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Sociol Compass. Author manuscript; bachelor in PMC 2016 Jun 30.

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Urban Poverty and Neighborhood Effects on Offense: Incorporating Spatial and Network Perspectives

Corina Graif

Department of Sociology and Criminology, The Pennsylvania State University

Andrew S. Gladfelter

Andrew South. Gladfelter, Department of Sociology and Criminology, The Pennsylvania State University;

Andrew Due south. Gladfelter

Department of Sociology and Criminology, The Pennsylvania State University

Stephen A. Matthews

Stephen A. Matthews, Department of Sociology and Department of Anthropology, The Pennsylvania Country Academy;

Stephen A. Matthews

Department of Folklore and Department of Anthropology, The Pennsylvania State University

Abstract

Enquiry on neighborhoods and crime is on a remarkable growth trajectory. In this article, nosotros survey of import recent developments in the scholarship on neighborhood effects and the spatial stratification of poverty and urban crime. We advance the instance that, in understanding the touch on of neighborhoods and poverty on criminal offense, sociological and criminological enquiry would do good from expanding the belittling focus from residential neighborhoods to the network of neighborhoods individuals are exposed to during their daily routine activities. This perspective is supported by reemerging scholarship on activity spaces and macro-level research on inter-neighborhood connections. Nosotros highlight piece of work indicating that non-residential contexts add together variation in criminogenic exposure, which in turn influence offending behavior and victimization chance. Too, we draw on recent insights from research on gang violence, social and institutional connections, and spatial mismatch, and phone call for advancements in the scholarship on urban poverty that investigates the salience of inter-neighborhood connections in evaluating the spatial stratification of criminogenic risk for individuals and communities.

Introduction

Since the get-go of the 20th century, urban scholars have extensively studied the role of urbanism and poverty in increasing crime. Rapid urban growth and population mobility together with stark socioeconomic differentiations across the urban space were, from the early years of the Chicago Schoolhouse, associated with the breakdown of social control and increased criminal offense (Zorbaugh 1929). Classic ecological studies showed that neighborhoods with high poverty near commercial and industrial districts exhibited the highest levels of delinquency and criminality (Shaw and McKay 1942). These levels persisted over decades even when neighborhood population groups inverse dramatically, indicating that structural conditions like neighborhood poverty contributed to delinquency and crime above and beyond individual disposition.

In the late-20^th century, industrial restructuring and suburban flying has exacerbated the spatial differentiation of resources and concentration of unemployment among the low-skilled. In The Truly Disadvantaged, Wilson (1987) noted that unemployment and poverty clustered and that together these 'concentration furnishings' weakened family unit bonds and institutional ties, undermining the formal and informal chapters for crime control. Scholars today refer to areas of high poverty equally areas of concentrated disadvantage. The Great Recession of 2008 added greater strain to struggling depression-income urban communities beyond the country and recent studies increasingly connect economical distress (e.g. foreclosures) to college crime (Ellen et al. 2013; see Arnio and Baumer 2012 for an exception).

Building on a century old tradition of enquiry, enquiry on neighborhoods and law-breaking in the past decade has shown remarkable growth. More than 250 articles were published on this topic in 2012 alone (Figure 1). The scholarship on place, space, and geography in relation to law-breaking exhibited similar trajectories. Combined, this literature demonstrates that neighborhood poverty and related social and economic conditions are closely related to multiple indices of criminal exposure and offending. Specifically, studies find that neighborhood poverty and associated structural factors continue to predict multiple crime-related outcomes, including: individuals' exposure to violence (Bingenheimer et al. 2005; Sampson et al. 1997); risk of victimization (Burchfield and Silverish 2013); boyish violent crime (De Coster et al. 2006; Zimmerman and Messner 2010); aggression (Molnar et al. 2008); arrests for violent behavior (Ludwig et al. 2000); domestic violence (Benson et al. 2003); incarceration (Rodriguez 2013); and backsliding (Kubrin and Stewart 2006). With few exceptions, these patterns tend to hold in multiple cities and in nationally representative studies.

Yearly Publication Count by Keyword Combination for the Past x Years Based on ISI Web of Science Search Results

Near studies implicitly presume that exposures to gamble (e.g. criminal offending or victimization) are sufficiently represented by attributes of the neighborhood of residence. Chaix (2009) refers to this as the "residential trap." However, the residential focus ignores the fact that individuals' routines, in the amass, betrayal them to different neighborhoods on a daily basis. Few studies take examined the implications of routine exposures to multiple, non-residential neighborhood locations for criminal offence. In this paper, we address this gap and advance a case for the thought that a more complete agreement of neighborhood furnishings on crime will profoundly benefit from moving beyond the traditional focus on residential exposure to research based on an individual's exposure to networks of neighborhoods. In building our argument, we draw on classic and modernistic theorizing on neighborhood effects and the spatial differentiation of poverty and criminal offense, and integrate it with re-emerging literature on activeness spaces, inter-neighborhood connections, and social and institutional networks.

Neighborhood effects on crime

In this section, we review some of the cadre mechanisms and associated theoretical perspectives that have been proposed to business relationship for observed neighborhood effects. Nosotros discuss problems related to the definition of the neighborhood, scales of spatial exposures, and the spatial clustering of neighborhood disadvantage across urban environments.

Internal neighborhood mechanisms

Several major theoretical perspectives shed light on some of the possible mechanisms underlying the neighborhood effects on crime. First, one of the oldest theoretical perspectives, social disorganization, posits that ecological atmospheric condition similar socioeconomic disadvantage, racial heterogeneity, and residential mobility, erode neighborhood social control and facilitate offense (Shaw and McKay 1942). Social control largely operates through local ties to other individuals and institutions (Bursik and Grasmick 1993). A later extension of this theory proposes that independent of social ties, collective efficacy—a combination of social cohesion, trust and the ability of neighborhood residents to realize mutual goals and values—reduces neighborhood offense (Sampson et al. 1997). Second, a perspective that is gaining increasing traction recently, routine activities states that crimes are nearly common when motivated offenders intersect in time and infinite with attractive targets in the absenteeism of guardianship (Cohen and Felson 1979). Oft, routine activities components are assessed through measures such as unemployment rate (a proxy for motivated offenders) and time spent out of the household (low level of guardianship). Tertiary, subcultural theories advise that local value structures tin promote criminal offence (Fischer 1975), the focus being on "urban," "street" and a "southern" culture of violence. Finally, relative deprivation or strain approaches advise that socioeconomic continuing relative to peers or neighbors may influence offending behavior (Merton 1938). In empirical tests of these theories, indicators of social disorganization and routine activities are constitute to exhibit the strongest and nearly consequent furnishings on crime (for a meta-assay, meet Pratt and Cullen 2005). Valuable reviews of social disorganization research and related theoretical thinking on neighborhood effects together with of import suggestions for futurity directions are offered past Sampson et al. (2002) and Kubrin and Weitzer (2003).

The main social mechanisms have been summarized by Sampson and collaborators (2002) under iv categories: social ties and local interactions, referring to local interpersonal networks of friends and kin and neighborly exchanges; norms and collective efficacy, based on dissimilar dimensions of civilization, social cohesion, trust, and social control (Sampson et al. 1997); institutional resources, which include neighborhood organizations, family wellbeing support centers, youth centers and the like; and routine activities (Cohen and Felson 1979), referring to the mix of residential, commercial or industrial state use and also the design of daily routine activities which facilitate access to local desirable targets by potential offenders living exterior the neighborhood. The latter encompasses spatial mismatch theory (Kain 1968), which highlights the distance between abode and workplaces among population subgroups, a phenomenon also understood as institutional isolation (Wilson 1987).

These dimensions of neighborhood processes, while analytically distinct, are empirically related. Yet few studies have examined the nature and extent of these relationships. Sampson and Graif's study (2009b) is an exception that investigated the social networks of friends and kin and reciprocal exchange, collective efficacy (the power of residents to realize mutual goals), civilization (norms of conduct for different age groups), institutional engagement (neighborhood activism, involvement in local organizations, schools, and churches) and neighborhood leader contacts within and outside the community. They concluded that "as residents seem to disengage and are more than cynical in disadvantaged communities, community leaders become more intensely involved in seeking resource, frequently from afar" (Sampson and Graif 2009b, p. 1601). Independent of disadvantage, another study found that internal community network structures are positively associated with trust among leaders and amidst residents (Sampson and Graif 2009a). When networks extending outside the community shape the density of internal networks (Sampson 2012), nosotros might expect additional improvements in trust and other dimensions of social order. This literature implies important, notwithstanding understudied, relationships between the individual or parochial levels of command and the external, public level (Hunter 1985, detailed below) with consequences for command of offense particularly in disadvantaged communities.

Despite great advancements on the theoretical and empirical testing of neighborhood level mechanisms, nosotros hold with Kubrin and Weitzer'due south (2003, p. 387) cess that "compared to the big number of studies on the effects of intra-neighborhood factors on crime, surprisingly niggling attention has been given to the role of exogenous determinants, and very little is known about the connections and interactions between internal and external factors. This would be a fruitful avenue for time to come research, and would rightly aggrandize the scope of social disorganization theory in a more macro direction." Beneath, we nowadays important recent developments relevant to bridging the internal-external mechanisms gap and offer boosted suggestions for the future.

Neighborhood definitions and scales of spatial exposures

Over twoscore years ago, Hunter and Suttles (1972) stressed the importance of multiple scales of measurement. They place 4 scales: the "face up-block," where residents tend to know each other; the "dedicated neighborhoods," the smallest areas with singled-out identities recognized by outsiders and insiders; the "community of limited liability," where local participation depends on residents' attachment to community; and the "expanded communities of limited liability," a big geographic surface area in which groups of residents come together only when needed to gain larger traction on specific political or economic decisions. Importantly, each of these traditions uses pre-defined, administratively-bounded areas. Since the 1970s much of the measurement of neighborhoods in criminal offence enquiry spanned the meso-to-macro scales, from demography tracts (Graif and Sampson 2009) to community areas (Sampson and Graif 2009a, 2009b) to counties (Messner and Anselin 2004). More recent studies have fabricated important advances at the micro-level likewise, illustrating the importance of local network groupings (Hipp et al. 2012), blocks (Hipp 2007) and street segment dynamics (Weisburd et al. 2004) in shaping offense.

A express merely growing number of studies, however, have adopted a unlike framework altogether—eliminating dependence on administrative boundaries. These researchers ascertain neighborhoods egocentrically, as the geographic context around an individual's residence or around a cake contained of neighborhood administrative boundaries (Hipp and Boessen 2013). The features of the surround that are closest geographically to the focal residence are assumed to be almost influential (Tobler 1970). Work in geography also has used kernel density analyses and routines that treat the globe every bit a continuous surface (Matthews 2011). A major advantage of these analytic frameworks is an acknowledgment that access to resources is often facilitated past geographic proximity (e.thou. access to youth services may decrease delinquent behavior) contained of artificially divers neighborhood boundaries.

The bounded neighborhood approach and the respondent-centered arroyo fed recurrent debates near the "proper" definition of the neighborhood. Nosotros believe this is a imitation dichotomy that may distract from thinking in an integrative way about local social processes. Similarly, the debates over the right geographic scale of the neighborhood mask an of import point: certain features of the surrounding non-residential areas may matter above and beyond the residential neighborhood, however divers. We revisit the iv types of mechanisms noted past Sampson and colleagues (2002) with respect to the immediate neighborhoods of residence in the beginning cavalcade of Table 1. Additionally, nosotros expand further to illustrate how these types of processes may interact in shaping individuals' victimization experiences and offending behavior with features of a) the broader area surrounding the immediate neighborhood of residence (the extended neighborhood, column two) and b) the neighborhoods frequented as part of peoples' daily routine activities (e.1000. the neighborhood of workplace or of close friends, column three). These examples may be translated into research hypotheses in future studies.

TABLE 1

Examples of Neighborhood Mechanisms from Extended Spatial and Network Perspectives

Immediate neighborhood (monadic neighborhood) Residential neighborhood mechanisms (Sampson et al. 2002)	Extended neighborhood (dyadic neighborhood) Features of surrounding areas	Network of Neighborhoods (polyadic neighborhoods) Features of neighborhoods on residents' routine activity routes
1. Social ties/interaction	Density of friendship and kin groups in nearby areas may compensate for lack of interaction within focal neighborhood.	Gang presence on residents' route to school/work may undermine the supervising role of local master groups in the focal neighborhood
2. Norms and Collective Efficacy	Collective efficacy in nearby neighborhoods may act equally a buffer against crime in focal neighborhood; A single criminal offence hotspot nearby may spillover and undermine the function of CE in focal neighborhood	When high numbers commute between two distant neighborhoods, crime control strategies may spill over every bit well
3. Institutional Resource	Density of certain types of organizations, social activities, recreational centers, family wellbeing support centers in nearby areas may deter crime and compensate for institutional isolation within a focal neighborhood	Accessible organizations, youth training family and support centers in walking proximity or en route to work for many residents, etc may recoup for low density of institutions within neighborhood or surroundings
4. Routine Activities (country use and daily routine activities)	The balance of residential and commercial country utilise in the surrounding area of a focal neighborhood, the nearby density of transportation nodes may shape local interaction patterns between local youth and transient population groups	Daily routine activity routes may enable targeting of the neighborhood by outside offenders or the targeting of outside areas past local offenders

The spatial embeddedness of neighborhoods

Information technology has long been shown, in multiple cities, that poverty and crime are both associated with each other and showroom spatial clustering (Peterson and Krivo 2010). In improver, social processes like neighborhood trust and commonage efficacy also cluster in infinite, and the spatial covariation betwixt poverty and neighborhood processes remained strong over the past four decades (Sampson and Graif 2009a). Moreover, the associations between neighborhood poverty and crime tend to be similar for multiple neighborhoods that are geographically proximate to each other, even though they vary from 1 section of the metropolis to another (Graif and Sampson 2009).

Given the progress in highlighting the ecological levels of covariation between poverty and criminal offense, it is surprising that advances in our commonage understanding of spatial dynamics at the ecological level accept not been integrated into the analytical framework of neighborhood effects on individuals (for an exception, see Sampson et al. 1999). This gap is related to the fact that we still know little about the processes underlying observed spatial clustering (Kubrin and Weitzer 2003). These patterns are in part attributed to measurement bug and in role to processes of contagion or improvidence, whereby nearby criminal offense activity spills over neighborhood boundaries (Anselin et al. 2000; Tita and Griffiths 2005). Other processes assumed to explicate clustering are residents' daily movement and increased exposures to run a risk factors in nearby neighborhoods. To the extent that effects of spatial proximity are in large part due to overlapping activity spaces, a more than general form of interdependence—which transcends geographic proximity while subsuming some aspects of it—may exist inter-neighborhood connections forged as a result of individuals' frequent motion (e.g. daily commuting) beyond space.

Non-residential neighborhoods and routine activeness spaces

Individuals routinely travel outside the neighborhood of residence for leisure and work. Pathways of movement beyond large distances may increase variability of access to resources, institutions, information, and people in ways that may bear on offense. Furthermore, much of the time spent in the neighborhood of residence is spent inside the domicile, when the objective risk of committing crime or being victimized is arguably low (Agency of Labor Statistics 2013). Despite increasing calls for definitions of neighborhood context that take into account individuals' daily activity patterns (Cagney et al. 2013; Matthews 2011; Matthews and Yang 2013), well-nigh social science literature however relies on demography tract of residence as the operational definition for the neighborhood of influence. However, research on the journey to crime indicates that up to lxx percent of crimes are committed past individuals outside their neighborhood of residence (Bernasco 2010; Wikström 1991, p. 213-223). Moreover, compared to violent criminal offense, holding crimes are committed farther away from offenders' neighborhoods of residence (White 1932). Additionally, Bernasco (2010) finds that locations where offenders lived in the past are more likely to be called as the location of current offending. Evidence on the importance of not-residential contexts in the study of crime is thus condign increasingly more salient.

The statement that researchers need to focus on relevant contexts other than the neighborhood of residence is not new to folklore (eastward.g. Foley 1950). McClenahan (1929) was one of the first to argue that urban residents' activities are rarely located within the immediate vicinity of the home. Routine activity patterns have been shown to matter for individuals' outcomes. Inagami and colleagues (2007) suggest that the negative effects on health of living in disadvantaged neighborhoods may be confounded and suppressed past exposure to non-disadvantaged, non-residential neighborhoods in the course of routine daily activities (i.e. grocery shopping). More recently, both qualitative and quantitative research in folklore has highlighted the importance of nonresidential contexts (Matthews, 2011). Other disciplines too accept started to adopt action infinite approaches and are beginning to focus on nonresidential neighborhoods (Cagney et al. 2013; Zenk et al, 2011). To date nevertheless, few studies have assessed the bear upon of individual activeness spaces on the propensity to commit criminal offense or become the victim of crime.

One notable exception is a contempo study of youth in a United kingdom city (Wikström et al. 2010), which showed that more than 54% of respondents' awake time was spent outside their dwelling surface area (Figure 2). Those with higher propensities for crime were exposed more often to criminogenic settings outside their home and schoolhouse areas and, in such settings, were more probable to become involved in criminal behavior. More than than half of the respondents' crimes were committed at locations central to their routine activities. These findings highlight the importance of designing new studies that do not rely on residential contexts as the merely purveyor of contextual effects.

Examples of Cross-Neighborhood Activity Spaces

Source: Adapted from Wikström et al. 2010. With kind permission from Springer.

Networks of neighborhood exposures

The piece of work on the importance of not-residential neighborhoods on offense and victimization provides some show for the necessity to study neighborhoods non as isolated, independent places but rather as parts of a larger, interconnected network of places. This type of perspective also has roots in earlier sociology, geography, and planning (see Matthews 2008). In the early sixties sociologists wrote nearly the "customs without propinquity" or spatially dispersed communities (Webber 1963) . Later Wellman (1979) discussed "community liberation", extended social networks, and long altitude communications such as "networks in the global village" (Wellman 1999) which provides a bridge to the "new mobilities enquiry" image (Larsen et al. 2012). We reintroduce and emphasize the idea that neighborhoods are part of a larger organization of resource exchanges, in the form of networks, between places.

Our network perspective also draws on Hunter's (1985) discussion of the importance of three core types of relational networks in shaping neighborhood social command. The "private" social gild refers to intimate informal principal ties within a neighborhood (east.thousand. kin and friends) which can control offense through the threat of social disproval or other forms of impecuniousness. The "parochial" order is given past the broader connections with local institutions such as schools, churches, or community organizations, characterized past weaker attachment than the primary networks but nonetheless important. Finally, the "public" social social club describes a community's connections to external organizations and institutions that facilitate the mobilization of resources, mediate the ability of local networks to control local crime, and sometimes even enable the foundation of local institutions (also Taub et al. 1977).

The literature to date has predominantly focused on the private or parochial dimension of law-breaking control, with little attention to the public dimension (Bursik and Grasmick 1993). Interestingly, the individual and parochial ties often extend across space to create the foundation for public control. In a creative approach to neighborhoods every bit inter-related friendship networks, Hipp and colleagues (2012) bear witness that while a loftier proportion of teens' friends are predictably spatially amassed, many ties cross neighborhood boundaries over large geographic distances. To the extent that individuals' contextual exposures are defined through their interactions, these findings underscore that a focus on only the administrative area of residence would miss substantial exposures to many friends' neighborhoods. ^ane

The extent of betwixt-neighborhood connections in Chicago are investigated in a recent monograph, Sampson's (2012) Dandy American City, as a function of residential mobility and nominations of influential people. Sampson (2012, pp. 309-310) finds it surprising "how lilliputian neighborhood networks have actually been studied, equally opposed to being invoked in metaphorical terms. … [P]rior research is dominated by a focus on private connections and an "egocentric" perception of social structure. … [R]arely has social science documented variations betwixt communities in social networks, much less the citywide construction."

A recent article past Slocum and colleagues (2013) addresses in function this gap by showing that organizations whose function it is to bridge to the larger community and secure resources for the local residents (e.g. customs boards, political groups, economic development centers) are significantly associated with lower trigger-happy and property criminal offense, even after controlling for multiple features of the community. All the same, few empirical studies exist that show how neighborhoods are connected and more specifically how these ties matter for crime related outcomes. Simply as residential neighborhood contexts thing to individuals through their connections with institutions and organizations within it (Tran et al. 2013), similarly, interest in non-residential neighborhoods may be consequential for criminal beliefs or victimization gamble.

Broader social phenomena highlight the importance of the interconnectedness of neighborhoods. For example, economic declines have been found to play a role in increasing violence (Catalano et al. 2011; Ellen et al. 2013) but the testify tends to be mixed and fiddling is understood virtually the underlying mechanisms. We suggest that through plant closures and mass layoffs, recessions may sever disquisitional interaction pathways (i.east. resource exchange in the class of labor) between neighborhoods. Despite a long tradition of research on spatial mismatch in employment prospects (Kain 1968), understanding violence in the context of a neighborhood's connectivity to or isolation from other particularly influential communities in the city is underexplored.

In sum, equally individuals motion near space and across neighborhoods within urban contexts, patterns of behavior amass to create functional ties between sets of neighborhoods. Such ties may turn out to be as important for neighborhood change as spatial proximity is observed to be. In other words, underlying (or complementing) the spatial clustering of poverty and law-breaking among neighborhoods in a urban center may be a broader network structure of interdependence governed by how people routinely motility through the urban landscape. To the extent that communities are connected to others who are successful in dealing with offense, those strategies and tools may be transmitted through such ties (i.e. innovation diffusion).

Methodological considerations in the study of neighborhood networks

The empirical study of "networks of neighborhoods" is relatively new and underdeveloped. While we cannot offer definite approach to the written report of neighborhood networks, we provide some guidance based on prior inquiry and the emergence of data and methods to study complex networks. We highlight 5 relevant macro-level studies and their commonalities and differences along six dimensions: the nodes (neighborhoods) and the ties (relationships betwixt nodes) as units of analysis; the levels of analysis; the blazon of analysis; the blazon of data used; and the questions of interest. This information is intended to provide readers with an overview of the types of methodological choices when designing a study of neighborhood networks.

In the networks of neighborhood approach, the nodes, or units of analysis, are oftentimes a geographic subdivision. In our selected examples, the operational definition of nodes range from authoritative definitions of "neighborhoods" of the Paris Commune in the tardily xix century and community areas in gimmicky Chicago (Gould 1991; Sampson 2012) to tracts (Schaefer 2012) and more complex units like gang turfs (Papachristos et al. 2013). The definition and measurement of the ties between nodes - arguably the main focus of a networks arroyo to neighborhoods – vary equally a function of the research question. In Gould'south (1991) report, ties were represented past the number of men living in a neighborhood serving in the same armed forces units as residents of another neighborhood. Papachristos and colleagues (2013) and Schaefer (2012) represented ties every bit gang violence and criminal co-offending relationships betwixt places, respectively. Sampson (2012) measured ties equally nominations by political leaders of people in the urban center who they believed they could rely on to "get things done" in their community. Thus, the core requirement of a tie is that it represent a form of meaningful interaction or human relationship between nodes (see Table ii).

TABLE 2

Selected Macro-level Applications of a Network of Neighborhoods Approach

Author(south)	Measurement		Levels of Analysis	Data	Blazon of Analyses	Area/Question
	Nodes	Ties
Gould (1991)	Neighborhoods ("Arrondissement" )– administratively defined geographic divisions in Paris	Enlistment in the same military units by residents of different neighborhoods	Macro-level nodal (neighborhoods) and dyadic (ties between neighborhoods)	Archival records - battalion size, deaths; Administrative data	GIS mapping and mixed nodal and dyadic network modeling. Network and spatial lag regression models	What predicts neighborhood armed resistance and similarity between neighborhoods in armed resistance
Papachristos et al. (2013)	Gang turf "spaces" Boston and Chicago	Fatal and non- fatal violence between gangs	Macro-level dyadic (ties between gang "turf" areas)	Archival - records of gang violence; Census data	GIS mapping and network (dyadic and structural) analysis-- exponential random graph models (ERGM)	How spatial proximity and group processes affect violence between gangs
Radil et al. (2010)	"Spatialized" gang territories in Hollenbeck area of Los Angeles	Inter-gang violence and rivalries (surveys of LAPD and gang members)	Macro-level dyadic (ties between rival gang "turfs")	Archival records of gang violence; surveys of informants	GIS mapping and network analysis – structural equivalence models (CONCOR)	How violence and rivalry between gang "turfs" are related to gangs' geographic location relative to each other
Sampson (2012)	Chicago community areas	Political "mover and shaker" nominations	Macro-level dyadic (ties betwixt communities)	Surveys, Census and administrative data	GIS mapping and network modeling	How inter-community networks are related to the distribution of resources across communities
Schaefer (2012)	Census tracts in Maricopa County, Arizona (including Phoenix and 7 other large cities)	Juvenile co- offending between residents of different neighborhoods	Macro-level – dyadic (ties between tracts)	Archival - court records; Demography data	GIS mapping and network (dyadic and structural) analyses-- (ERGM)	How inter- neighborhood social similarities and geographic proximity influence co-offending relationships

The level of analysis is typically macro considering of the interest in inter-neighborhood interactions or how neighborhoods are continued. All the studies we selected examined exchanges between macro-level units defined as a neighborhood. Data sources vary depending on the topic of interest, though some common themes emerge from the types of information used. Three of the studies (Gould 1991; Papachristos et al. 2013; Schaefer 2012) used archival records whereas Sampson (2012) used a prospectively longitudinal survey format to collect data. Other types of data that link places to other places - including but not limited to resource exchange (east.g. financial exchange; commuting to work), criminal exchange (e.g. court records of co-offenders' neighborhoods of residence; police reports linking offenders' or victims' accost and crime location), or political exchange (due east.g. nominations of "movers and shakers;" political interactions) - can be used to appraise neighborhood networks. As an instance, 1 ongoing study (Writer 2013) uses police records and administrative data to connect employers' location and employees' neighborhood of residence to examine the extent to which commuting to tearing neighborhoods increases victimization rates among the residents of a focal neighborhood (Effigy iii).

Inter-Neighborhood Networks and Exposure to Violence

Source: Adjusted from Author (2013).

The leftmost map represents Chicago's 77 community areas while the middle and the rightmost maps are network representations of the communities in the highest thirtile (cerise nodes) and everyman thirtile of violence (light-green nodes), respectively. Customs areas are represented as nodes and situated in geographic space according to the latitude and longitude coordinates of their centroids. Ties between nodes represent workers living in i neighborhood and commuting to the other. The arrows point toward the neighborhood of work and show the extent to which communities of similar violence level are connected to each other or not. This ecological perspective on networks of communities opens the field to new perspectives on age-old questions related to structural embeddedness, selection and exclusion, deportation of crime, and the diffusion of norms relevant for crime control (see likewise column 3 of Tabular array 1).

With respect to modeling and belittling approaches, all of the selected studies utilize a combination of GIS mapping, spatial, and network analyses. These methods are used to assess how different types of neighborhoods are distributed over space, to calculate the geographic distance between them, and to assess the association between social and spatial distance on the one hand and prevalence of inter-neighborhood connections on the other. The network analyses utilize two different approaches: a) a nodal approach (Gould 1991) where the issue and most covariates are modeled egocentrically at the nodal level while the dyadic relationships are simply summarized in the form of a network autocorrelation term, similar to a spatial autocorrelation term, and, b) a dyadic and structural approach, using exponential random graph models (ERGMs), where the outcome is at the necktie level equally are many covariates, simply nodal attributes and structural backdrop of the overall network are also included (Papachristos et al 2013; Schaefer 2012).

Non represented among these examples, only all the same an important arroyo for the futurity that allows for changes in the network structure over time, is modeling using SIENA (Snijders 2001). A relatively recent development, SIENA is gaining traction in examining longitudinal networks at the private level but little research so far has made utilise of information technology in examining change in a network of neighborhoods. For instance, one type of question that this strategy would assist address in the future is whether increases in neighborhood unemployment contribute to subsequent increases in co-offending relationships betwixt whatsoever 2 neighborhoods or whether co-offending occurs before or independent of increases in unemployment. Other types of questions may focus on the improvidence of criminal offence betwixt neighborhoods (how shots fired beyond neighborhoods may pb to further shootings in retaliation) or on crime deportation (how policing in a neighborhood pushes offense into new places) (Tita and Cohen 2004). In sum, while the macro-level written report of networks of neighborhoods is nonetheless in its early stages, existing examples are encouraging.

When the primary interest focuses on private behaviors, experiences, and outcomes related to law-breaking and victimization, studies of neighborhood network effects may combine network analytic tools with more typical approaches to the study of neighborhood effects or peer influences. Just like exposure to a network of delinquent friends affects individuals' attitudes and delinquent behavior, exposure to criminogenic places in which individuals spend considerable fourth dimension (whether their own neighborhood of residence or outside it) may shape their attitudes and behavior. The mechanisms of peer influence on individual behavior may simply in role overlap, if at all, with the mechanisms of identify influence. However, the methodological advancements in assessing the role of one'due south network of peers (Kreager et al. 2011) may also exist valuable to scholars interested in assessing the role of an private'southward network of neighborhoods.

The logic of the typical multilevel approach, for case, equally used in estimating effects of peer groups or of residential neighborhoods on individual attitudes and behavior related to criminal offence and victimization may be too used to approximate the effects of a network of neighborhoods. The core difference consists in assessing criminogenic exposures based not merely on where respondents live but based on the neighborhoods they frequent when they hang out with friends, go to school, shop, or commute to work. Exposures to each place can be weighted by the time respondents study (or are observed) to spend in that location or by another index representing functional ties (eastward.m. the number of friends they know in each place). GPS, smartphones, and tracking technologies enable drove of information that allows for weighting by the elapsing of exposure to a identify. Alternatively, researchers may account for the time spent in traditional "nodes" such equally home, work, and school as captured through activity logs (Basta et al. 2010). To account for individuals' exposures to multiple non-nested places, multiple-membership models may constitute a valuable strategy (Browne et al. 2001).

Related modeling strategies include the use of network lagged variables in hierarchical linear models. This would be similar to the utilise of spatial lag variables in multilevel analyses (see Crowder and South 2011; Sampson et al. 1999) simply instead of geographic proximity it would model the lag every bit a function of existing network ties. For different examples of modeling social and spatial networks we direct the reader to Entwisle and colleagues (2007) and Larsen and colleagues (2012).

Conclusions and directions for the future

In this article, we surveyed classic and recent studies on neighborhood effects and on the spatial stratification of poverty and urban law-breaking. We fence that for a more than complete understanding of the impact of neighborhoods and poverty on criminal offence, sociological inquiry would benefit from expanding the analytical focus from the residential neighborhoods to the network of neighborhoods (residential and not-residential) that individuals use during the course of their routine daily activity.

The reemergence of scholarship on activity spaces offers much hope for studies of non-residential contexts and law-breaking. These non-residential contexts may add variation in criminogenic exposure, which would in plow influence their offending behavior (Wikström et al. 2010) and victimization risk. We proposed that non-residential exposures may be idea of as a part of a "network of neighborhood exposures" that includes the neighborhood contexts of the workplace, school, friends' homes, recreation activities, or wherever individuals tend to spend their time on a routine basis.

Our arroyo is too related to insights on the importance of inter-neighborhood connections over big geographic distances directly or indirectly implied in studies of residential mobility (Sampson 2012), actress-local organizational connections and involvement (Sampson and Graif 2009a, 2009b; Slocum et al. 2013), daily commuting distances (Zenk et al. 2011), and spatial mismatch (Holzer 1991; Kain 1968). We suggest that the criminogenic role of chronic unemployment resulting from the spatial mismatch between the location of jobs and the location of housing may be in function due to the absenteeism of positive externalities of inter-neighborhood connections that may be forged through daily mobility beyond the urban landscape. Nosotros believe that our collective understanding of the causal human relationship betwixt neighborhood poverty, inter-neighborhood networks and offense will be greatly advanced by creative designs applied to studies of the contempo Great Recession and economic decline more by and large. More inquiry is needed on how changes to activity spaces due to plant closures shape neighborhoods and law-breaking and what happens when communities get disconnected as a result of economical restructuring.

Our chief purpose was to highlight the importance of studying how neighborhoods are related across space for advancing our collective agreement of macro-level patterns of neighborhood crime as well as individual attitudes and behavior. Even so, the study of inter-neighborhood connectivity is of import for our understanding of urban stratification across space above and beyond crime. For instance, Krivo and colleagues (2013), using the Los Angeles Families and Neighborhoods Survey (L.A. FANS) data, institute that social inequality is reproduced through daily activities. That is, people living in socioeconomically advantaged neighborhoods similarly tend to behave activities in (i.eastward. piece of work, recreation, shopping, dining) neighborhoods that are not-overlapping with those in which disadvantaged populations conduct activities. Individuals from disadvantaged areas rarely enter non-disadvantaged parts of the metropolis.

The comprehensive overview of the state of the field in the last decade and the discussion of the historical and theoretical context of the scholarship on urban poverty, neighborhoods, and crime left little room for addressing other of import and recurrent problems in the field such equally selection bias, ecological fallacy, and neighborhood alter. We recommend several fantabulous reviews for more detailed discussions on these (Kim et al. 2013; Kirk and Laub 2010; Kubrin and Weitzer 2003; Matthews and Yang 2013; Pratt and Cullen 2005; Sampson et al. 2002). We differ from previous reviews in our focus on a network approach to understanding neighborhood exposures. We call for new and creative research designs and analytical approaches to understanding urban crime that transcend the typical focus on the neighborhood of residence to include a focus on the broader context of routine activities. We also call for advancements in enquiry on urban poverty that investigate the salience of inter-neighborhood connections in evaluating criminogenic risk for individuals and communities.

Acknowledgments

The first author thanks the Social Science Research Institute and the Population Research Institute at Penn State (NIH grant # R24 HD041025) for support during the writing of this article.

Footnotes

¹While a focus on networks as neighborhoods is valuable, we would as well caution that the absence of friendships could alternatively actuate criminogenic processes like alienation and anomie.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928692/

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