Exploring tourist spatiotemporal behavior differences and tourism infrastructure supply–demand pattern fusing social media and nighttime light remote sensing data

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

“…In line with earlier studies, the present research addressed the findings of Salas-Olmedo et al (2018) and Martí et al (2021), which suggested that many sources should be considered in conjunction when appraising the concentration of tourism-related activities and their urban spatial patterns can be portrayed in a way that incorporates user experiences and opinions into account through the use of LBSNs (as data sources). Additionally, the results support and validate other studies that contend Big Data derived from UGC provides a multitude of opportunities for addressing tourism-related phenomena (Martí Ciriquián et al, 2019;Gao et al, 2024). In particular, the reference framework provides different forms of analysis-spatial, clustering, and network-for identifying and analyzing TAC areas according to their functional variety.…”

“…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). Big data is distinct from traditional data on several levels.…”

“…Meanwhile, Vu, Li &Law (2015) pointed out that researchers examine the behavior of inbound tourists by using geotagged photos. Furthermore, social media has emerged as a major source of highly accurate and instantly updated geographic data (Gao, 2024) that is referred to as "digital footprints" (Girardin et al 2008). In fact, tourists leave a digital "footprint" in most of their activities in the cities they visit (Wu, 2022).They use bank cards to make payments, snap vast numbers of photographs and post them to photo-sharing websites, communicate on social media and mobile devices (Cebrián & Domenech, 2023).…”

“…By "checking-in" at a particular area, foursquare users may share not only their location but also their thoughts and experiences regarding that particular spot (Mondo et al, 2020). In tourism researches, Foursquare has also been used to rank preferences (Tammet et al, 2013) and identify popular tourist destinations or activities (Gao et al, 2024). Fifthly, The TripAdvisor website stands out as a prominent platform for user-generated content, significantly impacting the decision-making process of prospective travelers (Nolasco-Cirugeda et al, 2022).…”

“…Consequently, it becomes possible to recognize the venues preferred by tourists, enabling the verification of urban areas that attract tourists' attention and providing guidance for the city's development based on patterns of user activity (Nolasco-Cirugeda et al, 2022). Thirdly, the proposed reference framework incorporates LBSN data, serving as a supplementary tool for urban planning and decision-making processes aimed at improving city urban dynamics (Gao et al, 2024) and to achieve organized tourism development of cities supporting the wellbeing of current and future tourists flow (Sedarati, 2019).…”