arXiv subjects

Zhenjiang Shen

Publications and source records attributed to Zhenjiang Shen.

3 recordsLinked to original sources

arXiv2025

Quantifying Key Design Factors for Thermal Comfort in Underground Space Through Global Sensitivity Analysis and Machine Learning

This study identified the key design factors related to thermal comfort in naturally ventilated underground spaces under high temperature conditions (outdoor Tmax = 42.9 C) in Fuzhou, China. Fuzhou has a humid subtropical climate and is one of the three hottest cities in China in 2024. Daytime measurements indicated reduced air temperature (AT), mean radiant temperature (MRT), and wind speed (V), together with elevated relative humidity (RH) in the underground space. Physiological Equivalent Temperature (PET) in the underground was consistently lower during peak hours (08:00-16:00), with the maximum difference in PET between pedestrian and underground levels being 11-11.9 C. Higher pedestrian-level PET at L1 was attributed to reduced greenery and shading, and decrement factors indicated greater thermal dampening at L2 (0.197) than at L1 (0.308). Sensitivity analysis showed that MRT was the most influential factor (S1/ST = 0.59-0.72) for aboveground spaces, followed by AT (0.13-0.26). In contrast, underground PET was mainly affected by metabolic rate (MET) (0.63-0.65), followed by RH (0.14-0.20) and V (0.08-0.18). Partial dependence analysis revealed that a 1 met increase in MET raised PET by 1.6 C, whereas a 1 m/s increase in V reduced PET by 1.5-2.2 C in the underground space. Due to cooler and more stable thermal conditions, underground spaces have higher tolerance for intensive physical activities. By buffering fluctuations in AT and MRT, underground environments can significantly alleviate heat stress and provide passive cooling shelter during daytime heat waves. Overall, this study provides empirical evidence to support underground space design in hot-humid climates and offers insights for sustainable urban heat mitigation.

physics.app-ph↗

arXiv2014

Population spatialization and synthesis with open data

Individuals together with their locations & attributes are essential to feed micro-level applied urban models (for example, spatial micro-simulation and agent-based modeling) for policy evaluation. Existed studies on population spatialization and population synthesis are generally separated. In developing countries like China, population distribution in a fine scale, as the input for population synthesis, is not universally available. With the open-government initiatives in China and the emerging Web 2.0 techniques, more and more open data are becoming achievable. In this paper, we propose an automatic process using open data for population spatialization and synthesis. Specifically, the road network in OpenStreetMap is used to identify and delineate parcel geometries, while crowd-sourced POIs are gathered to infer urban parcels with a vector cellular automata model. Housing-related online Check-in records are then applied to distinguish residential parcels from all of the identified urban parcels. Finally the published census data, in which the sub-district level of attributes distribution and relationships are available, is used for synthesizing population attributes with a previously developed tool Agenter (Long and Shen, 2013). The results are validated with ground truth manually-prepared dataset by planners from Beijing Institute of City Planning.

cs.OH↗

arXiv2014

Big Models: From Beijing to the whole China

This paper propose the concept of big model as a novel research paradigm for regional and urban studies. Big models are fine-scale regional/urban simulation models for a large geographical area, and they overcome the trade-off between simulated scale and spatial unit by tackling both of them at the same time enabled by emerging big/open data, increasing computation power and matured regional/urban modeling methods. The concept, characteristics, and potential applications of big models have been elaborated. We addresse several case studies to illustrate the progress of research and utilization on big models, including mapping urban areas for all Chinese cities, performing parcel-level urban simulation, and several ongoing research projects. Most of these applications can be adopted across the country, and all of them are focusing on a fine-scale level, such as a parcel, a block, or a township (sub-district), which is not the same with the existing studies using conventional models that are only suitable for a certain single or two cities or regions, or for a larger area but have to significantly sacrifice the data resolution. It is expected that big models will mark a promising new era for the urban and regional study in the age of big data.

cs.OH↗