Neighborhood change from the bottom Up: What are the determinants of social distance between new and prior residents?

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 four key characteristics for explaining this social distance: the type of housing unit, the age of the housing unit, the length of residence of the exiting household, and the crime and social disorder in the neighborhood. 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

A typological approach to studying policing

Policing in the United States has experienced immense change throughout the past quarter-century. Although police agencies have shared their goals of preserving life and protecting property, their philosophies and practices for achieving these goals have differed. The present research, therefore, explores patterns in policing via a novel, typological approach. Using six waves of data (1993, 1997, 2000, 2003, 2007, and 2013) from the Law Enforcement Management and Administrative Statistics (LEMAS) data series, we first employ factor analyses to generate indices for six important policing dimensions: (1) officer diversity, (2) community policing, (3) patrol strategy diversity, (4) militancy, (5) technology, and (6) staffing rigor. Using these indices, we then employ latent class analyses to construct typologies of police agencies, and examine the distribution of such typologies across space at various points in time. Our results reveal several key findings. We detect consistent patterns in typologies across time, including classes with high militancy, high diversity, or low staffing rigor (among others). Within these sets of classes, we also detect micro-heterogeneity amongst patterns of index values: for example, subsets of classes which all score high on one dimension but score high versus low on other dimensions. Finally, we find evidence to suggest spatial convergence of typologies in one large geographic region: Southern California. By offering a multidimensional classification scheme over a 20-year period, we contribute to the policing literature by highlighting the importance and implications of studying multiple policing dimensions simultaneously

Rising inequality and neighbourhood mixing in US metro areas

Superstar cities with high-paying creative-class jobs, venture capital, and innovation are thought to be more unequal. We analyze mixing in neighbourhoods by income, education and occupation, relating this intra-urban measure with regional productivity indicators. Using non-overlapping census units and a machine-learning estimation technique that iterates over all combinations of economic, business, housing and cultural indicators, we identify ‘ingredients’ associated with economically and socially diverse neighbourhoods. Broad support is not found that neighbourhoods in superstar regions are less mixed; however, overrepresentation in creative occupations stymies mixing as does a combination of weak economic fundamentals with high shares of new housing

What came first: the police or the incident? Bidirectional relationships between police actions and police incidents

The present research examines the long-term, bidirectional relationships between calls for service, crime, and two police patrol strategies in Santa Monica, California: foot patrol and police stops. Using nine years of monthly data (2006–2014), we estimate two sets of block-level, longitudinal models to tease apart these relationships. In our first set of models, we use police actions and calls for service in the preceding month(s) to predict crime in the subsequent month. In our second set of models, we use calls for service and crime in the preceding month(s) to predict police actions in the subsequent month. We find that while changes in calls for service and crime often precede changes in police action, changes in crime also tend to follow them. For example, police stops appear to be particularly receptive to burglary: blocks with more burglaries receive greater numbers of police stops, and blocks with more police stops have reduced odds of experiencing burglary. We also find that the length of effects of predictors varies as a function of predictor and outcome: whereas some predictors exhibit short temporal effects (e.g. one month), other predictors exhibit much longer temporal effects (e.g. twelve months). Our results thus provide important insight into the spatial and temporal relationships between police actions and police incidents. Police actions must be neatly tailored to police incidents at precise levels if long-term deterrent effects at these levels are to be achieved

Social distance and social change: how neighborhoods change over time

Two key theoretical themes guided my exploration of neighborhood change. First, I utilized the classic sociological notion of social distance in testing its determinants and viewing its effect on neighborhood change over time. I measured social distance in various manners: 1) racial/ethnic differences, 2) a composite of several characteristics (including racial/ethnic, socio-economic, and demographic), or 3) the consolidated inequality created by difference along both racial/ethnic and socio-economic differences simultaneously. Second, I built an explicit micro-level theory of household residential mobility decisions to explain the generation of the structural characteristics that theories posit cause neighborhood crime. I found that social distance had important implications for neighborhoods. Using a multi-level, longitudinal sample of the American Housing Survey (AHS) I found that individual-level social distance along multiple characteristics helps explain neighborhood satisfaction: this suggests the importance of focusing on the fit of the household with the neighborhood. Dynamic analyses using this same sample showed that racial/ethnic heterogeneity explains crime rates four years later. Fixed effects analyses using a sample of census tracts in eleven cities found that changing ethnic heterogeneity over the decade is positively related to changing crime rates. These same fixed effects analyses showed that increasing inequality between African-Americans and whites is positively related to the change in various official crime rates. My theoretical model helped explain the change in neighborhood structural characteristics. Using the AHS sample, I found that perceived crime in a block increases general residential mobility. This theoretical model also predicted and found that the presence of more homeowners on a block reduces perceived crime four years later in dynamic models. While residential instability had no effect on crime four years later in cross-lagged models, more vacant units in the block are positively related to perceived crime four years later. This suggests a possible manner in which residential mobility may affect neighborhood crime rates. I also found using the AHS sample that higher levels of block perceived crime creates racial/ethnic residential transformation by increasing the likelihood that African-Americans and Latinos will move into the block, and reducing the likelihood that whites will move in

Parolees' physical closeness to health service providers: a study of California parolees.

We studied a sample of parolees and health service providers in the state of California in 2005-2006 to examine the relative physical closeness to health providers (and the potential demand of these providers) of parolees based on their demographic and prior offending characteristics. Although African-American and Latino parolees have more health providers nearby, these providers have considerably more potential demand. The health providers near long-term prisoners and sex offenders have more potential demand. The results suggest inequity in access to services, as minority parolees and those with greater needs may live near more impacted providers. The results also suggest some differences in access based on rural, suburban, or urban location

Social Capital, Too Much of a Good Thing? American Religious Traditions and Community Crime

Using American religious traditions as measures of bonding and bridging social capital in communities, we empirically test how these different forms of social capital affect crime rates in 3,157 U.S. counties in 2000. Our results suggest that the bonding networks evangelical Protestants promote in communities explain why counties with a greater percentage of residents affiliated with this tradition consistently have higher crime rates. Conversely, our results suggest that the bridging networks mainline Protestants and Catholics foster in communities explain why counties with a greater percentage of residents affiliated with these traditions generally have lower crime rates. This article provides empirical corroboration for recent theoretical discussions that focus on how the social capital groups cultivate in communities need not always benefit communities as a whole

Fight or flight? Crime as a driving force in business failure and business mobility.

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

Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity

Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities