Measuring Neighborhood Change
Measuring Neighborhood Change: Dynamic Diversity and Urban Demographics
by Anise Vance
In 1964, British sociologist Ruth Glass, in an attempt to succinctly describe the process by which lower-income Londoners were squeezed out of their traditional neighborhoods, coined the term “gentrification.” Since, charged political meanings have infused Glass’ linguistic invention with much controversy. More widely, neighborhood change, the overarching geographic phenomena under which gentrification falls, has become a research topic of great interest to academics, policymakers, and city residents alike: few American cities have not experienced some form of “redevelopment” or “revitalization.”
How, then, can scholars and practitioners accurately assess the makeup of neighborhoods at which their policies are aimed? Over the last decade, novel methodologies used in studying neighborhood change and segregation have offered greater insight into urban demographics. In particular, multi-scalar and neighborhood classification techniques have addressed significant concerns in traditional segregation analyses. Those techniques have inspired scholars to introduce the term dynamic diversity to describe the instability of multi-racial and multi-ethnic neighborhoods in modern American cities. To understand the complexity of the term, understanding its roots in nuanced research approaches to segregation is critical.
Multi-scalar studies measure segregation across geographic scale, as opposed to focusing on only one unit of space. For example, instead of measuring the isolation of a black population across census blocks, a multi-scalar analysis might measure that isolation in radii of 1, 3, and 5 miles from individual locations. In this way, multi-scalar approaches allow for comparative readings of segregation in smaller chunks of space that replicate real neighborhood conditions, not fixed geographic units.
Neighborhood classification techniques place neighborhoods in typologies and track changes in those typologies instead of changes in the residential populations themselves. For example, a typical study might look at neighborhood change across a twenty-year span according to the dissimilarity index. A classification techniques study would use multiple indices relevant to the local population to create a city-wide typology of neighborhoods and then track neighborhoods as they shift between types. Classification techniques are thereby able to quickly, and more accurately, convey demographic trends.
A new study, recently published in the Annals of the Association of American Geographers, combines multi-scalar and neighborhood classification techniques. By doing so, it mostly avoids the drawbacks of using fixed geographic units (those units may be unrepresentative of residents’ actual neighborhood patterns) and uni-index studies (reliance on one generalized index may miss local nuances). In applying their techniques to Los Angeles, the study’s authors, Clark et al., made discoveries as widely significant as their methodology.
Finding that Los Angeles is characterized by dynamic diversity, Clark et al. use the term to indicate “that diversity is not in general a stable state but instead neighborhood populations are often in a state of transition.” As they discuss, some neighborhoods that fit under that description are moving from low to high diversity while others are moving in the opposite direction. The point, however, is that in their highly nuanced and localized examination of neighborhood change, Clark et al. found that diversity is rarely a stable state.
A litany of questions must then follow: are some diverse neighborhoods simply gateway neighborhoods, for low-income and immigrant populations, to more segregated neighborhoods? Are some diverse neighborhoods diverse only because gentrification is midway through a process that leads to spatial divisions between races and ethnicities? It it possible for diversity to be a stable condition and, if so, how?
