Category: Peer-Reviewed Research

Kim, Jae Hong, Kevin Kane, Young-An Kim, and John R. Hipp. (2023). “Business churning and neighborhood instability: Is there a link?” International Regional Science Review. Online.

Abstract: “Much of the work concerning economic dynamism has focused on its aggregate-level implications, while there have been rising concerns about business displacement at the community or neighborhood level. In this article, we analyze this important (restructuring) process using detailed establishment-level business information and explore how it manifests itself across space within the Los Angeles—Long Beach—Santa Ana, CA Urbanized Area. We also investigate the association between business churning and neighborhood-level housing vacancy rates to understand the implications of dramatic changes in the business landscape. We find that housing vacancies are more likely to increase in urban neighborhoods with a higher establishment death rate, while the creation of new businesses can mitigate the association to some extent. We also detect substantial variation across decades not only in the spatial distribution of business churning but also in its association with housing vacancy rates, suggesting the evolving nature of business dynamics and their implications.”

Hipp, John R., Sugie Lee, Dong Hwan Ki, and Jae Hong Kim. 2022. “How concentrated disadvantage moderates the built environment and crime relationship on street segments in Los Angeles.” Criminology & Criminal Justice Forthcoming.

Abstract: “Objectives: Criminological theories have posited that the built environment impacts where crime occurs, however measuring the built environment is difficult. Furthermore, it is uncertain whether the built environment differentially impacts crime in high disadvantage neighborhoods.
Methods: This study extracts features of the built environment from Google Street View images with a machine learning semantic segmentation strategy to create measures of fences, walls, buildings, and greenspace for over 66,000 street segments in Los Angeles.
Results: Results indicate that the presence of more buildings on a segment was associated with higher crime rates, and had a particularly strong positive relationship with robbery and motor vehicle theft in low disadvantage neighborhoods. Notably, fences and walls exhibited different relationships with crime. Walls, which do not allow visibility, were strongly negatively related to crime, particularly for robbery and burglary in high disadvantage neighborhoods. Fences, which allow visibility, were associated with fewer robberies and larcenies, but more burglaries and aggravated assaults. Fences only exhibited a negative relationship with violent crime when they were located in low disadvantage neighborhoods.
Conclusions: The results highlight the importance of accounting for the built environment and the surrounding level of disadvantage when exploring the micro-location of crime.”

Hipp, John R., Sugie Lee, Jae Hong Kim, and Benjamin Forthun. (2022). “Employment Deconcentration and Spatial Dispersion in Metropolitan Areas: Consequences for Commuting Patterns.” Cities. 131: 1-14.

Abstract: “There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying “job centers” and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes.”

Hipp, John R. and Jae Hong Kim. (2023). “Persistent Racial Diversity in Neighborhoods: What Explains it and what are the Long-term Consequences?” Urban Geography.  44(4): 640-667.

Abstract: “We explore neighborhoods in Southern California from 1980 to 2010 that exhibit persistent racial diversity (PRD) and the consequences of this PRD. Initial exploratory analyses show that the racial composition of the area surrounding the neighborhood in 1980 is associated with which neighborhoods become PRDs. Our primary analyses compare how PRD neighborhoods change over time (1980–2010) based on several socio-demographic measures to a matched group of non-PRD neighborhoods that had similar characteristics in 1980. The key finding is that PRD neighborhoods improved more on per capita income and percent in poverty compared to their matched tracts from 1980 to 2010. We also found that there was not a single route to persistent diversity, but rather a myriad of pathways through which racial/ethnic diversity can persist over a long time period at the neighborhood level.”

Kim, Jae Hong, Sugie Lee, John R. Hipp, and Dong Hwan Ki. (2021). “Decoding Urban Landscapes: Google Street View and Measurement Sensitivity.” Computers, Environment and Urban Systems Online.

Abstract: “While Google Street View (GSV) has increasingly been available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and directions. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.”

Hipp, John R., Sugie Lee, Dong Hwan Ki, and Jae Hong Kim. (2021). “Measuring the Built Environment with Google Streetview and Machine Learning: Consequences for Crime on Street Segments.” Journal of Quantitative Criminology. Online.

Abstract: “Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro-geographic units, measuring this is difficult.
Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes.
Results: The presence of more persons in the environment is associated with higher levels of crime. The auto-oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted-U relationship with two crime types.
Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.”

Hipp, John R., Jae Hong Kim, and Benjamin Forthun. (2021). “Proposing New Measures of Employment Deconcentration and Spatial Dispersion across Metropolitan Areas in the U.S.” Papers in Regional Science. Online.

Abstract: “A well‐known challenge is measuring employment concentration across metropolitan areas and analysing the evolving spatial structure. We introduce a new approach that avoids identifying “job centers” and conceptualizes the distribution of employment based on two dimensions: (1) employment deconcentration; and (2) spatial dispersion of high employment locations. We apply this framework to study 329 US metropolitan regions based on 1 sq km. grid cells. We find diverse trajectories of metropolitan restructuring between 2000 and 2010, and substantial variation across regions in employment concentration. The new framework enables researchers to compare metropolitan regions to gain insights into the dynamic nature of metropolitan spatial structure.”

Hipp, John R. (2020). “Typology of Home Value Change Over Time: Growth Mixture Models in Southern California Neighborhoods from 1960-2010.” Journal of Urban Affairs.  Online.

Abstract: “This study uses U.S. Census data on average home values in Southern California census tracts from 1960 to 2010. Using growth mixture modeling (GMM), 26 unique groups are detected capturing nonlinear change in neighborhood relative home values over this study period. There were seven broad patterns of changing home values: (1–3) decline and then rise (at high, mid, and low portions of the home value distribution); (4) rise and then decline; (5–6) a monotonic increase (either above or below the region average); and (7) a monotonic decrease. Multinomial regression models found that covariates exhibited a much stronger effect for distinguishing between the average level of home values in neighborhoods over the study period, rather than how home values changed over time.”

Hipp, John R. (2020). “Neighborhood Change from the Bottom Up: What are the Determinants of Social Distance between New and Prior Residents?” Social Science Research. 86: 1-20.

Abstract: “An important source of neighborhood change occurs when there is a turnover in the housing unit due to residential mobility and the new residents differ from the prior residents based on socio-demographic characteristics (what we term social distance). Nonetheless, research has typically not asked which characteristics explain transitions with higher social distance based on a number of demographic dimensions. We explore this question using American Housing Survey data from 1985 to 2007, and focus on instances in which the prior household moved out and is replaced by a new household. We focus on three key characteristics for explaining this social distance: the type of housing unit, the age of the housing unit, and the length of residence of the exiting household. We find that transitions in the oldest housing units and for the longest tenured residents result in the greatest amount of social distance between new and prior residents, implying that these transitions are particularly important for fostering neighborhood socio-demographic change. The results imply micro-mechanisms at the household level that might help explain net change at the neighborhood level.”