Geospatial Tools for Determining Visitor Carrying Capacity in Tourist Streets and Public Spaces of Historic Centres

Abstract

This paper presents a methodology for using geospatial tools to enact efficient tourism planning and management in streets and other public spaces in historic centres. The study uses 3D laser scanning, GIS, and spatial data processing techniques to analyse the visitor carrying capacity of streets near cultural attractions. The methodology was tested on Miguelete Street in València (Spain), next to the Cathedral. The results show that these digital tools are efficient and accurate for the spatial analysis of visitor carrying capacity studies.

3D Point Cloud and GIS Approach to Assess Street Physical Attributes

“…On the other hand, when the street is considered as a functional "place", where community gatherings and activities happen, its two-and three-dimensions are typically analysed. For this case, it is possible to mention the studies on open spaces that take into consideration the users and how they interact with the street, such as urban furniture supply [12,[14][15][16], walkable areas [14,15,17,18], crowding dynamics [19,20], street frontage quality [21][22][23], and human scale [16,23], among others. It is necessary to mention that even if streets are analysed in two-or three-dimensional approaches, the results are usually simplified to their linear representation.…”

“…In recent years, a wide variety of approaches and applications have been developed in the field of digital tools and 3D models to address the study of various properties and characteristics [26][27][28][29][30], such as 3D modelling, satellite images, point clouds, and even gamification, allowing users to evaluate physical attributes, such as ground surface and pavement conditions, urban planning, people and vehicular dynamics, and weather conditions, among others. These methods not only allow for the evaluation of physical attributes, such as ground surface and topographical conditions using digital terrain models [20,[31][32][33], but also delve into more detailed aspects of the urban environment being studied, such as the identification of urban furniture, like lampposts, trees, traffic, and pedestrian signs, and so on [34][35][36].…”

“…To obtain the digital terrain model (DTM) map (Figure 5) necessary for further analysis, the resulting surface point cloud was exported as a raster to QGIS, including its elevation and point population density data, as described in related works [20,40].…”