Exploring Urban Shrinkage via Computational Approaches: A Case Study of Detroit

Keywords: urban shrinkage, agent-based model, Detroit
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 03:40 PM (Eastern Time (US & Canada)) - 2/26/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 46

Abstract

While the world’s total urban population continues to grow, not all cities are witnessing such growth, some are actually shrinking. This shrinkage causes several problems to emerge including population loss, economic depression, vacant properties and the contraction of housing markets. Such problems challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom up using dynamic computational models and social media analysis. To further explore on this phenomenon, this work mainly focuses on three main questions: 1) To what extend can urban shrinkage be raveled by applying social media analysis (i.e., sentiment analysis)? 2) How urban shrinkage emerges at the macro-level through the simulation of housing trades at the individual level? 3) How can patterns of shrinkage be measured through social media analysis and simulation?