Visualizing demographic evolution using geographically inconsistent census data

Abstract

Video Abstract: https://youtu.be/bKeV08Os0uA 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.

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

“…The characterization of clusters for time-space analysis is not simple (Vickers & Rees, 2007). The literature reveals that there are several approaches to characterize clusters, among which are the analysis of the most relevant indicators (Dias & Silver, 2018), the global index of indicators (Singleton et al, 2016), the relative strength of indicators (McLachlan & Norman, 2020), the centroid values of the clusters (Vickers, 2010) and the averages of the cluster indicators (Li & Xie, 2018).…”

“…The lack of consensus on which approach to use suggests that cluster characterization should be based on a combination of these approaches. First, it is possible to identify the most relevant indicators to characterize each cluster through a box plot (Dias & Silver, 2018). Second, the relative strength of the indicator obtained by the standardized indicators' signal allows for recognizing whether the indicator is positive or negative in the cluster's characterization (McLachlan & Norman, 2020).…”