Fast and slow change in neighbourhoods: characterization and consequences in Southern California

Abstract

BioJohn R. Hipp is a Professor in the departments of Criminology, Law and Society, and Sociology, at the University of California Irvine. His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role networks and institutions play in that change. He approaches these questions using quantitative methods as well as social network analysis. He has published substantive work in such journals as American Sociological Review, Criminology, Social Forces, Social Problems, Mobilization, City & Community, Urban Studies and Journal of Urban Affairs. He has published methodological work in such journals as Sociological Methodology, Psychological Methods, and Structural Equation Modeling. Nicholas Branic is a graduate student in the department of Criminology, Law and Society at the University of California Irvine.3 Fast and slow change in neighborhoods: Characterization and consequences in Southern California AbstractDue to data limitations, most studies of neighborhood change within regions assume that change over the years of a decade is relatively constant from year-to-year. We use data on home loan information to construct annual measures of key socio-demographic measures in neighborhoods (census tracts) in the Southern California region from 2000-10 to test this assumption. We use latent trajectory modeling to describe the extent to which neighborhood change exhibits temporal nonlinearity, rather than a constant rate of change from year to year.There were four key findings: 1) we detected nonlinear temporal change across all sociodemographic dimensions, as a quadratic function better fit the data than a linear one in the latent trajectories; 2) neighborhoods experiencing more nonlinear temporality also experienced larger overall changes in percent Asian, percent black, and residential stability during the decade; neighborhoods experiencing an increase in Latinos or a decrease in whites experienced more temporal nonlinearity in this change; 3) the strongest predictor of racial/ethnic temporal nonlinearity was a larger presence of the group at the beginning of the decade; however, the racial and SES composition of the surrounding area, as well as how this was changing in the prior decade, also affected the degree of temporal nonlinearity in the current decade; 4) this temporal nonlinearity has consequences for neighborhoods: greater temporal nonlinear change in percent black or Latino was associated with larger increases in violent and property crime during the decade, and the temporal pattern of residential turnover or changing average income impacted changes in crime. The usual assumption of constant year-to-year change when interpolating neighborhood measures over intervening years may not be appropriate.1 Fast and slow change in neighborhoods: Characterization and consequences in Southern CaliforniaNeighborhoods are important contexts for the daily lives of individuals and have therefore been the focus of considerable scholarship....

The shape of neighborhoods to come: Examining patterns of gentrification and holistic neighborhood change in Los Angeles County, 1980–2010

“…Conversely, we somewhat arbitrarily adopt decades as our temporal scale, necessitated by our use of Census data. We note that scholars have documented varying paces of gentrification (Walks and Maaranen, 2008), and of neighborhood change more broadly (Hipp and Branic, 2017). As such, it may be the case that neighborhoods undergoing successive periods of gentrification are simply experiencing a slower process of change, rather than discrete waves of gentrification.…”

Growing pains or appreciable gains? Latent classes of neighborhood change, and consequences for crime in Southern California neighborhoods

“…Although a substantial body of literature employs census data to measure neighborhood change over time, some argue that decennial census variables are not adequately detailed to capture ongoing processes of neighborhood change (Kreager, Lyons, and Hays 2011). In other words, a ten-year measure of change neglects many of the short-term fluctuations in neighborhood characteristics (Hipp and Branic 2017). In the following sections, we first describe the variables included in the third stage of analyses, and then describe the approach, and results, of the three analytic stages.…”

“…Thus, we do not posit that some of these changes necessarily have immediate effects on changes in crime levels, but rather that it is the gradual unrolling of these processes that changes the level of crime in the neighborhood. In this way, we borrow insights from a recent study that focused on the relative speed of yearly demographic change in neighborhoods and consequences for changes in crime levels (Hipp and Branic 2017). That is, Hipp and Branic found that it was not simply demographic change in neighborhoods that was related to changes in crime levels; rather, it was also the relative quickness of such change that impacted the level of change in crime that occurred over the decade.…”

Improving or declining: What are the consequences for changes in local crime?*

“…The expected consequence is a monotonic relationship in which increases in disadvantage will correspond to higher levels of crime, whereas decreasing disadvantage results in reduced crime. A study of Los Angeles census tracts found that increasing disadvantage over a decade was associated with increases in aggravated assault and larceny, but it was unrelated to other crime types (Hipp & Branic, 2017), and a study of Brisbane neighborhoods found a positive relationship between disadvantage and changing violent crime (Hipp & Wickes, 2017). Also, decreasing average income was associated with rising aggravated assaults, robberies, and burglaries in another study of Los Angeles egohoods (Hipp & Kubrin, 2017).…”