WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 22Citation - Scopus: 31The Path of Least Resistance Explaining Tourist Mobility Patterns in Destination Areas Using AirBNB Data(Elsevier Sci Ltd, 2021-06) Turk, Umut; Osth, John; Kourtit, Karima; Nijkamp, PeterDestination attractiveness research has become an important research domain in leisure and tourism economics. But the mobility behaviour of visitors in relation to local public transport access in tourist places is not yet well understood. The present paper seeks to fill this research gap by studying the attractiveness profile of 25 major tourist destination places in the world by means of a 'big data' analysis of the drivers of visitors' mobility behaviour and the use of public transport in these tourist places. We introduce the principle of 'the path of least resistance' to explain and model the spatial behaviour of visitors in these 25 global destination cities. We combine a spatial hedonic price model with geoscience techniques to better understand the place-based drivers of mobility patterns of tourists. In our empirical analysis, we use an extensive and rich database combining millions of Airbnb listings originating from the Airbnb platform, and complemented with TripAdvisor platform data and OpenStreetMap data. We first estimate the effect of the quality of the Airbnb listings, the surrounding tourist amenities, and the distance to specific urban amenities on the listed Airbnb prices. In a second step of the multilevel modelling procedure, we estimate the differential impact of accessibility to public transport on the quoted Airbnb prices of the tourist accommodations. The findings confirm the validity of our conceptual framework on 'the path of least resistance' for the spatial behaviour of tourists in destination places.Article Citation - WoS: 13Citation - Scopus: 16Inequality in Leisure Mobility: An Analysis of Activity Space Segregation Spectra in the Stockholm Conurbation(Elsevier Sci Ltd, 2023-07) Toger, Marina; Turk, Umut; Osth, John; Kourtit, Karima; Nijkamp, PeterLeisure mobility forms an important part of people's spatial activity and mobility spectrum. This study aims to analyse the inequality dimensions of spatial mobility of individuals who seek to move to recreational and leisure destinations (often 'green' and 'blue') on designated days. The study traces - through the use of spatially dependent multilevel models - the mobility patterns of people from the greater Stockholm area, using individual pseudonymised mobile phone data and other publicly accessible data. We find significant socio-demographic inequalities in the observed residents' spatial leisure choices, where less affluent groups display especially low variation in mobility when comparing between weekdays, weekends, vacation season and work-periods.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.Article Citation - WoS: 17Citation - Scopus: 24City Love and Place Quality Assessment of Liveable and Loveable Neighbourhoods in Rotterdam(Elsevier Sci Ltd, 2022-08) Kourtit, Karima; Nijkamp, Peter; Tuerk, Umut; Wahlstrom, Mia; Türk, UmutAfter the worldwide interest in global sustainability and climate change challenges, an increasing concern is voiced on local quality of life and neighbourhood liveability. In recent urban studies, human well-being, satisfaction and happiness studies are gaining much popularity in a local context (the 'microcosmic city'). The present study seeks to identify the determinants of the residents' appreciation for their daily environment, called here 'city love'. The latter concept captures both tangible or material aspects of city life ('body') and immaterial and emotional dimensions of local quality of life ('soul'). The present paper seeks to develop and test a new quantitative 'city love' concept, inspired by the soul and body conceptualisation of urban attractiveness for residents and visitors - based on a novel 'feelgood' index (FGI) and a 'human habitat' index (HHI) -, with a view to map out the citizens' contentment or appreciation (called neighbourhood love index - NLI) at a district or neighbourhood scale in the city of Rotterdam. Our study utilises data from a quantitative survey among thousands of residents located in 63 neighbourhoods in this city. In addition, the Rotterdam dataset contains not only survey data, but also register data on these neighbourhoods, e.g., real-estate values, crime statistics, and socio-demographics, while geographical information from OpenStreetMap (OSM) is added as a complement. In addition to a multivariate analysis of the rich data set, the paper employs also a quantile regression analysis extended with fixed effects. The results show that the coefficients of the feelgood index (FGI) and the human habitat index (HHI) decrease slightly as we move up the distribution of the neighbourhood love index (NLI). This means that physical and functional aspects of neighbourhoods, e.g., access to such amenities as public transportation, sport facilities, and also streets with diverse attractions or bikeable and walkable road networks, become more important for the lower end of the distribution of the neighbourhood love index (NLI). Our neighbourhood-specific analyses show that the Rotterdam districts and neighbourhoods differ substantially in many physical and social-emotional respects, which calls for place-based policies and sub-local well-being initiatives.Article Citation - WoS: 35Citation - Scopus: 41AirBNB and COVID-19: Space-Time Vulnerability Effects in Six World-Cities(Elsevier Sci Ltd, 2022-12) Kourtit, Karima; Nijkamp, Peter; Osth, John; Turk, UmutThis study examines the COVID-19 vulnerability and subsequent market dynamics in the volatile hospitality market worldwide, by focusing in particular on individual Airbnb bookings-data for six world-cities in various continents over the period January 2020-August 2021. This research was done by: (i) looking into factual survival rates of Airbnb accommodations in the period concerned; (ii) examining place-based impacts of intracity location on the economic performance of Airbnb facilities; (iii) estimating the price responses to the pandemic by means of a hedonic price model. In our statistical analyses based on large volumes of time- and space-varying data, multilevel logistic regression models are used to trace `corona survivability footprints' and to estimate a hedonic price-elasticity-of-demand model. The results reveal hardships for the Airbnb market as a whole as well as a high volatility in prices in most cities. Our study highlights the vulnerability and `corona echoeffects' on Airbnb markets for specific accommodation segments in several large cities in the world. It adds to the tourism literature by testing the geographic distributional impacts of the corona pandemic on customers' choices regarding type and intra-urban location of Airbnb accommodations.