Comparing spatial and content analysis of residents and tourists using Geotagged Social Media Data. The Historic Neighbourhood of Alfama (Lisbon), a case study

Abstract

Tourism flows to large cities have increased drastically in the past few years. The Alfama neighbourhood in Lisbon (Portugal) is facing major changes with respect to land uses, demographic features and social appropriation patterns in public spaces, caused by the intensification of tourism. The consequences of new emerging economic and symbolic values have rapidly given rise to a scenario of touristification and gentrification in the neighbourhood. In order to address such complexities, sustainable urban planning can benefit from real-time data sources that can represent the tourism flows in spatial and temporal perspectives. The research question allows Twitter to be used as an emerging data source and for analysing spatial patterns and content, based on two sample groups: residents and tourists, and their interpretations about the use of space for leisure activities. The research method is based on an analysis of two years of geotagged tweets in the city of Lisbon, differentiating between tourist and resident users, and, in a subsequent step, in the Alfama neighbourhood. The spatial distribution analysis and the content analysis have revealed not only spatio-temporal activity patterns but also emotional responses to new trends in urban tourism uses, consumption and perception of an increasing tourism pressure in Alfama. The results are relevant in the field of tourism and sustainable urban planning.

رصد البصمة الرقمية السياحية ومراكز الأنشطة السياحية من خلال بيانات التوسيم الجغرافي والبيانات المحددة للموقع: مدينة الإسكندرية كدراسة حالة

“…This data originates from systems with which users frequently interact in their daily routines. These systems include social media platforms, mobile phone networks, online search engines, various sensors, wearable technology, GPS tracking, water and electricity usage records, weather information, video recordings, and credit card transactions (Reif & Schmücker, 2020;Yubero et al, 2021). Analyzing these datasets enables us to gain a deeper insight into our interactions within urban environments (Waiyausuri et al, 2023;Gao et al, 2024).…”

“…It is one of the most prevalent sources because tweets enable free, geotagged, real-time information. (Encalada-Abarca et al, 2024), thus valuable information on the tourist footprint can potentially be retrieved from tweets and sender profiles (Yubero et al, 2021). Actually, using Twitter's API (application programming interface) is the primary method for obtaining data from the social media platform for scientific purposes (Provenzano et al, 2018;Sontayasara et al, 2021).…”

“…These studies range from the segmentation of tourist markets using user-generated content (UGC) to exploring the behaviors within online tourist communities. Scholars such as Li et al (2018), Yubero et al (2021), andNolasco-Cirugeda et al (2022) have delved into this phenomenon. Social media platforms have effectively engaged tourists, offering them a space to exchange insights and information concerning their travel experiences as well as finding sophisticated method to illustrate individuals' spatiotemporal preferences and mobility patterns (Yu et al, 2023).…”

“…Moreover, this work offers a scientific foundation for enhancing and managing tourism spaces, traffic, and service facilities in specific regions (Gu et al, 2023). This approach can also contribute to orientating urban land-use planning (Mou, 2022) and formulating tourism strategies that align with urban revitalization and regeneration (Yubero et al, 2021) to address imbalances in various areas, thus mitigating issues like over-tourism (Nolasco-Cirugeda et al, 2022), gentrification, and uneven distribution of tourist flow towards specific destinations.…”

Comparing the Spatial Distribution of Popular Destinations in the Era of Mobile Internet: Case Studies of 9 Cities in the Pearl River Delta

“…More and more scholars describe and visualize the spatial distribution of popular destinations with the help of spatial data analysis and network analysis to detect hotspot spaces and give spatial planning suggestion [11,12] . The spatial distribution of popular destinations has been mostly analyzed using geospatial analysis to reveal the spatial distributions of tourists, the mobility of urban tourism, and the interaction between visitors and popular destinations [13]. Some research have paid attention to the spatio-temporal behavioral patterns of visitors, and they widely recognize punch card data and digital footprint data as more important and credible data in related studies, using hotspot analysis, spatial clustering, and other methods to analyze the spatial patterns [14].…”