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Big Data Analytics and Neighborhood Organizational Vitality

Date
Date
Thursday 9 March 2023, 14:00-1500
Location
HYBRID - online booking link below, on campus Maurice Keyworth SR (1.33)
Speaker:
Mingshu Wang

BOOK HERE

Abstract

In this seminar, I will present a recent collaborative case study where big data from street-level images were applied to analyze how the built environment impacts the survival rate of neighborhood-based social organizations in Amsterdam, the Netherlands. These organizations are essential building blocks for social life in urban neighborhoods. Examining these organizations’ relationships with their environment has been a useful way to study their vitality. We combined a deep learning model with elastic net regression to test the relationship between the built environment empirically – distinguishing between car-related, walking-related, and mixed-use land infrastructure – and the survival of neighborhood organizations. Besides revealing the effects of built environment features on the social life between buildings, we want to highlight the value of easily applicable observational big data. Such data and other recently developed methods allow researchers to conduct detailed yet relatively swift and inexpensive studies without resorting to overly coarse or common subjective measurements.

The Speaker

Dr. Mingshu Wang is an Associate Professor of Geospatial Data Science at the University of Glasgow. He is also an Associated Scientist at the 4TU Centre for Resilience Engineering of the Netherlands. His research focuses on developing and applying GIScience methods and big data analytics to understand urban systems and development. He has received research grants from the World Bank, Dutch Research Council, Chinese Academy of Sciences, and Microsoft. He is an Editor of Asian Geographer and an Associate Editor of ISPRS International Journal of Geo-Information, and Regional Studies, Regional Science.