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This paper investigates the life experiences of creative expats and the associated impact of, and on, the built environment of their host localities. Grounded on participants' testimonies, we develop the concept of transient mentality as a potential mediating factor in-between such reciprocal relations. Employing an interview and survey-based research approach and drawing on grounded theory for data collection and analysis in Beijing, China, we found that the prevalence of a transient mentality among creative expats is influenced by the nature of their occupations, the fluidity of their social relationships, and the rapid transformations in the built environment. This transient mentality, in turn, affects the production of the cityscape through the consumption preferences of these expats. We argue that understanding such a transient mentality is crucial for urban planning and cultural policy, particularly in (emerging) global cities that work to brand themselves as international creative hubs. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

Green space accessibility affects housing prices, which is an issue that has been underappreciated. This paper takes Beijing's central urban area as an example. First, the Gaussian-based two-step floating catchment area (G2SFCA) method is used to measure four levels (community green space, subdistrict green space, district green space, and municipal green space) of green space accessibility by multiple travel modes (walking, cycling and driving). Second, the spatial error model (SEM) is used to examine the factors influencing housing prices. Finally, the geographically weighted regression (GWR) model is introduced to explore the spatial relationship between green space accessibility by walking, cycling and driving and housing prices. The results show the following: (1) The SEM shows that only driving accessibility has a positive spatial spillover effect on housing prices, indicating that the spatial inequity of local community green spaces by driving will exacerbate surrounding spatial inequity. (2) The GWR model shows that from the perspective of walking and driving accessibility, most areas within the Second Ring Road show spatial equity in green spaces, while cycling accessibility shows that the spatial inequity of green space exists only outside the Second Ring Road. (3) We find that municipal green space and subdistrict green space most affect the spatial equity of green spaces. Community green space, which is not open to the public, plays a minimal role. Regarding walking and driving accessibility, municipal green space increases the spatial equity of green spaces. In contrast, this type of green space can promote the spatial equity of green spaces regarding cycling accessibility. This study can significantly improve the quality of urban settlements in the Global South and has important implications for policy-makers, planners, and stakeholders in other large cities worldwide to create green and liveable cities. • We investigated green space accessibility impact on housing prices • G2SFCA was used to measure green space accessibility for multiple travel modes • Driving accessibility had positive spatial spillover effect on housing prices • Driving and cycling green space accessibility had opposite effect on housing prices • Our results provided guidance and example for other developing countries megacities. [ABSTRACT FROM AUTHOR]