The quantitative examination of neighborhood quality of life has traditionally focused on individual indicators and their level of occurrence, such as number of crimes and how much they rise or fall over time. While this approach can help to identify a neighborhood’s problems, it does not contribute to an understanding of the way indicators of quality interact with each other and change over time. This paper explores the use of analytic methods that are better suited for assessing neighborhood change, focusing on the rate at which changes occur — referred to as "trajectories" in this paper — as opposed to changes in magnitude. It seeks both to identify general correlations between indicators and to explore how these relationships are affected by neighborhood contexts and by conditions that originate outside the neighborhood, such as the collapse of the housing market in 2006 and the national recession that followed.

The paper draws on data from an evaluation of the New Communities Program (NCP), an ambitious, 10-year effort to improve conditions in distressed urban neighborhoods of Chicago. Using a longitudinal database, the paper explores analyses of the interactions among quality-of-life indicators in the neighborhoods where NCP is working and in other Chicago neighborhoods. It looks at these interactions over time in three domains of interest to the initiative — safety, housing, and the economic environment — focusing in particular on business cycle fluctuations, the economic downturn, and the association between home foreclosures and crime. The analyses show that very few indicators were strongly or moderately correlated at the same point in time, but many of them had "leading" or "lagging" relationships with others. For instance, completed foreclosures were found to lead, or precede, crime. More completed foreclosures during a year were followed by both an increase in the level of neighborhood crime and a slowing down of the decline in crimes, especially violent crime. The findings suggest the importance of considering the time-sequencing of changes in neighborhood quality-of-life trajectories and using a comprehensive framework that relies on longitudinal data. Given that there are few examples of this type of work in the literature, these exploratory analyses offer important insights for understanding associations between and among neighborhood quality-of-life indicators over time and the methods to study them.