the e-government context, providing empirical support for its theoretical framework. Additionally, the results offer practical guidance for optimizing e-government services. By improving the platform’s usefulness and ability to meet user expectations, governments can enhance public satisfaction and trust, effectively encouraging continuous usage of e-government services. This contributes to the realization of modern digital governance and supports the sustainable development of society.

A study of the Donetsk People's Republic retail enterprises’ adaptive development landscape

Subject. This article considers the business environment of retail enterprises of the Donetsk People's Republic and discusses their development problems in conditions of uncertainty. Objectives. The article aims to identify key areas for the adaptive development of retail enterprises in the new Russian regions. Methods. For the study, we used the expert assessment and integral estimation methods, multivariate analysis, and graphic modeling. Results. The article finds that the landscape of adaptive development of retail enterprises of the Donetsk People's Republic is uneven, since it is formed under the influence of a wide range of factors, each of which has a multi-vector impact on their development. The implementation of preferential lending programmes for entrepreneurs expands the opportunities for attracting and using borrowed capital to ensure adaptive development. Conclusions. The active implementation of measures to integrate into the legal, economic, cultural, and educational space of Russia contributes to increasing the comfort of the landscape of adaptive development of retail enterprises in the region. Retailers of the Donetsk People's Republic are facing the urgent task of digital transformation of business processes.

Exploring the Impact of Eco-Friendly Leadership and Motivation on Sustainable Workplace Culture and Environmental Effectiveness to Support the Sustainable Development Goals (SDG’s)

Objective: This study investigates the effects of Green Transformational Leadership, Green Intrinsic Motivation, and Green Extrinsic Motivation on pro-environmental workplace behaviors in Bandung City, Indonesia's creative economy. It is in line with the Sustainable Development Goals (SDGs). Theoretical Framework: This study is founded on the ideas of environmental performance, workplace pro-environmental behaviors, green transformational leadership, and green motivation (both intrinsic and extrinsic), all of which support sustainable practices in line with the SDGs. Method: Utilizing a survey approach, this research targets employees in the creative industry's fashion sector in Bandung City, Indonesia, encompassing sub-sectors like fashion, knitting, footwear, and accessories. The study aims to involve 312 willing participants and employs Confirmatory Factor Analysis and Covariance-Based Structural Equation Modeling (CB-SEM) methodologies. Results and Discussion: The report indicates that green transformational leadership, green intrinsic and extrinsic motivation, workplace pro-environmental behaviors, and environmental performance are all highly correlated. This draws attention to the study's contribution to the SDGs. Research Implications: While acknowledging limitations such as sector-specific generalizability and potential biases in data collection methods, this study highlights the need to expand respondent samples across industries to enhance the applicability of findings and promote sustainable practices in alignment with the SDGs. Originality/Value: This study enhances the existing body of knowledge by examining the relationship between green transformational leadership, green intrinsic and extrinsic motivation, workplace pro-environmental behaviors, and environmental performance within Bandung City's creative industry. It emphasizes how crucial motivation and leadership are to encouraging eco-friendly behavior and enhancing environmental performance, which supports the SDGs.

Psychometric properties of the parent-rated assessment scale of positive and negative parenting behavior (FPNE) in a German sample of school-aged children

BackgroundThe aim of this study was to develop and psychometrically evaluate a parent-rated parenting assessment scale including positive and negative dimensions of parenting. Factorial validity, reliability, measurement invariance, latent mean differences and construct validity of the Assessment Scale of Positive and Negative Parenting Behavior (FPNE) were tested in a pooled sample of five studies of 1,879 school-aged children (6.00 to 12.11 years).MethodsExploratory factor analysis (EFA) was performed on a first randomized split-half sample, and confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were conducted in the second half of the sample. Measurement invariance tests were conducted to assess factor structure equivalence across gender and age.ResultsThe EFA results supported a two-factor structure and the CFA results revealed a model with two correlated factors (Positive Parenting, Negative Parenting), which included 23 items and showed acceptable model fit and good psychometric properties. ESEM did not yield a model with significantly better model fit. Internal consistencies were acceptable. Adequate concurrent validity was demonstrated by low to moderate correlations between the FPNE and similar constructs. The factor structure was invariant (configural, metric, scalar) across different age groups and gender. Tests of latent mean differences revealed that older children scored significantly higher on negative parenting than younger children, while boys showed lower levels of positive parenting and higher levels of negative parenting compared to girls. All effect sizes were small.ConclusionsThe results suggest that the FPNE is a reliable and valid instrument for the assessment of parenting.

How does augmented reality enhance brand equity? The mediating role of the vividness experience

PurposeAdvancements in augmented reality (AR) technology have increased the interest in improving brand equity by creating AR-enhanced branding experiences. However, despite the potential of AR branding, knowledge regarding the underlying mechanisms required for AR features to build brand equity remains limited. Thus, we considered embodied cognition theory to investigate how designing AR features enhances brand equity, particularly through the vivid experience of AR-enhanced body–environment interaction. Specifically, this study focused on both environmental and physical AR features: environmental embedding (EE) and simulated physical control (SPC).Design/methodology/approachThe results of an online experiment with 297 participants in a 2 (high/low EE) × 2 (high/low SPC) between-subjects design underwent analysis of covariance and structural equation modeling to examine the relationship between AR features, vividness and brand equity. We also examined the moderating effects of prior AR experiences.FindingsThe results support most hypotheses that the experience of vividness is a crucial mediator linking AR features (EE and SPC) and consumer-based brand equity. The findings confirm the influential role of prior AR experience in the moderated mediation model, implying that AR-enhanced brand equity occurs primarily among technically adept AR consumers.Originality/valueThis study contributes to the literature by identifying AR-enhanced body–environment interactions as a novel approach for enhancing brand equity. We also revealed the antecedents of vividness in the context of AR-enhanced branding. Moreover, the findings reveal that AR effects are contingent on consumers’ prior AR experience.

Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design.