Trajectories of Multidimensional Neighbourhood Quality of Life Change

Abstract

This paper provides an empirical analysis of the multidimensional, spatio-temporal quality of life (QoL) trends followed by neighbourhoods in Charlotte, NC, between 2000 and 2010. Employing a combined geocomputational and visual technique based on the self-organising map, the study addresses which types of neighbourhood experienced the most change or stability, where (in attribute and geographical spaces) did neighbourhoods that began the decade with a particular set of characteristics evolve to, and where did neighbourhoods that concluded the decade transition from? Results indicate that the highest QoL neighbourhoods were most stable, while those with lower homeownership, closer to the city centre, exhibited the sharpest longitudinal trajectories. Lower-income neighbourhoods are found to be heterogeneous in terms of their social problems, dividing between high crime concentrations and youth-related social problems. An exchange of these social issues over time is observed as well as a geographical spread of crime to middle-ring suburbs.

Neighborhood Quality-of-Life Dynamics and the Great Recession: The Case of Charlotte, North Carolina

“…This conclusion supports the view that 'low' quality-of-life neighborhoods are heterogeneous in their corresponding social problems (Delmelle et al, 2013); the improvements observed from the combined index were not driven by any singular dimensions.…”

Mapping collective human activity in an urban environment based on mobile phone data

“…In essence, a SOM is a data clustering and projection procedure that takes input data of multiple dimensions and arranges it on an output space of a lower dimension (most often 2), so that observations that are most similar to one another across the initial variables are located in close proximity to one another on the output space (Skupin and Agarwal 2008). The inherently visual output of this procedure makes it an ideal starting point for exploring temporal variations across multiple attribute dimensions (Delmelle et al 2013).…”

“…In order to construct trajectories of change across this output space, each observation, in this case, geographic cell and corresponding mobile phone attributes, is entered into the SOM multiple times; once for each time stamp. The observation's position on the output space is then traced for each temporal instance to establish a trajectory (Skupin and Hagelman 2005;Delmelle et al 2013). See Figure 4 for an illustration of the procedure.…”

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

“…It has been frequently used to classify and locate, for example, patterns of pesticide contamination in the Asour-Garonne river basin in France [88], to create models of plant location for the treatment of wood residues [89] or regarding the environmental quality of soils [90]. The SOM and GIS have also been used to classify community health based on environmental conditions variables [91] and to show quality of life trends in the neighborhoods of Charlotte (USA) [92].…”