Tuuli Toivonen, University of Helsinki
Artificial Intelligence for Mobility Studies in Urban And Natural Areas
Abstract: Understanding how people use and move in space is important for planning, both in urban and natural areas. Recent research has shown that location-based social media data may reveal spatial and temporal patterns of the use of space, and reveal areas where human activities might be detrimental. We have shown that social media data corresponds to real-life spatial and temporal patterns of visitors in national parks and is able to bring light to use of space in cities, by providing meaningful information about the activities and preferences of people. The overwhelming magnitudes of social media data require special filtering and cleaning and tested analyses approaches. We are now using geospatial analysis methods together with machine learning to understand where, when, how and by whom areas are being used and how people and goods move about and why. Automated text and image content analysis is needed to leverage the full potential of social media data in spatial planning. Also new applications are yet to be discovered.
Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.
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Last updated on 27 Feb 2018 by Teemu Roos - Page created on 27 Feb 2018 by Teemu Roos