Professor Alex Singleton

Geodemographic classifications organize areas into categories sharing similarities across multiple socioeconomic attributes. These classifications have either a national extent or localized focus (e.g., a region) and are built to describe the generalities of places or to examine the geography of specific domains of interest (e.g., health). Within a geodemographic typology, each cluster is identified from a distinctive collection of attributes; for example, wealthy neighborhoods, where most households comprise older individuals living within apartments. Clusters are typically named by the classification builder (e.g., Elderly Suburbs) and are accompanied by rich media descriptions.

Urban Analytics is a broad interdisciplinary field that focuses on the use of data to study cities. The economic and political situation of cities has shifted in recent years, as have the data economies within which they operate. These new economic and political challenges include: rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization (and displacement), and how to utilize data from “smart” infrastructure. While these changes have been underway for decades, the availability of real time urban data potentially transforms both the study and management of cities. Over the past 5 years the way that data are used to understand urban systems has changed dramatically.