Category: Peer-Reviewed Research

Hipp, John R., Seth A. Williams, Young-an Kim, and Jae Hong Kim. (2019). “Fight or Flight? Crime as a Driving Force in Business Failure and Business Mobility.” Social Science Research. 82: 164-180.

Abstract: “A growing body of research has documented the consequences of neighborhood crime for a myriad of individual, household, and community outcomes. Given that neighborhood businesses figure into the link between neighborhood structure and crime as sources of employment or sites for neighbor interaction, the present study examines the extent to which neighborhood crime is associated with the survival, mobility, and destination locations of businesses in the subsequent year. Using business data from Reference USA (Infogroup 2015) and crime data from the Southern California Crime Study (SCCS) we assess this question for neighborhoods across cities in the Southern California region. We find that in general, higher violent and property crime are significantly associated with both business failure and mobility, and that higher crime in a destination neighborhood reduces the likelihood that a business locates there. We also present findings specific to industries, and discuss the implications of our findings for future research.”

Hipp, John R. and Nicholas Branic. (2017). “Fast and slow change in neighborhoods: Characterization and consequences in Southern California.” International Journal of Urban Sciences. 21(3): 257-281.

Abstract: “Due 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 socio-demographic 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.”

Hipp, John R., Kane, Kevin, and Kim, Jae Hong (2017). “Recipes for Neighborhood Development: A Machine Learning Approach toward Understanding the Impact of Mixing in Neighborhoods”  Landscape and Urban Planning 164, 1-12.

Abstract: “Scholars of New Urbanism have suggested that mixing along various dimensions in neighborhoods (e.g., income, race/ethnicity, land use) may have positive consequences for neighborhoods, particularly for economic dynamism. A challenge for empirically assessing this hypothesis is that the impact of mixing may depend on various socio-demographic characteristics of the neighborhood and takes place in a complex fashion that cannot be appropriately handled by traditional statistical analytical approaches. We utilize a rarely used, innovative estimation technique—kernel regularized least squares—that allows for nonparametric estimation of the relationship between various neighborhood characteristics in 2000 and the change in average household income in the neighborhood from 2000 to 2010. The results demonstrate that the relationships between average income growth and both income mixing and racial/ethnic mixing are contingent upon several neighborhood socio-demographic “ingredients”. For example, racial mixing is positively associated with average income over time when it occurs in neighborhoods with a high percentage of Latinos or immigrants, high population density, or high housing age mixing. Income mixing is associated with worsening average household income in neighborhoods with more poverty, unemployment, immigrants, or population density. It appears that considering the broader characteristics of the neighborhood is important for understanding economic dynamism.”

Hipp, John R. and Kane, Kevin (2017). Cities and the Larger Context: What explains changing levels of crime? Journal of Criminal Justice 49, 32-44.

Abstract: “This study explores whether the broader context in which a city is located impacts the change in crime levels over the subsequent decade. This study uses a wide range of cities (those with a population of at least 10,000), over a long period of time (from 1970 to 2010). We test and find that although cities with larger population and those surrounded by a county with a larger population typically experience larger increases in crime over the subsequent decade, cities experiencing an increase in population during the current decade experience crime decreases. The study finds that cities with higher average income experience greater subsequent crime decreases, and those surrounded by counties with larger unemployment increases experience crime increases. Higher levels of income inequality and racial/ethnic heterogeneity are associated with increasing crime rates, and increasing inequality and racial/ethnic heterogeneity in the surrounding county are associated with further increases. Furthermore, this relationship has strengthened since 1970, suggesting that both scales of inequality are even more important from a public safety perspective. Finally, we tested the time invariance of these relationships, and showed that the magnitude of the relationship between city-level inequality and increasing crime has increased over the study period. “

Kane, Kevin, John R. Hipp, and Jae Hong Kim. (2017). Analyzing accessibility using parcel data: Is there still an access-space trade-off in Long Beach, California? The Professional Geographer 69:3, 486-503.

Abstract: “This article analyzes the impact of changing housing and neighborhood characteristics on the accessibility of neighborhood businesses using Long Beach, California as a case study. While advocates of smart growth and New Urbanism encourage land use mixing, aggregate-level analysis can be too coarse to pick up on fine-grained aspects of urban streetscapes. This study uses assessor parcel records and a point-based business establishment dataset to analyze citywide patterns of accessibility from individual dwelling units to 31 types of neighborhood businesses including grocery stores, service shops, drug stores, doctor’s offices, and banks. Regression results compare parcel-level and neighborhood-level drivers of accessibility between 2006 and 2015 to gauge the aggregated effect of recent economic, demographic, and built environment changes on this aspect of urban spatial structure. Larger homes in older, multi-unit buildings and higher-income neighborhoods show substantial increases in accessibility to most establishment types, suggesting a trend toward both greater accessibility and larger dwelling units – despite the traditional tradeoff between access and space. While gradual increases in home and business density increased overall accessibility over this period, weaker neighborhoodlevel results indicate this trend is less pronounced in high-poverty and nonwhite areas.”

Kane, Kevin, John R. Hipp, and Jae Hong Kim. (2016). “Los Angeles employment concentration in the 21st century.” Urban Studies. 

Abstract: “This paper is an empirical analysis of employment centers in the Los Angeles region from 1997-2014. Most extant work on employment centers focuses on identification methodology or their dynamics during a period of industrial restructuring from 1980-2000. This timely study examines hypotheses derived from more recent perspectives on urban concentration and dispersion including New Urbanism, Smart Growth, sustainable cities, and the recent Global Financial Crisis. We use point-based, rather than census tractbased employment data to analyze concentration across five key industries: knowledge-intensive business services (KIBS), retail, creative, industrial, and high-tech, emphasizing changes in center composition and boundaries. While using point data necessitates slight changes to the nonparametric identification method typically used, results show far greater change across centers than previous longitudinal studies. Only 43% of the land area that is in an employment center is part of one in both 1997 and 2014. Using a persistence score, centers range from stable to highly fluctuating, but emerging, persisting, and dying centers are found in core and fringe areas alike. KIBS are most associated with stable centers, while high tech employment is attracted toward emerging areas and retail exists throughout. Emerging centers are more likely to have greater accessibility, while industrial employment becomes far more concentrated in centers by 2014.”