Nonlinear Canonical Correlation Analysis Research Articles

An Analysis of the Decision-Making Groups of Farmers' Travel Motivation: Insights from Micro-Surveys of Travel Motivations in Rural Communities of Changshan County with Sustainable Resource Management

Although extensive research has been conducted on the tourism market, the intricate decision-making processes underlying tourism motivations remain unexplored. This study examines the theoretical foundation of tourism motivation by adjusting observation indicators based on practical considerations to devise a more scientifically rigorous measurement questionnaire. Through a comprehensive analysis encompassing basic feature analysis, exploratory and confirmatory factor analysis, factor importance cluster analysis, repeated-measurement one-factor analysis of variance, K-means clustering analysis, and nonlinear canonical correlation analysis, we identified six critical motivation factors and two primary travel decision clusters from a sample of 503 rural tourists across four villages in Changshan County, encompassing diverse geographical landscapes. The key findings of this study are as follows. (1) Women and middle-aged rural tourists tend to be more involved in travel decision-making. Furthermore, a higher level of education was positively associated with increased travel intentions among rural tourists. These tourists prefer destinations rich in natural and cultural attributes that favor convenient and environmentally friendly travel modes. Family factors also play a significant role in shaping travel decisions. (2) Age demonstrated significant group differences in all six aspects of rural tourism motivation, highlighting the diverse motivations among different age groups. Notably, decision makers exert considerable influence on environmental factors. (3) Group 1, characterized by decision makers seeking a deep and immersive experience, correlates strongly with female farmer tourists, those under 30 years old, tourists from diverse occupations, and those preferring self-driving tourism modes. (4) Group 2, comprising decision-makers prioritizing leisure and enjoyment, shows a high correlation with male farmer tourists, those over 30 years old, and those favoring eco-friendly tourism. These insights provide a valuable understanding of rural tourists' motivations and decision-making processes, inform strategies for enhancing their travel experiences, and promote sustainable tourism development.

Farmers’ desire to make changes in their agricultural branches in the first wave of COVID-19 pandemic restrictions: The example of Türkiye

The coronavirus pandemic and subsequent protectionary lockdowns have negatively impacted farmers, especially those producing perishable agricultural products worldwide and Türkiye. For this reason, most researchers began to investigate the effects of restrictions on agrarian branches during the sudden shocks of the pandemic. This study was carried out to reveal the farmers’ desire for change in agricultural production branches during the period when coronavirus pandemic restrictions were implemented in Türkiye. A survey study was conducted with broad participation and telephone interviews involving 2125 farmers in different production branches in 22 provinces. Nonlinear Canonical Correlation Analysis was used to analyze variables. According to our analysis results, compared with other agricultural branches, it has been determined that the farmers who do vegetable farming, fruit farming, and livestock farming have a higher desire to change their bare agrarian branches. Approximately half of the farmers interviewed reported difficulties obtaining seeds, fertilizers, and chemicals and providing sufficient labor. Due to these difficulties, 16.5% of the farmers stated that if the first shock effect of the pandemic restrictions continues and this shock effect continues, they will abandon the current main agricultural branches of animal husbandry and vegetable growing. Fruit growing and switch to other farming branches that require less input and labor for agricultural production. For these reasons, policymakers should invest more in market-oriented strategies such as input supply, storage of products, logistics, and processing of manufactured products to maintain the supply chain during pandemic periods. Because these strategies require high costs and the necessary knowledge of post-harvest operations, they are investments that individual farmers cannot afford. Our study revealed that the initial shock effect of the pandemic restrictions in Türkiye had a limited impact on the production of agricultural products. Despite this little impact, 87.3% of farmers stated that their income decreased slightly during this period. We foresee that the restrictions made due to the coronavirus pandemic will affect the planning and social policy in the Turkish economy in the coming years. However, this situation will not change the basic structure of Turkish agricultural production and distribution.

Park Ziyaretçilerinin Sosyo-Demografik Özellikleri ile Park Kullanımı Arasındaki İlişkinin Doğrusal Olmayan Kanonik Korelasyon Analizi ile İncelenmesi: Konya Örneği

Modern cities have seen urban parks evolve from mere green spaces into integral components supporting human well-being and environmental sustainability. These parks offer individuals serene settings to unwind and reconnect with nature, promoting mental health by alleviating stress. However, as urbanization increases and public park demand surges, designing and planning these spaces becomes more challenging. To optimize park design, understanding user profiles and utilization patterns is crucial. By examining how visitor demographics intertwine with park usage, policymakers and planners can make informed decisions for future development.
 This study employs Nonlinear Canonical Correlation Analysis (NLCC), a robust statistical tool, to explore the intricate connections between socio-demographic attributes of urban park visitors and their usage patterns. Focused on Konya, Turkey, the research delves into the non-linear relationships between socio-demographic characteristics and park usage. Survey data from 444 participants forms the basis of the analysis, unveiling inherent dataset structures and assessing correlations between attributes and usage patterns.
 Results highlight the substantial impact of occupation, income, and age as determinants of socio-demographic characteristics affecting park use at an individual level. These findings hold significant implications for urban park planning and management. The research underscores the nuanced interplay between socio-demographic factors and usage patterns, offering targeted strategies for diverse visitor needs. As urban landscapes evolve, this study contributes to refining policies and practices that cultivate sustainable, equitable, and enjoyable park experiences for urban communities.

Regional frequency analysis of stream temperature at ungauged sites using non-linear canonical correlation analysis and generalized additive models

Regional frequency analysis of stream temperature at ungauged sites using non-linear canonical correlation analysis and generalized additive models

Café culture and retail gentrification: a nonlinear canonical correlation analysis of intraclass diversity and bivalent class narratives in Cluj, Romania

ABSTRACT Retail gentrification remodels high streets and inner cities worldwide to appeal to the middle class, including specialty coffee consumption. The consumption of coffee in Cluj-Napoca’s inner-city district, a Central and Eastern Europe city, challenges certain tenets of the ‘revanchist city’ hypothesis, as shown through the concepts of ‘intraclass diversity’ and ‘bivalent class narratives’. Analyzing café consumer preferences, keywords associated with cafés, and social positions through nonlinear canonical correlation revealed that the working-class is co-opted into sustaining a creative, business-friendly city image consistent with middle-class consumption fantasies and self-expression.

Potential conflicts between individual preferences and city color planning: a case study of Busan, South Korea

ABSTRACT Although there are various preferences for color, the city color planning in South Korea is dominated by the government, and the city color preferences of residents are considered to a less extent. Therefore, it results in a question of how to accommodate the diversity of individual preferences. This study took Busan City as an example, using the data collected by the questionnaire survey, first through the chi-square test and nonlinear canonical correlation analysis model to analyze individual attribute factors that distinguish the diversity of city color preferences. It was found that age significantly impacted city color preference, followed by occupation, residential type, educational background, residential location, sex, and monthly income. Regarding the three elements of color, the most significant differences in city color preference were chroma and hue, with slight differences in value preference. Finally, the results were compared with city color guideline to identify the potential conflicts. The findings can provide some recommendations for bottom-up city color planning, and be applied in architectural design and urban regeneration.

Distance to Natural Environments, Physical Activity, Sleep, and Body Composition in Women: An Exploratory Analysis

A growing body of evidence indicates that living close to nature is associated with better health and well-being. However, the literature still lacks studies analyzing the benefits of this proximity for sleep and obesity, particularly in women. The purpose of this study was to explore how distance to natural spaces is reflected in women's physical activity, sleep, and adiposity levels. The sample consisted of 111 adult women (37.78 ± 14.70). Accessibility to green and blue spaces was assessed using a geographic-information-system-based method. Physical activity and sleep parameters were measured using ActiGraph accelerometers (wGT3X-BT), and body composition was assessed using octopolar bioimpedance (InBody 720). Nonlinear canonical correlation analysis was used to analyze the data. Our findings reveal that women living in green spaces close to their homes had lower levels of obesity and intra-abdominal adiposity. We also demonstrated that a shorter distance to green spaces seemed to correlate with better sleep onset latency. However, no relationship was found between physical activity and sleep duration. In relation to blue spaces, the distance to these environments was not related to any health indicator analyzed in this study.

Socioeconomic Factors Affecting Sustainable Management of Improved Rangelands in Kayseri, Turkey

Sustainability of rangeland is adversely affected by many factors in Turkey, as well as in developing countries. The rangeland in Turkey's Kayseri province does not produce the amount of roughage needed by the current number of animals. To improve this situation, the state has encouraged rangeland improvement and management activities to increase the productivity and sustainability of rangelands. The aim of this study is to determine farmers’ behaviors and characteristics that affect their use of improved rangelands, especially whether their use maintained the improved level of forage production. Farmers engaged in animal husbandry in 11 villages where rangeland improvement activities were completed in Kayseri constituted the population of the study. Simple random sampling was used to determine the farmers included in the sample with 244 farmers interviewed. Data were collected by administering a questionnaire filled during the face-to-face interviews with each individual respondent. Nonlinear canonical correlation analysis was used in the analysis of the collected data. Results identified farmer characteristics and behaviors affecting sustainable rangeland use, as indicated by maintaining forage productivity greater or equal to 3 200 kg/ha on improved pastures. It has been determined that the contribution of the farmers with a large number of animals, a large land size, producing forage crops, and participating in the rangeland improvement activities is higher in the sustainability and adoption of rangeland improvement and management activities. The results of our study will provide a new and different perspective on how to increase the efficiency and planning of rangeland improvement activities and in the formation of agricultural policies on this subject.

Estimation of extreme rainfall quantiles at ungauged sites in the Loess Plateau, China by regional frequency analysis

AbstractAn increase in the frequency of extreme rainfall events has caused more severe floods than before under the influence of climate change, and is receiving a lot of attention in data‐scarce areas like the Loess Plateau, especially in some small basins without hydrometeorological data, and to avert the risk of disaster needs the estimation of extreme rainfall quantiles. In this study, generalized additive models (GAM) are combined with a nonlinear canonical correlation analysis (NLCCA) procedure to estimate extreme rainfall quantiles at ungauged sites in the Wei River basin on the Loess Plateau. Also, the single inverse distance weighting (IDW) interpolation, NLCCA‐backpropagation (BP), and NLCCA‐radial basis function (RBF) models were used as comparative methods for testing the efficiency of the NLCCA‐GAM model. Because meteorological data are generally lacking in rainfall‐ungauged basins, data at ungauged sites was interpolated by IDW interpolation when NLCCA delineated homogeneous regions. Results of validation indicated that maximum daily rainfall quantiles in the Loess Plateau were well estimated by NLCCA‐GAM‐based regional frequency analysis, implying the NLCCA‐GAM is a useful tool for estimating design extreme rainfall in ungauged basins. Results of comparison demonstrated that NLCCA‐GAM was more robust and better reflected the nonlinear relationship between explanatory and response variables in the region than did comparative approaches. In addition, it was found that the NLCCA‐GAM approach made full use of the data from sites in homogeneous regions to mine the valuable nonlinear correlation between ungauged and gauged sites and estimate extreme rainfall quantiles at ungauged sites.

Consumers attitudes toward animal welfare friendly produce: An island of Ireland study

Public concern has arisen between increased productivity in animal production systems and farm animal welfare (FAW). Consumers demand ethical production systems and tend to purchase products that reflect their animal welfare concerns. They are becoming increasingly judicious, with growing expectations around animal produce. Nonetheless, positive attitudes concerning FAW do not always correspond to purchasing behavior, where higher concern is not related to higher purchasing frequency. The study seeks to determine if individuals behave differently in their dual roles as citizens and consumers, expressing preferences for FAW but ultimately failing to act on such preferences when making purchasing decisions. Theoretically, the research uses elements of the COM-B model to investigate the importance of capability, opportunity, and motivation for consumers. The study is conducted on an island of Ireland basis and investigates the perceptions and purchasing frequency of Irish and Northern Irish consumer’s (n = 972). Firstly, attitudes towards purchasing animal welfare products are elicited using Categorical Principal Component Analysis (CATPCA). Cluster analysis subsequently defines three distinct typologies based upon the final cluster centers attained from CATPCA. The largest cluster is characterized as being Indifferent (n = 672) and differentiated by relatively low concern for animal welfare. Despite this, their purchasing frequency of animal welfare products is monthly. The other two clusters, The Engaged and The Struggling, are similar in size and share the same concern for animal welfare but are polarized in their motivation, capability, and opportunity to purchase animal welfare friendly products. The Struggling typology act as citizens rather than consumers; while they are highly concerned for welfare they have significantly lower purchasing frequency even compared to The Indifferent. Finally, Non-Linear Canonical Correlation Analysis (NLCCA) is used to reveal and visually demonstrate the structure of the whole dataset. The analysis demonstrates that concern for animal welfare is not associated with purchase frequency of animal welfare friendly produce when contextual factors are not conducive to doing so. The findings are relevant to a broad audience including industry, policymakers, and academics.

A Single-Side Neural Network-Aided Canonical Correlation Analysis With Applications to Fault Diagnosis.

Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold: 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.

Pro-Environmental Behaviors: Relationship With Nature Visits, Connectedness to Nature and Physical Activity.

Examine the association of visits to the natural environment, connectedness to nature, physical activity, and the adoption of pro-environmental behaviors (PEBs) in individuals aged 18years or older. Cross-sectional study. City of Vila Real, located in the north of Portugal. We recruited 194 individuals (61 men and 133 women) aged 18-75years. A self-administered questionnaire was used to measure nature visits, connectedness to nature, PEBs, and demographic characteristics. Neighborhood green space was appreciated through a Simplified Land Occupation Map and physical activity was measured using ActiGraph accelerometers (wGT3X-BT). Correlations and nonlinear canonical correlation analysis were used to analyze the data. The coefficients of canonical and multiple correlations were calculated. Nature visits were associated with involvement in environmental volunteering (V = .317, P ≤ .05) among men. In these, higher levels of moderate-vigorous PA were associated with green travel behavior (η2 = .325, P ≤ .05). Connectedness with nature was related (P ≤ .05) to private sphere behaviors, such as purchase of eco-products (η2 = .191) and local/seasonal products (η2 = .186) in females and encouraging care and protection of natural environment (η2 = .336, P ≤ .01) in males. Nature visits, connection to nature, and physical activity levels were related to the adoption of PEBs in the private and public sphere, and these relationships differed between men and women.

SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration.

Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds that aims at improving the classification of phenotypes and revealing the biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (such as deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations between multiple (2) modalities. Although there are a few methods to learn nonlinear CCA projections for classifying phenotypes, they only consider two views. Methods extended to multiple views either do not perform classification or do not provide feature ranking. In contrast, SDGCCA is a nonlinear multi-view CCA projection method that performs classification and ranks features. When we applied SDGCCA in predicting patients with Alzheimer's disease (AD) and discrimination of early- and late-stage cancers, it outperformed other CCA-based and other supervised methods. In addition, we demonstrate that SDGCCA can be applied for feature selection to identify important multi-omics biomarkers. On applying AD data, SDGCCA identified clusters of genes in multi-omics data, well known to be associated with AD.

INVESTIGATION OF THE RELATIONSHIP BETWEEN THE VARIABLES OF SOCIAL INSTITUTIONS AND GENDER INDEX (SIGI) IN MENA COUNTRIES: THE NONLINEAR CANONICAL CORRELATION ANALYSIS

Gender equality means that women and men have equal rights in many fields, and that all individuals benefit from all social services equally without any discrimination. Inequality in this area is called gender inequality. Various measurement tools have been developed to measure gender inequality. The Social Institutions and Gender Index (SIGI) is one of these measurement methods. In the study, discrimination in the family and restricted civil liberties, which are two sub-dimensions of SIGI, are discussed and the relationship between them is investigated within MENA countries. In the investigation of this relationship, nonlinear canonical correlation analysis, which is one of the multivariate statistical methods and used in the analysis of categorical variables, is preferred. As a result of the analysis, the existence of the relationship between the variables belonging to the sub-dimensions is confirmed by the Chi-Square independence test, and the strength of the relationship is determined by calculating the contingency coefficient obtained as a result of this test. According to the findings, a very high correlation is determined between the two sub-dimensions and most of the variables of these sub-dimensions.

Associations between university students’ online learning preferences, readiness, and satisfaction

The aim of this study was to examine the associations between the preferences, readiness, and satisfaction of freshman learners after taking online courses. The study group was comprised of Turkish students (892 females, 396 males) who attended their first-year classes at a public university. Participating students attended courses in different departments, but all took these courses for one year online. Data were analysed using optimal scaling analysis. Nonlinear canonical correlation analysis (OVERALS) was used as the variables examined were of different scale levels. According to our findings, learners who preferred face-to-face format were individuals with low levels of learner control, motivation, and satisfaction, and individuals who preferred the online format had high satisfaction levels. Individuals who preferred to take courses in a blended format had low or medium levels of self-directed learning, no previous online course experience, and a medium level of control.

The complexity of the conservation-development nexus in Central African national parks and the perceptions of local populations

Conservation programmes of recent decades aimed to adopt an approach that addresses biodiversity conservation goals through socio-economic tools and to better integrate the human dimension into biodiversity conservation. Yet, to analyse this complex conservation-development nexus, studying conservation perceptions of local populations are crucial to understand the dynamics and establish sound conservation-development management policies. Therefore, we aim to identify the key determinants of conservation perceptions in the Central African context in order to implement successful local and regional conservation strategies. Conservation perceptions of two national parks’ adjacent populations were examined through household surveys, adapted from the Poverty-Environment Network (PEN), in Rwanda and Republic of Congo. Outcomes were statistically analysed to identify the most important factors affecting perceptions about conservation measures. Using a nonlinear canonical correlation analysis, we found that economic factors (e.g. salary, savings, cattle size) and education positively affect conservation perceptions while ecosystem-dependent factors such as hunting and gathering other non-timber forest products have negative effects. Though, we identified a significant difference between two sites, whereby, conservation perceptions are negatively affected by bushmeat factors in Republic of Congo, and NTFP in Rwanda. In addition, our study showed that resource use and rights play a major role in communities’ perceptions and that revenue-sharing projects have a key impact on the perceptions. To ensure sound conservation and development measures, revenue-sharing schemes focusing on material benefits and alternative livelihoods may provide the best approach if participation of communities in the decision-making process is ensured. In this optic, improving education levels will raise awareness and positive perceptions of conservation measures. Development measures should target poor households as they appear to be more conservation-adverse. We conclude that in depth research on local demands for ecosystem products, relationships among stakeholders and community decision power are crucial factors to understand the complexity of the conservation-development nexus.

Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.

Data alignment is one of the first key steps in single cell analysis for integrating multiple datasets and performing joint analysis across studies. Data alignment is challenging in extremely large datasets, however, as the major of the current single cell data alignment methods are not computationally efficient. Here, we present VIPCCA, a computational framework based on non-linear canonical correlation analysis for effective and scalable single cell data alignment. VIPCCA leverages both deep learning for effective single cell data modeling and variational inference for scalable computation, thus enabling powerful data alignment across multiple samples, multiple data platforms, and multiple data types. VIPCCA is accurate for a range of alignment tasks including alignment between single cell RNAseq and ATACseq datasets and can easily accommodate millions of cells, thereby providing researchers unique opportunities to tackle challenges emerging from large-scale single-cell atlas.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.

Socio-demographic determinants of happiness in Turkey

This study examines the relationship between the perception of happiness and socio-demographic characteristics in Turkey using the Life Satisfaction Survey conducted by the Turkish Statistical Institute in 2017. For this purpose, nonlinear canonical correlation analysis was performed on a dataset of 4261 employees. Socio-demographic characteristics evaluated the variable set consisting of both the individual's direct perception happiness and the conceptual/personal sources of happiness. Consistent with the literature, it was seen that socio-demographic variables impacted the levels of happiness in Turkey. The main findings show that marital status has the highest effect on perceived happiness, and married people are happier than the unmarried ones. Age has a negative, educational background has a positive effect. For employment status, it is notable that per diem employees are unhappy. Besides, a detailed perspective to researchers working towards increasing perceived happiness by evaluating the identified sub-groups of working individuals living in Turkey is provided.

Doğrusal olmayan kanonik korelasyon analizi ile balıkçıların iç su balıkları avcılığı konusundaki görüşlerinin değerlendirilmesi

This study aims to present and examine the views among fishermen with different socio-demographic and socio-economic backgrounds (i.e., age, education level, annual income and source of income), fishing in four important inland fishing grounds in Turkey, namely Marmara Lake-Demirköprü Dam Lake (Manisa Province) and Eğirdir Lake- Beyşehir Lake (Isparta Province). The following key points are emphasized; a) Problems in inland fisheries b) Potential reasons of decrease in natural fish stocks c) Problem solutions and recommendations to improve the inland fisheries. The data obtained from a survey administered to 59 fishermen was evaluated using the Non-Linear Canonical Correlation Analysis method. According to the findings of the study: a) The most important problems encountered in fisheries; according to the fishermen with high education level, while the marketing and changes in the water level of the lakes, the fishermen with low education and income level, unlicensed hunting, the lack of punishment and electric hunting are problems b) Fishermen with a high level of education think that fishing with illegal fishing gears during the prohibited fishing season, and those with a low level of education and income are of opinion that small fishing is an important factor in the decrease of natural fish stocks c) Fishermen with high education-income sources adopt the idea of improving legal regulations and complying with them, whereas fishermen with a low-level of education and income think that problems in marketing should be eliminated as a solution to the problem in the fisheries. Results indicate that increasing the effectiveness of the practices that consider the opinions/suggestions of fishermen will contribute to the management and sustainability of fisheries in the region.