About

John R. Hipp is a Professor of Criminology, Law and Society, Urban Planning and Public Policy, and Sociology at the University of California Irvine. His research focuses on how neighborhoods change over time and how these changes influence and are influenced by neighborhood crime, social networks, and institutional roles. He employs quantitative methods and social network analysis to investigate these processes, with particular attention to the spatial and temporal dimensions of neighborhood dynamics. Hipp's work explores the agents of change within communities, examining processes at various spatial scales from street blocks to metropolitan areas, and considers how household decisions impact neighborhood ecology. He is the co-director of the Irvine Lab for the Study of Space and Crime (ILSSC) and the Director of the Metropolitan Futures Initiative (MFI), an interdisciplinary project dedicated to building economically vibrant, environmentally sustainable, and socially just communities through collaboration across disciplines and regions. Hipp has contributed to the field through his recent book, The Spatial Scale of Crime, and his research includes studying the spatial distribution of residents' social networks, supported by NSF funding. His work emphasizes the importance of networks and institutions in shaping neighborhood change, civic involvement, and residential mobility. Hipp has guided numerous students whose dissertations have led to faculty positions at various universities and has been active in disseminating his research through presentations, reports, and videos.

Research topics

• Computer Science
• Geography
• Sociology
• Medicine
• Mathematics
• Biology
• Psychology
• Environmental health
• Virology
• Machine Learning
• Ecology
• Cartography
• Economics
• Engineering
• Criminology
• Data Mining
• Econometrics
• Telecommunications
• Remote sensing
• Developmental psychology
• Demography
• Economic geography
• Social psychology
• Economic growth

Selected publications

• Who gets quality urban parks? A socioeconomic disparity analysis using user reviews and the opportunity algorithm in Los Angeles

  Urban forestry & urban greening · 2026-01-07 · 1 citations

  article
  PublisherDOI

• The Decay of Impact with Network Distance in Linear Diffusion Processes

  arXiv (Cornell University) · 2026-04-24

  preprintOpen access

  Many processes related to status, power, and influence within social networks have been modeled using forced linear diffusion models; examples include the highly successful Friedkin-Johnsen model of social influence, the status/power scores of Katz and Bonacich, and the widely used network autocorrelation model. While a basic assumption of such models is that the impact of one individual on another through any given path falls exponentially with path length, the total impact of the first individual on the second involves contributions from walks of all lengths; thus, while total impact is expected to decline with network distance, the relationship is not trivial. Here, we provide an approximate solution for the total impact of one node on another as a function of network distance, showing that the total impact is given to first order by a product of eigenvector centrality scores together with an expression in terms of the graph spectrum (eigenvalues of the adjacency matrix) that falls exponentially with distance. We also show how this solution can be refined using higher-order eigenvectors of the adjacency matrix. A numerical study on interpersonal networks drawn from educational settings verifies an average exponential decline in impact strength under the linear diffusion model, and shows that the first-order eigenvector approximation can often be a good proxy for total impact as obtained from the exact solution. This suggests a simple model that can be used to approximate total impact for social influence or status processes in a range of settings.

  PublisherDOI

• Business environment ecology and crime: A robust test across 182 cities

  CrimRxiv · 2026-03-09

  articleOpen access1st authorCorresponding

  Studies assessing the question of how certain types of business establishments are related to the level of crime on blocks typically do not account for the general business context of those blocks. The present study extends one previous study that did so by using a large sample of blocks across 182 cities in the U.S. We assess whether measuring the general business context of blocks as three broad categories of businesses—consumer-facing businesses, blue-collar businesses, and white-collar businesses—along with the heterogeneity of consumer businesses on a block can explain where crime occurs. The study finds that these four measures explain much of the variation in crime due to businesses across blocks. Furthermore, whereas 12 specific types of businesses exhibit strong relationships with crime when not accounting for this business context, their relationships with crime greatly diminish, or completely evaporate, once accounting for the general business context. Finally, blocks with more consumer business heterogeneity have higher levels of crime, and this relationship is stronger in small population cities and in low population areas.

  PublisherDOI

• How Does the Business Environment Shape Mobility By Offenders and Mobile Targets?

  Crime & Delinquency · 2026-03-11 · 1 citations

  article1st authorCorresponding

  Given that much street crime concentrates near businesses, a question is how the business composition of census blocks, not just specific businesses, impacts the spatial mobility of offenders and victims. The study uses data from Dallas, TX, from 2014 to 2020. The results showed that crimes occur farther from the home for offenders than for victims. Furthermore, locations with more consumer-facing businesses are targeted more often by offenders, and the mix of these businesses is particularly attractive for offenders. Finally, the results also showed that the presence of more consumer-facing businesses in the surrounding 400 m buffer increased the likelihood of offenders targeting a location for crime, regardless of the features of the block itself.

  PublisherDOI

• Peer influence decay and behavioral diffusion in adolescent networks: A simulation approach

  Science · 2026-04-30

  article

  How far does peer influence spread through social networks before dissipating? This study investigates the diffusion of smoking behavior in adolescent friendship networks using longitudinal data from two schools ( n = 3154 students) in the National Longitudinal Study of Adolescent to Adult Health. Using Stochastic Actor–Oriented Models, we simulate interventions targeting heavy smokers using various strategies (random, in-degree, eigenvector centrality) and coverage (10 to 100%). A new exponential decay model quantifies influence attenuation, revealing indirect peer influences, or spillover effects, up to three steps from targets. Targeting 10 to 30% of central individuals maximizes smoking reductions, but gains plateau beyond 40 to 50% owing to network saturation. In our analyses, the denser network exhibits broader diffusion and slower decay than the larger, sparser network. This decay metric optimizes intervention design across diverse network structures.

  PublisherDOI

• Uncovering Determinants of Homelessness Service Requests in Los Angeles: A Zero-Inflated Negative Binomial Approach Using Street-Segment Level Data

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• The Decay of Impact with Network Distance in Linear Diffusion Processes

  ArXiv.org · 2026-04-24

  articleOpen access

  Many processes related to status, power, and influence within social networks have been modeled using forced linear diffusion models; examples include the highly successful Friedkin-Johnsen model of social influence, the status/power scores of Katz and Bonacich, and the widely used network autocorrelation model. While a basic assumption of such models is that the impact of one individual on another through any given path falls exponentially with path length, the total impact of the first individual on the second involves contributions from walks of all lengths; thus, while total impact is expected to decline with network distance, the relationship is not trivial. Here, we provide an approximate solution for the total impact of one node on another as a function of network distance, showing that the total impact is given to first order by a product of eigenvector centrality scores together with an expression in terms of the graph spectrum (eigenvalues of the adjacency matrix) that falls exponentially with distance. We also show how this solution can be refined using higher-order eigenvectors of the adjacency matrix. A numerical study on interpersonal networks drawn from educational settings verifies an average exponential decline in impact strength under the linear diffusion model, and shows that the first-order eigenvector approximation can often be a good proxy for total impact as obtained from the exact solution. This suggests a simple model that can be used to approximate total impact for social influence or status processes in a range of settings.

  PublisherOA PDF

• Examining how structural characteristics and the physical environment simultaneously impact crime in neighborhoods: Using a semi-parametric strategy

  Journal of Criminal Justice · 2025-07-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Concentrated disadvantage and stress in daily life after prison

  Criminology · 2025-02-01 · 1 citations

  articleOpen accessSenior author

  Abstract Reentry from prison is a stressful life transition, which has consequences for recidivism, health, and well‐being. Navigating poor and highly surveilled neighborhoods after prison is considered a primary stressor after release; however, it is methodologically challenging to document how poor places exert these invisible, day‐to‐day strains. Bringing together theories of stress with “activity space” research, we analyze nearly 300,000 GPS estimates and more than 5300 daily reports of emotions collected through mobile phones across 3 months among a cohort of men recently released from prison in Newark, New Jersey. Using a new approach to measure activity spaces, which we term “egocentric places,” combined with multilevel models that investigate within‐person changes over time, we find that daily exposure to disadvantaged places is associated with increased negative emotions, specifically, stress. These associations are most evident when navigating commonly visited places (as opposed to rarely visited places) and are most concentrated among people who already live in highly disadvantaged residential areas. These findings illuminate a generally hidden process in which spending time in disadvantaged places exacerbates stress after prison.

  PublisherOA PDFDOI

• Examining how structural characteristics and the physical environment simultaneously impact crime in neighborhoods: Using a semi-parametric strategy

  CrimRxiv · 2025-09-15

  articleOpen accessSenior author

  This study examines the associations between various social and physical environmental characteristics and their interrelated influence on neighborhood crime. Using Kernel Regularized Least Squares (KRLS), we estimate the marginal effects of each independent variable at each datapoint by providing pointwise estimates of partial derivatives. Then we regress the derivative values for each independent variable on each other variable in the model to examine whether these derivative estimates (marginal effects) vary by other variables in the model. We found that the effects of the physical environment on different types of crime in neighborhoods vary by different levels of social structural characteristics. We simultaneously assess how the two different types of neighborhood environments can work together in a semiparametric way, theoretically integrate both social disorganization and criminal opportunity perspectives, and thus provide a more comprehensive as well as nuanced explanation of neighborhood crime.

  PublisherOA PDFDOI

Frequent coauthors

• Carter T. Butts

  University of California, Irvine

  32 shared

• Cynthia M. Lakon

  University of California, Irvine

  25 shared

• Adam Boessen

  University of Missouri–St. Louis

  23 shared

• Young-An Kim

  Florida State University

  22 shared

• Cheng Wang

  19 shared

• Jae Hong Kim

  University of California, Irvine

  19 shared

• Rupa Jose

  University of Pennsylvania

  15 shared

• Rebecca Wickes

  Griffith University

  14 shared

Labs

• ILSSC Irvine Lab for the Study of Space and CrimePI

Education

• Ph.D.

  University of North Carolina, Chapel Hill