Visualizing demographic evolution using geographically inconsistent census data
Fabio Dias, University of Toronto
Daniel Silver, University of Toronto
UT Sociology Working Paper No. 2018-03
March 2018 (updated November, 2018)
Keywords: Human-centered computing; Visualization; Visualization application domains; Visual analytics; Mathematics of computing; Probability and statistics; Statistical paradigms; Exploratory data analysis
Census measurements provide reliable demographic data going back centuries. However, their analysis is often hampered by the lack of geographical consistency across time. We propose a visual analytics system that enables the exploration of geographically inconsistent data. Our method also includes incremental developments in the
representation, clustering, and visual exploration of census data, allowing an easier understanding of the demographic groups present in a city and their evolution over time. We present the feedback of experts in urban sciences and sociology, along with illustrative scenarios in the USA and Canada.
This research was supported by a University of Toronto Connaught Global Challenge grant and is part of the Urban Genome Project.