Tourists' digital footprint in cities: comparing big data sources (1705.07951v1)

Abstract: There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data (Big Data) when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected). Tourist density in the three data sources is compared via maps, correlation analysis (OLS) and spatial self-correlation analysis (Global Moran's I statistic and LISA). Finally the data are integrated using cluster analysis and combining the spatial clusters identified in the LISA analysis in the different data sources. The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces (with several activities) and spaces specialising in one or various activities (for example, sightseeing and consumption). The case study analysed (Madrid) reveals a significant presence of tourists in the city centre, and increasing specialisation from the centre outwards towards the periphery. The main conclusion of the paper is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.

• The paper compares Panoramio, Foursquare, and Twitter big data sources to analyze the spatial behavior of urban tourists in Madrid.
• Analysis reveals distinct spatial patterns and complementarity among data sources, showing where tourists engage in sightseeing, consumption, or connectivity.
• Findings underscore the necessity of a multi-data-source approach for comprehensive insight into tourist footprints, aiding urban planning and policy.

Analysis of Urban Tourists' Digital Footprints Using Big Data Sources

The paper "Tourists' Digital Footprint in Cities: Comparing Big Data Sources" presents a comprehensive paper on the spatial behavior of urban tourists through the lens of diverse Big Data sources. The research uniquely leverages three distinct types of data—Panoramio for sightseeing activities, Foursquare for consumption, and Twitter for connectivity—to depict a multifaceted view of tourist activity within the city of Madrid. The paper meticulously pursues the integration and cross-comparison of these data sources to provide insightful correlations and spatial characterizations of urban tourist spaces.

Traditional data sources, such as surveys or hotel records, offer limited insights into the intricate spatial distributions of tourists within urban settings. This paper harnesses the power of geolocated Big Data, characterized by high volume, velocity, and variety, to highlight tourists' footprints while addressing the temporal and spatial patterns of their activities. Specifically, the analysis utilizes geolocated photographs from Panoramio, Foursquare check-ins, and geolocated tweets from Twitter to identify dense areas of tourist activity and discern between multifunctional and specialized tourist spaces.

The analysis reveals several notable patterns. Tourist densities, derived from these datasets, show high concentrations predominantly in the historic center of Madrid, aligning with expected attractions such as museums, monuments, and eateries. However, discrepancies emerge upon further examination. The spatial distribution of the digital footprints highlights complementarity among the sources; for instance, Foursquare captures consumption activities, which Panoramio's sightseeing-focused data overlooks, while tweets from Twitter reflect tourists' connected experiences often linked to their accommodations.

Statistical approaches, including correlation analysis, spatial self-correlation using Global Moran's I statistic, and cluster analysis, have been employed to scrutinize these patterns. Although moderate positive correlation exists between Twitter and Foursquare data sources, indicating overlap in tourist activities, the correlation between Panoramio and the other two sources remains low, suggesting varying utility and focus. Cluster analysis distinguishes areas of the city according to tourism activities: some tracts depict high sightseeing-related tourism activity, while others show a propensity for consumption or connectivity.

Significantly, the authors argue against the singular use of one data source for analyzing the spatial behavior of tourists, advocating for a multi-data-source approach to uncover layered insights. This multi-source methodology allows for comprehensive characterization and possible application in urban planning and policy development. Understanding tourists' spatial patterns can steer enhancements in infrastructure, such as pedestrian pathways and public Wi-Fi extensions, and illuminate business opportunities for targeted expansion in underutilized areas such as Madrid Río.

The paper also confronts inherent data biases that can stem from the nature of Big Data sources, particularly those related to user behavior on digital platforms. By focusing on the density of tourists, in contrast to the density of their digital interactions, researchers mitigate bias related to differing user engagement levels across platforms. This practice provides a refined view of tourist distributions, counterbalancing the influence of "superusers" who may flood the data with disproportionate activity footprints.

In summary, this research underscores the importance of employing multiple Big Data sources to paint a detailed picture of urban tourist behavior, offering both methodological advancements and practical insights. Future research could extend these analyses to a broader range of cities and explore new data sources as they emerge. The insights provided facilitate improved urban management strategies and enhance the sustainability of tourism as a critical economic sector in city landscapes.