The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

Optimal locations for public facilities such as public parks are significant issues in the urban planning of Larkana city. Therefore, specifically, Larkana city of Pakistan is selected as the study area where the land suitability model was applied to determine suitable land for public parks. This study was carried out within the framework of an Analytic Hierarchy Process (AHP) as a multi-criteria evaluation approach by integrating it with the Geographic Information System (GIS). Decision support system software called Expert choice 11.5 was used to calculate the weights based on three alternative scenarios. Computed composite weights were inserted into the spatial analysis function of GIS and produced three scenarios of suitability maps, i.e.: (a) land availability, (b) land value and (c) population density. Hence, based on the analysis and findings made in this research, finding suitable locations using the land suitability model for future park development is highly helpful. Results can be useful in the planning of public facilities and future land use planning in Larkana city.

One of the ubiquitous human behaviours observed in natural disasters and humanitarian crisis is irrational stockpiling (also known as hoarding or panic buying). Limited, distorted and exaggerated information during crisis disturbs people's judgement and results in aberrant actions which can be explained with economics and psychology theories. The objective of this paper is to examine the perplexing stockpiling phenomena during disasters like COVID-19 pandemic and discuss its immediate and long-term impact on economy, society and local communities.