The internal structure of Greater London: a comparison of national and regional geodemographic models

Abstract

Geodemographic classifications are categorical measures representing salient multidimensional population and built environment attributes of small areas. The UK Output Area Classification (OAC) is one such classification, created on behalf of the Office for National Statistics, and was built with an open methodology and entirely from 2011 Census variables. However, one criticism of national classifications such as OAC is that they do not adequately accommodate local or regional structures that diverge from national patterns. In this paper we explore this issue with respect to Greater London. We develop a London classification based upon the OAC methodology, and explore the extent to which these patterns are divergent from the national classification.

Revisiting the Past: Replicating Fifty-Year-Old Flow Analysis Using Contemporary Taxi Flow Data

“…These reflect a profound transformation in the face of Central and inner London. With the exception of Hampstead, in the 1960s these neighborhoods were, for the most part, solidly middle and lower middle class; today, they are bastions of housing for what the London Output Area Classification calls the "Urban Elite" and "City Vibe" demographics (Singleton and Longley 2015). Ultimately, these contrasts-driven by social, economic, infrastructural, and behavioral factors-help us to grasp the outlines of the underlying transformation of London from declining metropolis to "world city," but so great is the gulf between these two time periods that like-for-like comparison is quite simply no longer possible.…”

Beyond retail: New ways of classifying UK shopping and consumption spaces

“…The final stage in building the classification was to assign labels and descriptions to each of the clusters of this typology. Although there are multiple approaches to this task (Singleton and Longley, 2015), our preferred method was to calculate for each cluster and input variable median values and index scores, computed as a sum of the values per cluster divided by the total sum of the values. By considering variability in these scores, the characteristics of each cluster were compared and descriptions, the so-called pen portraits, of each 'Supergroup' created.…”

Characterizing Volunteered Geographic Information using Fuzzy Clustering

“…Widely used both in social studies and marketing, these classifications are commonly based on census data or surveys, and are constructed through clustering methods such as hard k-means (e.g., Singleton and Longley, 2015), or fuzzy c-means (e.g., Fisher and Tate, 2015). Longley and Adnan (2016) recently applied these clustering methods to data derived from social media.…”