Drawing on cognitive-affective system theory, this study proposes that employees’ perceived green human resource management (HRM) influences their’ workplace green behaviors through two psychological processes: the cognitive and the affective route. By analysing 358 questionnaires collected from Chinese firms in the oil and mining industry, we obtain evidence in support of our predictions, finding that employees’ perceived green HRM positively impacts their voluntary workplace green behaviors and green creativity. Additionally, green psychological climate and harmonious environmental passion are found to partially mediate the relationship between employees’ perceived green HRM and voluntary workplace green behavior while harmonious environmental passion is found to fully mediate the relationship between employees’ perceived green HRM and green creativity. These findings shed light on the importance of green HRM in shaping employees’ proactive workplace green behaviors and uncover how green HRM transforms employees’ cognitive, affective, and motivational (CAM) factors into green actions.

Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and more accurate using innovative information techniques that have become available in the era of big data. This study introduces an integrated method based on geographical information systems (GIS) and an emotion-tracking technique to quantify the relationship between people’s emotional responses and urban space. This method can evaluate the degree to which people’s emotional responses are influenced by multiple urban characteristics such as building shapes and textures, isovist parameters, visual entropy, and visual fractals. The results indicate that urban spaces may influence people’s emotional responses through both spatial sequence arrangements and shifting scenario sequences. Emotional data were collected with body sensors and GPS devices. Spatial clustering was detected to target effective sampling locations; then, isovists were generated to extract building textures. Logistic regression and a receiver operating characteristic analysis were used to determine the key isovist parameters and the probabilities that they influenced people’s emotion. Finally, based on the results, we make some suggestions for design professionals in the field of urban space optimization.

Detecting network communities has recently attracted extensive studies in many fields. However, little attention has been paid to detection and analysis of dynamical communities. This study intends to propose a methodological framework to detect dynamical communities in multilayer spatial interaction networks and examine their spatiotemporal patterns. Random walks are used to merge network layers with different weights, the Leiden technique is used for deriving dynamical communities and exploratory analytic methods are adopted to examine spatiotemporal patterns. To verify our methods, experiments were conducted in Wuhan, China, where trajectory data were used to construct the time-dependent multilayer networks. (1) We derived a set of spatiotemporally cohesive and comparable dynamical communities on each day for one week; (2) They exhibit interesting clustering patterns according to the similarity of their growth curves; (3) They display distinct life courses of occurrence, expansion, stability, contract and disappearance, and their dynamical interactions are vividly depicted; (4) They manifest mixed land use patterns via transfers of human activities. Thus, our methods can enrich research on dynamical organization of urban space and may be applicable in other contexts, while experimental results can provide decision-making support for sustainable urban management.

Architectural style positioning is an important part in the process of residential building design and project planning. However, in practice, due to the complexity and ambiguity of styles, style positioning often relies more on the subjective judgement of the designers and lacks scientificity. This paper proposes a method for the classification and prediction of residential building styles. Through structured interviews and questionnaire surveys on front-line designers and project planners, it refines the key morphological elements and the site economic factors that influence architectural style classification and positioning. Based on machine learning, after analysing the data of 372 newly built real estate projects in Hangzhou, the research finds t|TABE_A_1779728|TABE_A_1779728hat the current real estate styles can generally be divided into 8 categories. Whether it is a curved volume, the shape of the roof and the richness of the tones are the most important morphological variables that differentiate style categories, and the building height is the most important economic factor for style positioning. When using the selected five economic factors as independent variables to train a neural network model and predict the morphological elements and style categories, the average accuracy reaches 77.2%.

Investigating how people perceive Chinese Classical Gardens (CCGs) and their appeal are important issues of landscape studies. By considering the Liu-Yuan Garden, we developed a measurement instrument to investigate the perceived dimensions of landscape appreciation. Based on literature review, the descriptive items to measure how people perceived Liu-Yuan Garden were developed. Then, the explorative factor analysis and confirmatory factor analysis were employed, and six dimensions were extracted and validated including “Adoration”, “Nostalgia”, “Liveliness”, “Exquisiteness”, “Hedonic Value”, and “Placeness”. Finally, the Ordinary Linear Regression method was employed to estimate how the aesthetic appreciation is influenced by these perceived dimensions. The result revealed that four of these dimensions were significantly correlated with aesthetic pleasure. The influences of these perceived dimensions on aesthetic pleasure were measured and compared. The results indicated that “Placeness” provided the most important influence on aesthetic pleasure, whereas “Liveliness” was the least influential of the four related dimensions.