Data Science for Social Impact and Management
Data-Driven Planning for Sustainable Tourism in Portugal
Mass tourism is at a tipping point. High-speed trains and low-cost airlines allow larger amounts of people to travel faster, and more frequently than ever before. Tourism is of great economic benefit to communities worldwide, however many touristic destinations are insufficiently equipped to react to the increasing flux of visitors.
New methods combining data mining, machine learning, and data science are beginning to be used to understand the impact of mass tourism on cities and find solutions to better accommodate tourists. Data Science for Social Impact and Management is a Nova SBE's Research Group oriented to the use of such techniques to tackle key issues with a social and business impact. Throughout this academic year, João Fonseca (junior data science researcher at the research group Data Science for Social Impact and Management) assisted Turismo de Portugal in shedding light on tourism patterns in Portugal, and specifically Lisbon. This work using digital traces created by tourists aims to provide tools that can improve both the management of crowds and the quality experience for tourists and residents alike.
Advisors:
The team:
New methods combining data mining, machine learning, and data science are beginning to be used to understand the impact of mass tourism on cities and find solutions to better accommodate tourists. Data Science for Social Impact and Management is a Nova SBE's Research Group oriented to the use of such techniques to tackle key issues with a social and business impact. Throughout this academic year, João Fonseca (junior data science researcher at the research group Data Science for Social Impact and Management) assisted Turismo de Portugal in shedding light on tourism patterns in Portugal, and specifically Lisbon. This work using digital traces created by tourists aims to provide tools that can improve both the management of crowds and the quality experience for tourists and residents alike.
Advisors:
- • Prof. Leid Zejnilovic (NOVA SBE)
- • Prof. Miguel Neto (NOVA IMS)
The team:
- • Author: João Fonseca (NOVA SBE | NOVA IMS)
- • Project Manager: Lénia Mestrinho (NOVA SBE)
- • Technical Mentor: Qiwei Han (NOVA SBE)
- • Domain advisor: Prof. Margarida Novais (Griffith University)