Forecasting Museum Soft Power: Predicting Attraction Power of Traveling Exhibitions | #MW20-2gg

Natalia Grincheva, National Research University "Higher School of Economics", Moscow, Russia


This demo will showcase the advances of the research creation project that employed geo-visualization and data mining to explore  the geography of museum soft power. The power of museums to attract tourism and facilitate urban regeneration is well-recognized, with the Bilbao effect as the most symbolic example. However, nobody has attempted yet to understand how the “attraction” power of museums works. Challenged by this question, an experimental research project, Deep Mapping: Creating a Dynamic Web Application Museum Soft Power Map, developed in the Digital Studio at the University of Melbourne, offered a solution. This collaborative practice-based creative research piloted a demo version of the first in the world digital mapping system. This system measure attraction power of museums and visualize it on the global map. Developed in collaboration with the Australian Centre for the Moving Image (ACMI), the project designed the digital platform that exposes and explores correlations in geographical layers of museum data on several levels, including collections, online and onsite audiences, international activities and constituencies. In 2019 the project was nominated and became GLAMi Awards finalist of the MuseWeb. This year I will demonstrate how the project was developed further to design new measurement system that can predict and forecast museum soft power.   In 2019 the unique Museum Soft Power Map acquired a new layer, the Local Engagement Power Forecast, which predicts the soft power of the ACMI international blockbusters. This layer forecasts the soft power of the traveling exhibition “Wonderland” in potential hosting cities across continents.

Keywords: AI in museums, geo-visualization, digital mapping, museum soft power, traveling exhibitions, measuring impact

Forecasting “attraction” power of traveling exhibitions: download PPT here

Cite as:
Grincheva, Natalia. "Forecasting Museum Soft Power: Predicting Attraction Power of Traveling Exhibitions | #MW20-2gg." MW20: MW 2020. Published April 2, 2020. Consulted .