WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Now showing 1 - 5 of 5
  • Article
    Citation - WoS: 22
    Citation - Scopus: 31
    The 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, Peter
    Destination 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.
  • Book Part
    The Geography of Daily Urban Spatial Mobility During COVID: The Example of Stockholm in 2020 and 2021
    (Springer Nature Switzerland Ag, 2023) Shuttleworth, Ian; Toger, Marina; Turk, Umut; Osth, John
  • Editorial
    Modelling Place Attractiveness in the Era of Big and Open Data Introduction
    (Wiley, 2022-08) Osth, John; Turk, Umut; Huang, Jie
  • Article
    Citation - WoS: 6
    Citation - Scopus: 8
    Introducing a Spatially Explicit Gini Measure for Spatial Segregation
    (Springer Heidelberg, 2023-06-14) Turk, Umut; Osth, John
    This paper proposes an alternative measure of economic segregation by income that utilizes the Gini index as the basis of measurement. The Gini Index of Spatial Segregation (GSS) is a ratio of two Gini indices that compares the inequality between neighbourhoods to the inequality between individuals at the macro-level where neighbourhoods are nested. Unlike earlier measures of segregation found in the literature, the GSS uses individualized neighbourhoods, which can be defined as an area constituted within a radius or as a population count method around an individual geo-location, depending on the population density and proximity among individuals in the study area. The GSS can measure residential segregation by any continuous variable for both radii and k-nearest neighbours (knn with and without a decay factor) approaches to bespoke neighbourhoods. Therefore, it is sensitive to the spatial configuration of the area, easy to compute and interpret, and suitable for comparative studies of segregation over time and across different contexts. An empirical application of the index is illustrated using data from Sweden that covers the entire population for 1994, 2004, and 2014. We demonstrate how the definition and scale of the neighbourhood influence the measures of economic segregation. Overall, the GSS offers a flexible and robust framework for measuring segregation that can be used to inform policy decisions and research on inequality.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 18
    How Much Does Geography Contribute? Measuring Inequality of Opportunities Using a Bespoke Neighbourhood Approach
    (Springer Heidelberg, 2019-03-30) Turk, Umut; Osth, John
    To what extent an individual is successful in a variety of outcomes is the result of multiple factors such as (but not limited to) parental background, level of education, discrimination and business cycles. Factors like these also indicate that the success in life can be attributable to factors that both take individual-level merits into account but also to structural factors such as discrimination and contextual effects. Over the last decades, a growing interest in decomposing and categorising factors that affect the life chances of individuals has led to the formation of inequality of opportunity as a research field. This paper builds upon this growing literature, which amounts to quantify the contribution of factors that lie beyond the control of individuals to the total inequality observed in different spheres of life. Using rich Swedish longitudinal register data, we are able to follow individuals over time and their educational attainment during upbringing and later labour market outcomes. In difference from other inequality of opportunity studies, we make use of an egocentric neighbourhood approach to integrate the socio-economic composition of the parental neighbourhood in an inequality model and illustrate its contribution to the total inequality in both outcomes quantitatively. Using multilevel regression analyses, we show that the parental neighbourhood is highly influential in educational attainment and remains so for market outcomes even years after exposure.