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.Editorial Citation - WoS: 1Citation - Scopus: 2Special Issue on The City 2.0 - Smart People, Places and Planning(Elsevier, 2022-06) Nijkamp, Peter; Kourtit, Karima; Turk, UmutArticle 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: 1Citation - Scopus: 3Effects of External Shocks on the AirBNB Market - Modeling Business Survival Using Geocoded Open Data(Routledge Journals, Taylor & Francis Ltd, 2024-12-11) Turk, Umut; Osth, John; Kourtit, Karima; Nijkamp, PeterThis paper seeks to trace the determinants of business survival in the Airbnb market during the latest pandemic. The paper starts with an examination of the key factors determining survival rates of accommodation listings in the Airbnb market during the early shock phase of the pandemic. The analysis is carried out for 10 metropolitan cities all over the world in both 2019 and 2020, so as to investigate the differences in survival probabilities between the pre- and early-pandemic period. We also study the dynamics of the Airbnb market in the late pandemic period using a multinomial logistic regression model. The results show similar patterns as the pre- and early-pandemic periods, indicating a tendency to return to a pre-pandemic state. In particular, in the pandemic time analyses, distance to nature appeared to be positively associated with firm survival, suggesting the importance of a healthy environment for attracting guests during this period. The findings contribute to our understanding of the effects of the pandemic on short-term rental and highlight the role of various critical background factors.Article Citation - WoS: 5Citation - Scopus: 5Cyclists as Intelligent Carriers of Space-Time Environmental Information: Crowd-Sourced Sensor Data for Local Air Quality Measurement and Mobility Analysis in the Netherlands(Routledge Journals, Taylor & Francis Ltd, 2023-03-30) Kourtit, Karima; Nijkamp, Peter; Osth, John; Turk, UmutIn recent years, slow travel modes (walking, cycling) have gained much interest in the context of urban air quality management. This article presents the findings from a novel air quality measurement experiment in the Netherlands, by regarding cyclists as carriers and transmitters of real-world information on fine-grained air quality conditions. Using individual sensors on bicycles-connected to a GPS positioning system-online local pollution information originating from cyclists' detailed spatial mobility patterns is obtained. Such air quality surface maps and cyclists' mobility maps are then used to identify whether there are significant differences between the actual route choice and the cyclists' shortest route choice, so as to identify the implications of poor air quality conditions for their mobility choices. Thus, the article seeks to present both a detailed pollution surface map and the complex space-time mobility patterns of cyclists in a region, on the basis of online quantitative data-at any point in time and space-from bicycle users in a given locality. In addition, the article estimates their response-in terms of route choice-to detailed air-quality information through the use of a novel geoscience-inspired analysis of space-time "big data." The empirical test of our quantitative modeling approach was carried out for the Greater Utrecht area in the Netherlands. Our findings confirm that spatial concentration of air pollutants have great consequences for bike users' route choice patterns, especially in the case of non-commuting trips. We also find that cyclists make longer trips on weekends and in the evenings, especially towards parks and natural amenities.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: 10Citation - Scopus: 14City Love and Neighbourhood Resilience in the Urban Fabric: A Microcosmic Urbanometric Analysis of Rotterdam(Elsevier, 2022-06) Kourtit, Karima; Nijkamp, Peter; Turk, Umut; Wahlstrom, MiaUps and downs in city life are dependent on the citizens' appreciation for their urban 'home', in particular the neighbourhood liveability. Taking modern research on urban wellbeing and happiness as a point of departure, this study presents and tests a new methodology for assessing the residents' affection for their local neighbourhood. This approach is inspired by the 'city love' concept and seeks to examine and decompose city love through an analytical distinction into the 'body and soul' of the city. Using a rich multi-period and geographically detailed database on neighbourhoods in the city of Rotterdam, including distinct social capital indicators for analysing social resilience in urban areas, a microcosmic decomposition of objective and subjective socio-economic information is carried out. On the basis of geo-science visualisation methods and advanced spatial-econometric techniques for handling neighbourhood autocorrelation effects ('urbanometrics'), a series of explanatory regression analyses is executed in order to identify and explain the determinants of city love at neighbourhood level in Rotterdam. We find that bonding and bridging social capital are prominent in shaping neighbourhood love and social resilience.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.Article Citation - WoS: 5Citation - Scopus: 5A Digital 'Smiley Analysis of the Appreciation for Tourist Amenities by Visitors to London(Springer, 2025-03-28) Kourtit, Karima; Nijkamp, Peter; Osth, John; Turk, UmutDigital wellbeing research is on a rising edge. It is also increasingly applied in the hospitality sector to measure the satisfaction of visitors (metaphorically called here 'smileys'); understanding and enhancing visitor satisfaction are pivotal for the success of tourism destinations. This study seeks to identify critical factors influencing the visitors' appreciation for London, a city renowned for its allure, by harnessing available user data from Airbnb listings and hotels, using online reviews, with a particular view to the spatial pattern of visitors' choices in corona times. Advanced statistical techniques, including sentiment analysis, digital text analysis, multilevel analysis, and geographically weighted regression, are employed to discover geographical patterns as well as statistical correlations between land use, density, geographic location, and visitor contentment. The findings reveal that proximity to parks, accessibility to public transportation, and the presence of natural amenities exert substantial influence on visitor satisfaction in London. Especially, the proximity to a park enhances visitor satisfaction, predominantly in western London. Efficient access to public transportation in central areas of the city positively impacts visitor contentment levels as well. Furthermore, the availability of and accessibility to natural attractions in the southern and southwest areas of London appear to elevate visitor satisfaction. These novel insights empower destination managers, policymakers, and tourism stakeholders to make informed decisions, formulate targeted strategies, and enhance visitor experiences in specific London locales. The research highlights the importance of considering location-specific factors and customizing approaches to optimize the visitor appreciations for a city. By understanding the complex dynamics between land use, density, location, and visitor satisfaction, stakeholders can foster sustainable tourism growth and create a more appealing environment for visitors.