The detection of focal points of tourism overcrowding in European cities. A multi-scale perspective

Overcrowding has become a common problem in European cities. The study of spatiotemporal behaviour with advances in tracking technology and big data offers possibilities for managing excessive tourism in urban destinations. However, there is a shortage of theoretical and methodological models that address this issue. The main objective of this study is to create a theoretical-methodological framework based on core-periphery theories, time geography and crowd density that detects hotspots of overcrowding in European destinations. The aim is to manage this problem through hierarchical spatial modelling, space-time study using a multi-scale approach and the use of location intelligence and big data. The methodology used is based on location intelligence through spatial analysis and the use of a combination of different technologies—user data, mobile phone data, and Wi-Fi sensors—and geographic information systems (GIS). The model also uses Exploratory Spatial Data Analysis (ESDA) and Kernel Density Estimation (KDE) for overcrowding management. The results obtained mark a new roadmap for modelling spatio-temporal behaviour using big data and its applicability for managing flows and mitigating the impacts of excessive tourism in European historic centres.