Exploring the spatial patterns of visitor expenditure in cities using bank card transactions data
Bibliographic Data
| ID | 21698672 |
|---|---|
| Authors | Daniel Aparicio (Universidad Complutense de Madrid), María Soledad Hernández Martín-Caro (0000-0002-7430-2197, Dirección General de Economía, Ayuntamiento de Madrid, Madrid, Spain), Juan Carlos García-Palomares (0000-0002-8759-6809, Universidad Complutense de Madrid), Javier Gutiérrez Puebla (0000-0003-2611-8587, Universidad Complutense de Madrid, corresponding author) |
| Year | 2022 |
| Volume | 25 |
| Issue | 17 |
| Pages | 2770-2788 |
| Publication date | 2022-09-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Current Issues in Tourism (JOURNAL) |
| Journal identifiers | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2021.1991898 |
| OpenAlex | W3212571898 |
| Language | EN |
| Citations received | 11 |
| References cited | 38 |
The importance of tourism expenditure is widely recognized, but the spatial patterns of visitor expenditure in cities is so far an unexplored subject. The main aim of this paper is to analyze the spatial distribution of visitor spending using bank card transaction data. Spatial statistics tools are used to identify expenditure clustering patterns and hot spots, distinguishing between national and foreign visitors, and comparing their patterns with those of locals. Our analysis shows that visitor expenditure, and especially that of foreigners, is much more concentrated in the city centre and spatially clustered than local spending. OLS regressions and spatial regression models are used for explaining the spatial distribution of visitor expenditure. Predictors are extracted from old (Cadastre) and new (the social network Foursquare) data sources. Regressions results indicate that socio-symbolic variables have greater explanatory power than physical dimension variables, that expenditure falls sharply with the distance to the city centre, and that the amount of spending in each spatial unit is influenced by the expenditure in the neighbouring spatial units. The paper demonstrates the great potential of this data source to explore with a high level of detail the spatial patterns and drivers of visitor spending within urban destinations
Business · Database transaction · Econometrics · Economics · Geography · Regression analysis · Spatial analysis · Statistics · Tourism · Transaction data · Variables · Visitor pattern · Computer Science · Consumer Retail Behavior Studies · Diverse Aspects of Tourism Research · Sharing Economy and Platforms
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| Unique citing works | 11 |
|---|---|
| Citations per year | 2,75 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |