## 1 Introduction
Moreover, the article underlines the potential of artificial intelligence as a forecasting system to predict natural disasters using high-quality datasets for a deeper understanding of large-scale social phenomena. They stress the contribution of digital twins to strengthening the socio-economic fabric of society and its capacity to enhance resilience by assisting adaptation efforts to meet the challenges of risk management in a dynamically changing environment. The utilization of location-based data for city analysis has garnered significant attention as a promising method for applied research [15]. Ref. [15] explores challenges, opportunities, and limitations in collecting geodata from various platforms for diverse cities in urban phenomena research. It outlines a comprehensive method for requesting and retrieving data from APIs, emphasizing the need for meticulous review and verification steps to capture nuances in geolocated user-generated data. The study highlights the ongoing trend of using geodata in city studies, offering qualitative insights more promptly than traditional sources. At the same time, the article addresses challenges such as reliance on third-party platforms, emphasizing the importance of validating geo-location-based data across large datasets for representative sampling. Proper handling of API requirements is critical to the reliability and validity of data in urban research. The methodology introduced in Ref. [16] provides a holistic perspective on urban spaces, surpassing conventional constraints. Using geodata processing and semantic segmentation models, the approach assesses longitudinal and cross-sectional aspects, deriving key metrics like the green coverage rate and green view index. Osaka City in Japan serves as the exemplar, revealing an uneven distribution of green spaces and variations in quality. The study calls for a multidimensional approach rooted in the geographical context and relevant characteristics, emphasizing the opportunities presented by these technological advancements and street view data for informed urban planning. The literature review in Ref. [17] delves into mobility-oriented urban studies, analyzing 400 papers to underscore the dynamic nature of mobility data. It emphasizes the crucial role of mobility data in understanding human mobility patterns and intricate interactions between individuals and urban environments. Highlighting big geodata's significance, the paper emphasizes its potential as a valuable resource, empowering researchers to unravel complex knowledge and innovate applications. Mobility data allows tracing movement trajectories, characterizing daily activities, and gaining holistic insights into human mobility across diverse urban scenarios, emphasizing a cyber-physical-social perspective. Ref. [18] addresses the limited exploration of citizens' perspectives on the impact of smart city development on their quality of life. Adopting a citizen-centric approach, the study examines attitudes, perceptions, and support for smart city development. Through a quantitative approach involving a questionnaire survey with 848 Macao citizens, the study identifies domains affecting citizens' attitudes and support for citizen-centric smart city development. Factor analysis, multiple linear regression, and cluster analysis reveal the need for greater focus on smart services that enhance livelihoods and mobility. The researchers emphasize reconsidering citizens' lifestyles and promoting a transition to smarter support by leveraging smart technologies to streamline and enhance public services. Recognizing the importance of citizens' quality of life in a smart city, the study suggests adjusting strategies based on citizen feedback for more effective citizen engagement. The researchers in Ref. [19] emphasize the early stage of exploring smart city user preferences.