Measuring visitor flows using mobile positioning data in three Hungarian second-tier cities

Abstract

Purpose The research analyses the spatial-temporal behaviour of international visitors using mobile positioning data (MPD) in three second-tier cities in Hungary: Szeged, Debrecen and Pécs. The purpose of this study is to identify the origins of visitors, length of stay, seasonal concentrations and mobility between cities. Design/methodology/approach A large volume of data generated by foreign mobile phone users was processed and analysed for six consecutive months in 2018. The movements of around 3.5 million foreigners visiting Hungary were captured. Findings The data showed significant differences in tourist turnover, mobility patterns and flows indicating different levels of tourism activity and potential in the three cities. This included day trips, cross-border tourism, seasonal fluctuations and activity-driven demand (e.g. special events and cultural tourism). Research limitations/implications MPD cannot fully explain or predict drivers of visitor behaviour, such as purpose of visit, the precise activities that they undertake there and the experiences that they gain. It can, however, provide information on spatial and temporal flows, patterns and concentrations of visitors. Practical implications The investigated cities should follow distinct tourism development policies to attract more tourists and strengthen the links with their neighbouring destinations at the same time as differentiating themselves through marketing as competitive alternative destinations. Social implications Augmenting tourism has socio-economic implications for residents and communities. The development of cultural tourism and local event-based tourism must incorporate community needs. Originality/value The paper analyses the extent to which MPD can provide insights into visitors’ spatio-temporal mobility and flows in relatively under-visited second-tier cities and it highlights opportunities and gaps for big data research in an urban context.

Identifying the Spatial Patterns and Influencing Factors of Leisure and Tourism in Xi’an Based on Point of Interest (POI) Data

“…Traditionally, urban leisure and tourism spatial data were mostly obtained through case studies [20], participatory observation [21], in-depth interviews [35], and other methods. With the advent of the big data era, some open platforms now provide big data, such as digital footprints [36], mobile positioning data [37], geotagged photos [38], and point of interest (POI) data [39], which have changed the original paradigms around geospatial data acquisition and research methods [40]. It also provides an opportunity for the accurate measurement of urban leisure and tourism space.…”