Smart Tourism Toolkit for
geo-temporal crowd visualization solutions

Rationale

Geo-temporal crowd visualization allows to scaffold the work of destination managers in identifying areas of concern and implementing measures to mitigate the negative impacts of tourism while promoting smarter tourism practices. It allows exploring historical crowd data, visualizing patterns, understanding trends, and predicting future events. It also provides the ability to view near real-time data to support medium- and short-term decision-making. In the context of tourism, for example, it can help identify alternative routes and places to visit that are just as interesting but less crowded, or when the most popular areas should be avoided. Another tourism-related situation is the organization of public mass events, such as music festivals, fireworks, or video mapping shows. Based on data from past event editions, event organizers can use it to plan urban cleaning, policing, and assignment of paramedics, as well as real-time monitoring to detect potential bottlenecks or safety hazards in real-time to implement crowd control measures and ensure the smooth flow of people.

In a nutshell

This STT is a web application for the visual exploration of geo-temporal data. It allows for understanding trends and predicting future events by analyzing crowd density. The data can be historical or real-time, supporting medium and short-term decision-making. For example, this tool allows us to plan alternative routes and places to visit that are equally interesting but less crowded, or when the most popular areas should be avoided.

Features:

Visualize crowding levels, either as absolute values or as density relative to the carrying capacity of the location
Visualize historical or real-time data
Select areas of interest to focus on
Access control mechanisms, so that it can be hosted in the cloud with a public address while maintaining the confidentiality of the data
Extensible connector-based architecture, so it can fetch data from new types of data sources

Short Demo

Long demo

This video showcases the various features of the STT, using real data, in three different configurations: the city of Lisbon (using data from a mobile network), the Iscte campus (using Wi-Fi sensors), and the city of Melbourne (using movement sensors from the city's pedestrian counting system).

[Duration: 20m:29s]