This article explores the potential of geotagged data from social networks to analyse visitors’ behaviour in national parks, taking the Teide National Park as a study area. Given its unique landscape and characteristics, plus the fact that it is the most visited national park in Spain, Teide National Park presents itself as a suitable candidate to explore new sources of data for studying visitors’ behaviour in national parks. Through data from a social photo-sharing website (Flickr) and GPS tracks from a web platform (Wikiloc), we outline several visitors’ characteristics such as the spatial distribution of visitors, the points of interest with the most visits, itinerary network, temporal distribution and visitors’ country of origin. Additionally, we propose a practical use of geotagged data for determining optimal locations for new facilities such as information stands. Results show that data from social networks is suitable to analyse visitor behaviour in protected areas.

Visitor data is essential for decision-making, policy formulation, and monitoring of protected areas. In this context, the data on the temporal distribution of visitors is essential to characterize influx and seasonality, and even to measure the carrying capacity of a site. However, obtaining information from visitors often involves high costs and long production times. Moreover, traditional visitor data has a limited level of detail. New sources of data can provide valuable information regarding the timing of visits. In this study, we tested the use of geotagged data to infer the temporal distribution of visitors to 15 Spanish national parks, and we identified temporal patterns of the visits at three levels: monthly, weekly, and daily. By comparing official monthly visitor counts and geotagged photographs from Flickr, we observed that the number of monthly users who upload photos significantly reflects the number of monthly visitors. Furthermore, the weekly and daily distributions of the Flickr data provided additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors, and improve the overall visitor experience. The results obtained indicate the potential of new data sources for visitor monitoring in protected areas and to open opportunities for future research. Moreover, monitoring tourism in protected areas is crucial to ensure the sustainability of their resources and to protect their biodiversity.

The present study undertakes a scoping literature review on the use of geotagged data in protected areas’ tourism research. We contribute to the literature by providing a detailed exploration of visitor monitoring's main aspects that can be extracted from geotagged data. The revised studies presented various methodological approaches to obtain meaningful information from geotagged data. Their findings outlined new data sources’ potential to assess visitor behaviour and provide visitors’ use indicators. Further, the data nature makes it possible to analyse visitors’ spatial and social behaviour. The literature review allows us to draw conclusions about geotagged data's validity as indicators of visitors in Protected Areas and the benefits and limitations of using such data in research.

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments—geography, ambient population, and street networks—influence criminal through spatial dependence across multiple scales. Despite growing interest in the spatial distribution of crime, few studies have explicitly explored the spatiotemporal dimensions of VAW in Monterrey. This study explores spatio-temporal patterns of VAW in Monterrey, Mexico, based on the analysis of 27,036 georeferenced and verified emergency reports from the 911 system (2019–2022). The study applies kernel density estimation (KDE), the Getis–Ord G_i* statistics, the Local Moran I index, and space–time cube analysis to identify spatial and temporal clusters of VAW. The results show concentrations of incidents during nighttime and weekends, particularly in northern and eastern sectors in Monterrey. The analysis reveals clusters in areas of high socioeconomic vulnerability. VAW in Monterrey follows predictable and cyclical patterns. These insights contribute to the design of tailored public policies and actions to improve women’s health, well-being, and safety in critical zones and timeframes. The findings also enhance international understanding of gender-based spatial violence patterns in the rapidly urbanizing contexts of the Global South.