What is the Strength of the Link Between Objective and Subjective Indicators of Urban Quality of Life?

Urban quality of life is usually measured by either subjective indicators using surveys of residents' perceptions, evaluations and satisfaction with urban living or by objective indicators using secondary data and relative weights for objective indicators of the urban environment. However, rarely are subjective and objective indicators of urban quality of life related to each other. In this paper, these two types of indicators were linked using Geographical Information Systems (GIS) to both locate respondents to the "2003 Survey of Quality of Life in South East Queensland" and also to gather objective indicators about their urban environment within the region with regard to services, facilities and overcrowding. Using Structural Equation Modelling (SEM), the strength of the relationships between these objective indicators and subjective indicators was examined. The results show that relationships between objective and subjective indicators of urban QOL can be weak, and suggests care should be taken when making inferences about improvements in subjective urban QOL based on improvements in objective urban QOL. However, further research is needed into the links between objective and subjective indicators of urban QOL including examining other aspects of the urban environment, non-linear relationships, and moderating effects for individual differences

The canary in the city: indicator groups as predictors of local rent increases

Abstract As cities grow, certain neighborhoods experience a particularly high demand for housing, resulting in escalating rents. Despite far-reaching socioeconomic consequences, it remains difficult to predict when and where urban neighborhoods will face such changes. To tackle this challenge, we adapt the concept of ‘bioindicators’, borrowed from ecology, to the urban context. The objective is to use an ‘indicator group’ of people to assess the quality of a complex environment and its changes over time. Specifically, we analyze 92 million geolocated Twitter records across five US cities, allowing us to derive socio-economic user profiles based on individual movement patterns. As a proof-of-concept, we define users with a ‘high-income-profile’ as an indicator group and show that their visitation patterns are a suitable indicator for expected future rent increases in different neighborhoods. The concept of indicator groups highlights the potential of closely monitoring only a specific subset of the population, rather than the population as a whole. If the indicator group is defined appropriately for the phenomenon of interest, this approach can yield early predictions while simultaneously reducing the amount of data that needs to be collected and analyzed

Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Census Tracts

Contextual research on time and place requires a consistent measurement instrument for neighborhood conditions in order to make unbiased inferences about neighborhood change. We develop such a time-invariant measure of neighborhood socio-economic status (NSES) using exploratory and confirmatory factor analyses fit to census data at the tract level from the 1990 and 2000 U.S. Censuses and the 2008–2012 American Community Survey. A single factor model fit the data well at all three time periods, and factor loadings—but not indicator intercepts—could be constrained to equality over time without decrement to fit. After addressing remaining longitudinal measurement bias, we found that NSES increased from 1990 to 2000, and then—consistent with the timing of the “Great Recession”—declined in 2008–2012 to a level approaching that of 1990. Our approach for evaluating and adjusting for time-invariance is not only instructive for studies of NSES but also more generally for longitudinal studies in which the variable of interest is a latent construct