Cellular automaton (CA) is an important method in land use and cover change studies, however, the majority of research focuses on the discovery of macroscopic factors affecting LUCC, which results in ignoring the local effects within the neighborhoods. This paper introduces a Local Land Use Competition Cellular Automata (LLUC-CA) model, based on local land use competition, land suitability evaluation, demand analysis of the different land use types, and multi-target land use competition allocation algorithm to simulate land use change at a micro level. The model is applied to simulate land use changes at Jinshitan National Tourist Holiday Resort from 1988 to 2012. The results show that the simulation accuracies were 64.46%, 77.21%, 85.30% and 99.14% for the agricultural land, construction land, forestland and water, respectively. In addition, comparing the simulation results of the LLUC-CA and CA-Markov model with the real land use data, their overall spatial accuracies were found to be 88.74% and 86.82%, respectively. In conclusion, the results from this study indicated that the model was an acceptable method for the simulation of large-scale land use changes, and the approach used here is applicable to analyzing the land use change driven forces and assist in decision-making.

This study used global land-use data interpreted from remote sensing images to quantitatively analyze the spatial and temporal changes in global urban expansion over the past 20 years, as well as the source, rate of expansion, and urban growth patterns of newly added urban land (NAUL) around the world. Some main conclusions included the following. (1) Globally, NAUL was mainly derived from agriculture, grassland, and forest. These three types of land use contributed 68.93%, 10.10%, and 9.76%, respectively, to the land sources for NAUL. (2) Eight countries/regions (CRs)—India, Pakistan, Nigeria, Bangladesh, Philippines, Ethiopia, Egypt, and Vietnam—had significant potential for future urban growth and were designated as the “Emerging Urban Growth G8.” Also, Africa will continue to lead global urbanization after Asia. (3) Global urban expansion was still in a typical stage of edge expansion. Urban expansion in Oceania was the most aggregated, whereas in Asia, it was the most diffuse. (4) Apart from African CRs, the urban expansion rate in most CRs was higher than the population urbanization rate, so urbanization does not pose a significant threat to global food security. In addition, for CR with NAUL>1,000 km², the level of economic development had a positive effect compact urban development. This study mapped large-scale urban expansion using unified data, a unified definition of urban boundaries, and over a unified time span.

The correct urban building layout is an important influencing factor in urban ventilation, and the heat island effect has become an important factor affecting the quality of urban life. Optimization of the urban building layout can play a role in mitigating the heat island effect. The traditional ventilation corridor analysis method, based on a least-cost path analysis, can only generate a few main ventilation corridors. It is difficult to obtain global ventilation results covering the whole study area using this method of analysis. On the basis of urban morphology and a least-cost path analysis, this study proposes a “least cumulative ventilation cost” method for analyzing urban ventilation. Taking Wuhan downtown as a research area, the urban ventilation environment under different wind directions and seasons was analyzed. This method can effectively express the ventilation conditions throughout the whole study area and can simultaneously express the quality of the generated corridors effectively. The results show that Wuhan has three levels of ventilation corridor. Moreover, the ventilation conditions in Wuchang (Wuchang, Qingshan, and Hongshan) are better than those in Hankou (Qiaokou, Jianghan, and Jiang’an).

Urban ventilation corridors play a critical role in improving wind environments, mitigating the urban heat island (UHI) effect, and enhancing urban climate resilience. Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100–2000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design.

With the increase in urban development intensity, the urban climate has become an important factor affecting sustainable development. The role of urban ventilation corridors in improving urban climate has received widespread attention. Urban ventilation identification and planning based on morphological methods have been initially applied. Traditional morphological methods do not adequately consider the dynamic process of air flow, resulting in a rough evaluation of urban ventilation patterns. This study proposes a new urban-scale ventilation corridor identification method that integrates the Lattice Boltzmann method and the K-means algorithm. Taking Wuhan, China as the research area, an empirical study in different wind directions was conducted on a 20 m grid. The results showed that three levels of ventilation corridors ($245.47 {\mathrm{k}\mathrm{m}}^2 \mathrm{in} \mathrm{total}$) and two levels of ventilation obstruction areas ($658.09 {\mathrm{k}\mathrm{m}}^2 \mathrm{in} \mathrm{total}$) were identified to depict the ventilation pattern of Wuhan’s central urban area. The method proposed in this study can meet the needs of urban-scale ventilation corridor identification in terms of spatial coverage, spatial distribution rate and dynamic analysis. Compared with the classic least cumulative ventilation cost method, the method proposed in this study can provide more morphologic details of the ventilation corridors. This plays a very important role in urban planning based on urban ventilation theory.