WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 1Citation - Scopus: 2Is Artificial Intelligence a Trustworthy Route Navigation System for Smart Urban Planning(Univ Alexandru Ioan Cuza, Centrul Studii Europene, 2024) Kourtit, Karima; Nijkamp, Peter; Osth, John; Turk, UmutIn the age of smart or intelligent cities, the use of Artificial Intelligence (AI) presents a spectrum of new opportunities and challenges for both the research and policy community. The present study explores the intricate interplay between AI-generated content and actual choice spectra in urban planning. It focuses on the concept of 'city intelligence' and related AI concepts, underscoring the pivotal role of AI in addressing and understanding the quality of life in contemporary urban environments. As AI continues its transformative impact on communication and information systems in the realm of urban planning, this study brings to the forefront key insights into the challenges of validating AI-based information. Given the inherently subjective nature of AIgenerated content, and its influential role in shaping user-perceived value, AI will most likely be a game changer catalyzing enhancements in the urban quality of life and inducing favorable urban developments. Additionally, the study also addresses the significance of the so-called 'Garbage-in Garbage-out' (GiGo) principle and 'Bullshitin Bullshit out' (BiBo) principle in validating AI-generated content, and seeks to enhance our understanding of the spatial information landscape in urban planning by introducing the notion of an urban X'XQ' performance production function.Article Citation - WoS: 5Citation - Scopus: 6Hedonic Price Models, Social Media Data and AI - An Application to the AirBNB Sector in US Cities(Elsevier Sci Ltd, 2025-09) Osth, John; Turk, Umut; Kourtit, Karima; Nijkamp, PeterThe Airbnb sector has experienced exponential growth over the past decade and has led to extensive research in fields such as hospitality sciences, urban geography, tourism economics, and information management. This paper contributes to quantitative research in the Airbnb sector by focusing on the integration of digital platform data at the neighborhood level. It explores innovative methodologies for analyzing urban attractiveness by combining insights from hedonic pricing models with large-scale digital data sourced through AI-based approaches. This novel framework compares user-based valuations of accommodations derived from hedonic pricing with subjective, AI-generated neighborhood descriptions, offering new perspectives on data quality and reliability in information systems. The study also critically examines the challenges of integrating AI-generated content in information science, referencing also 'Garbage-in Garbage-out' and 'Bullshit-in Bullshit-out' concepts. Employing a multi-scalar modeling approach, the research examines Airbnb pricing dynamics across several U.S. cities, starting with Manhattan (USA) as an illustrative case. A subsequent large-scale application to additional metropolitan areas utilizes a combination of hedonic price modeling, social media data, and AI-generated urban descriptions, including a Shapley decomposition analysis. This interdisciplinary integration provides actionable insights into neighborhood attractiveness and pricing mechanisms, while highlighting methodological and empirical contributions to the broader field of information management. By employing the relationship between AI-driven textual data and quantitative modeling, this research provides added value in analyzing urban information systems and their application to digital platforms.