Forecasting homicide in the red stick: Risk terrain modeling and the spatial influence of urban blight on lethal violence in Baton Rouge, Louisiana

Social Science Research 80 (2019) 186–201

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Forecasting homicide in the red stick: Risk terrain modeling and the spatial influence of urban blight on lethal violence in Baton Rouge, Louisiana

T

Matthew Valasika,∗, Elizabeth E. Braulta, Stephen M. Martinezb a b

Department of Sociology, Louisiana State Univeristy, Baton Rouge, LA, USA East Baton Rouge District Attorney's Office, Baton Rouge, LA, USA

ARTICLE INFO

ABSTRACT

Keywords: Risk terrain modeling Homicide Environmental criminology Spatial risk factors Urban blight Violence exposure

Incorporating features of the built environment, risk terrain modeling (RTM), is used to predict future criminal events in micro-units (i.e., city blocks). The current study examines the application of RTM to forecast homicide in the capital city of Baton Rouge, Louisiana while including a novel environmental risk factor, blighted properties. Based upon the extant literature and knowledge of the city, eighteen environmental risk factors are expected to spatially influence homicide. Results indicate that places most at risk of experiencing a homicide are located in areas where blighted properties are concentrated and in close proximity to convenience stores. RTM successfully identities and evaluates environmental risk factors that spatially influence lethal violence. Additionally, RTM is able to accurately forecast future acts of homicide. The results underscore how crime prevention through environmental design (CPTED) and blight remediation could be utilized as straightforward and prudent strategies to reduce lethal violence.

1. Introduction The United States, for over two decades, experienced a shift in the overall patterns of lethal violence to levels not previously witnessed since the early 1960s (Rosenfeld, 2011, 2018). While low rates of crime and violence prevail throughout much of America, post-2000 crime patterns are “largely specific to individual cities” (Wallman and Blumstein, 2006: p. 343). Thus, even as the overall number of homicides trend downwards, local law enforcement agencies, stakeholders and policymakers continue to struggle with how to further moderate levels of lethal violence throughout many urban centers, particularly in the South (Huff-Corzine et al., 1986; Lee and Shihadeh, 2009). In fact, violence does not transpire in a vacuum. Murder has a context that structures these acts of lethal violence, with the environment being a direct contributor that impacts the spatio-temporal patterns of these crimes (Kondo et al., 2018; Papachristos, 2009; Sampson, 2013). Research has highlighted the toll that the physical disorder of abandoned and distressed buildings takes on a community, directly influencing patterns of not just crime but also public health (Garvin et al., 2013; Kondo et al., 2015; Skogan, 1990; Wilson and Kelling, 1982). While some scholars suggest that social structural conditions (e.g., poverty/ disadvantage, residential instability, etc.) of a neighborhood are actually the principal roots contributing to elevated levels of crime, yet, others contend that the physical landscape is responsible (Taylor, 2000). More recently, there has been increased attention to the influence that physical disorder, in particular urban blight, has on crime patterns in a community (Branas et al., 2016, 2018; Garvin et al., 2013; Kondo et al., 2015, 2018). Research has shown that blighted and abandoned properties are crime attractors, where

∗

Corresponding author. E-mail addresses: [email protected] (M. Valasik), [email protected] (E.E. Brault), [email protected] (S.M. Martinez).

https://doi.org/10.1016/j.ssresearch.2018.12.023 Received 24 August 2018; Received in revised form 9 November 2018; Accepted 22 December 2018 Available online 24 December 2018 0049-089X/ © 2019 Elsevier Inc. All rights reserved.

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offenders are less concerned about the attentiveness of police and/or community residents (Branas et al., 2018; Mills, 1990; Spelman, 1993; Tita, 1999). Studies have also shown that communities which treat blighted properties not only are able to disrupt crime pattern but also positively impact public health and economic viability of the neighborhood (Branas et al., 2012, 2018; Han, 2014; Kondo et al., 2015, 2018). The focus of much of the extant literature is on the relationship between social structural covariates and homicide across a range of areal units (i.e., cities, metropolitan areas, and states) (see Land et al., 1990; McCall et al., 2010). Generally, research has indicated that neighborhoods with greater levels of concentrated disadvantage and residential instability are significantly more likely to experience lethal violence (Kondo et al., 2015, 2018; Mears and Bhati, 2006; Morenoff and Sampson, 1997; Rosenfeld et al., 1999; Thompson and Gartner, 2014; Jones-Webb and Wall, 2008). While these macro-level studies have provided an advanced understanding about homicide trends and patterns, a lacuna remains on the number of studies examining how environmental characteristics contribute to lethal violence. A spatial diagnostic approach known as risk terrain modeling (RTM) provides a procedure that is able to assess an area's risk of experiencing future acts of homicide (Dugato et al., 2017; Giménez-Santana et al., 2018; Valasik, 2018). Best described as a “merging together of key concepts from environmental criminology and spatial analysis,” RTM is a technique with the ability to identify statistically significant characteristics of the built or physical environment and their association with each other in regard to some outcome incident (e.g., homicide, robbery, burglary, etc.) to ascertain a place's level of risk (Caplan and Kennedy, 2016: p. 11). Risky places, those areas with an elevated level of risk, have traditionally been referred to in environmental criminology (see Brantingham and Brantingham, 1995) as a crime generator or a crime attractor, which influence an area's geographic patterns of crime. As motivated offenders navigate their surroundings in search of a suitable target, they interact with the local environment, continually evaluating an area as favorable or unfavorable for crime (Brantingham and Brantingham, 1993). Criminogenic areas generate or attract crime by providing the location and time for the convergence of a motivated offender, suitable target, and lack of capable guardianship (Cohen and Felson, 1979; Felson, 1987). Additionally, while all places are at risk of exercising a crime some places are riskier than others, with crime occurring in a location as a result of the coalesced influence of exposure to prior crime incidents and the spatial vulnerability to criminogenic features (Caplan and Kennedy, 2016; Kennedy et al., 2016). RTM is able to discern the statistical significance of environmental risk factors that spatially influence crime by computing a relative risk score, with larger values indicating that a particular place is more likely to contend with a future criminal event (Caplan et al., 2017). As such, RTM is a systematic approach that is able to ascertain not just where crime is likely to occur but also answer why a particular place has an elevated risk of experiencing crime. The current study uses RTM to investigate which environmental risk factors are positively associated with homicides in Louisiana's capital city of Baton Rouge. Prior RTM research has examined patterns in overall violent crime (Drawve et al., 2016; Piza and Gilchrist, 2018) or disaggregated homicides (e.g., organized crime, gang-related, etc.) (Dugato et al., 2017; Valasik, 2018), yet studies on total homicides remain scarce (see Giménez-Santana et al., 2018). Additionally, while prior research has incorporated various measures that capture the character of a building including vacancy (Thomas and Drawve, 2018), foreclosure (Caplan et al., 2014; Garnier et al., 2018; Piza et al., 2018; Piza et al., 2016) at-risk/problem (Caplan et al., 2014; Garnier et al., 2018) or demolished (Lersch, 2017), however, most studies fail to clearly define these building designations making it difficult for findings to be readily compared across jurisdictions (for exceptions see Caplan et al., 2014; Lersch, 2017). This study builds upon the nascent RTM literature analyzing lethal violence and is the first to incorporate an explicitly defined and novel environmental risk factor that measures building character -blighted properties-in identifying the spatial correlates of homicide. 1.1. Review of the literature 1.1.1. Environmental criminology Initially introduced in theoretical form by Guerry (1883) and Quetelet (1969/1842) in the 19th century, spatially informed application of criminology dates back to the Chicago School and Park and Burgess’ (1925) concentric zone theory. The theory held that cities would take on a sort of bulls-eye structure of five concentric rings, with the most disadvantaged areas carrying the most social and physical deterioration within the central business district. Each subsequent ring/zone radiating outward from the core would be more stable and prosperous than the last (Park and Burgess, 1925). The zone adjacent to and surrounding the core, known as the zone in transition, has an abundance of affordable (though poorly maintained and overcrowded) housing. This zone is also spatially proximate to a vast supply of jobs in the central business district, yet it experiences a constant churning of impoverished immigrants seeking to improve the standard of life. Linking patterns of delinquency and ethnic succession to concentric zone theory, Shaw and McKay (1942) demonstrate that regardless of the racial/ethnic composition of the inhabitants the zone in transition continues to experience consistently high levels of delinquency and crime over time. The consistent nature of this spatial pattern of crime, independent of who lives in the area, suggests that the environment itself is an important factor in understanding crime. Research has also investigated Hoyt’s (1939) sector theory, which argues that cities do not develop in the uniform concentric circles proposed by Park and Burgess (1925), but instead emanate out from the central zone in more partitioned sectors each with a distinct purpose. Specifically, the sector model maintains the centricity of the central business district (CBD), but plots a major transportation line (e.g. railways) asymmetrically bisecting the city. Major industries grow along the route, providing easy access to both transportation and resources. Poor residential areas are immediately adjacent to the major industry, with inexpensive land and low quality of living due to its proximity to the industry. Middle and high class residential areas occupy sections of the city opposite of the CBD, sitting the furthest from the downtown and providing better quality of life (i.e., cleaner air, less traffic and noise) (Hoyt, 1939; Harris and Ullman, 1945). This theory allows space for the sectors to emanate from the core, a distinct advantage when 187

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modeling city growth and its emphasis on transportation routes as a foundational part of city structure, a notable critique of concentric zone theory. Yet the sector model has two key limitations. First, it's structural plot assumes a single central industrial hub, a critique addressed by the multiple nuclei theory. Second, the sector theory does not account for transportation corridors other than public conveyance. Automobiles allow workers to live in less expensive housing outside the city, creating suburbs and commuter towns, inconsistent with the ecology predicted by the sector theory (Schwirian, 2007; Zeigler et al., 2003). A third theory of city orientation, the multiple nuclei theory, recognizes that while many cities begin with a central hub, other smaller growth points will spring up around a burgeoning city (Harris and Ullman, 1945). These growth points, or ‘nuclei,’ develop small industries immediately surrounding them. This theory integrates the availability of private transportation, arguing that easily available transportation (i.e., the automobile) allows for travel between multiple regional centers based upon a variety of needs. The city of Los Angeles is a prime example of a multinucleated city with industrial-residential clusters as satellites within a metropolitan orbit forming a network of urban villages (Dear, 2002). Frequently cited examples of multiple nuclei city orientations include airports with hotels, retail, and pay-parking businesses surrounding them, or universities and the accompanying bookstores, retailers, and coffee shops. These separate nuclei account for Hoyt’s (1939) critiques of concentric zone theory and portray the complexity of modern urban centers (Schwirian, 2007; Zeigler et al., 2003). Building from these prior ecological studies, Brantingham and Brantingham (1981) formulated crime pattern theory, suggesting that the primary way that an offender and a potential target meet is through shared spaces. According to crime pattern theory, the daily movements of individuals and groups from location to location create routes traveled by groups of people along relatively consistent schedules. These routes, as well as the number and types of people following them create patterns of crime across space and time (Brantingham and Brantingham, 1993). Each core location of an individual's daily life, such as home, work, school, or recreation spaces, referred to as ‘nodes’, and the pathways between them represent a general ‘activity space’ for an individual (Brantingham and Brantingham, 1993; Golledge and Stimson, 1997). The pathways and nodes visited most frequently by potential offenders are referred to as the offender's awareness space. Crime pattern theory proposes that paths and nodes which contain the highest volume of traffic are also the most likely places to experience crime. The exception to this rule is a ‘buffer zone’ around an offender's residence in which he/she does not offend, for fear of being recognized by neighbors or other individuals frequenting their neighborhood (Brantingham and Brantingham, 1993; Hicks and Sales, 2006). The types of environments that offenders deem favorable to crime include crime generators and crime attractors (Brantingham and Brantingham, 1995). Crime generators produce crime simply by creating times and locations in which suitable targets are available to be encountered by otherwise-unmotivated offenders, offering ideal opportunities for the exploitation of these individuals (Angel, 1968; Brantingham and Brantingham, 1995; Kinney et al., 2008). Crime attractors have characteristics that provide wellknown opportunities of a particular type of crime. The offenders learn of these locations and are drawn to them for the purpose of committing crimes. For example, a poorly secured parking lot or shopping mall offers prime opportunities for auto or retail theft, while a red-light district lures sex workers and solicitors (Brantingham and Brantingham, 1995; Kinney et al., 2008; Spelman, 1993). Unlike crime generators, where offenders unknowingly stumble upon a location with enticing opportunities, attractors tend to draw in offenders who are otherwise unconnected to the area. That is, the offender's awareness space does not usually include the crime attractor locations. When an offender's awareness space overlaps with a crime attractor, it is usually because the offender moved into said space after initially stumbling upon it (Brantingham and Brantingham, 1995). Crime generators and attractors largely occupy spaces designed for commercial or public activities. When characteristics of an environment limit an individual's mobility, both offenders and targets are constrained within that environment, and a ‘hotbed’ of crime is created when the offender engages in criminal activity within this cloistered space (Weatherburn et al., 1999). A welldocumented example of crime hotbeds in the literature are public housing complexes (Griffiths and Tita, 2009). While these facilities were designed to provide a sense of community, with buildings facing each other and a communal space in the center, yet, their insular structure restricts informal surveillance typically provided in traditional neighborhoods. Disagreement exists on the realworld relationship between public housing complexes and hotbeds (Griffiths and Tita, 2009; Weatherburn et al., 1999; Barthe et al., 2014), yet, it serves as a demonstration of how an environment's social, political, and/or economic barriers shape the pattern of offending. While crime pattern theory places more of an emphasis on the proximate influences that deter or incite crime, a similarly environment-focused approach, routine activities theory, focuses on how a constellation of three factors forms the conditions that produce crime. Routine activities theory was developed to explain why crimes are committed by certain individuals, against specific victims, and in particular places (Cohen and Felson, 1979). Specifically, the theory focuses on how everyday activities of individuals' and groups' lives can intersect in such a way that, when three conditions are met in time and space, a criminal event can or will occur. According to routine activities, there are three components to a crime: (a) a motivated offender, (b) a vulnerable target, and (c) a lack of capable guardians (Cohen and Felson, 1979; Felson, 1987). Guardianship can take many forms, including handlers (individuals and groups who oversee potential offenders), place managers (individuals and groups who supervise places) and guardians overseeing specific potential victims or targets (Eck and Weisburd, 1995; Felson, 1987; Felson and Eckert, 2018; Hollis et al., 2013). Additionally, guardianship is not mutually exclusive; security cameras, a crowd of people, dogs, or even signs warning ‘beware of dog’ can serve the function of guardians simultaneously. From its roots in rational choice, it is understood that any potential offender is a motivated offender, should he/she deem the act and conditions to be favorable to crime. Should any one of these three main elements be absent, however, the three requirements of routine activities would be incomplete and crime would not occur (Felson and Eckert, 2018).

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1.1.2. Blight and crime Integral to this discussion of crime generators and attractors is the relationship between violent crime and blighted properties. While blight has plagued many urban areas in decline for years (e.g., traditional rust-belt cities), blight has become a growing concern since the bursting of the housing bubble and ensuing recession (Boessen and Chamberlain, 2017; Branas et al., 2012; Herbert, 2018; Kondo et al., 2016; Kondo et al., 2015; Skogan, 1990; Spelman, 1993; Wheeler et al., 2018). Blight is generally defined as the “conditions upon or affecting premises, which are hazardous to the health, safety or welfare of the public, and/or conditions which are detrimental to property values, economic stability, or to the quality of the environment” and include conditions with a state of deterioration that properties are unable to be profitably restored, such as dilapidated structures, condemned properties, or abandoned adjudicated (City of Baton Rouge, 2018). As such, the presence of blighted properties serves as a clear indicator that a community is experiencing greater levels of physical disorder. Furthermore, physical disorder represents the level of maintenance for a neighborhood's physical environment which includes the condition of buildings, property surrounding a building, and vacant lots (Wilson and Kelling, 1982). Physical disorder, a central tenet of broken windows theory (Kelling and Coles, 1997; Wilson and Kelling, 1982), supplemented by Skogan’s (1990) Disorder and Decline, which argues that signs of physical disorder and minor offenses lead to crime. Accordingly, the accumulation of physical disorder in a community initiates a sequence of events that leads local residents to withdraw into their homes, disconnecting from neighbors and weakening or abandoning co-operative efforts to improve their surroundings (Skogan, 1990; Taylor, 1988). As pro-social residents withdraw from the public sphere, disordered individuals and groups fill the gaps, leading to poor social control and further physical and social disorder (Taylor and Gottfredson, 1986; Taylor, 1997, 2000). Following routine activities theory, any potential offenders will view these signs of incivility as indicating lower risk of being caught for their offense (Weisburd et al., 2012). Research also suggests that law enforcement officers are less attentive to locations where blighted and vacant buildings are prevalent, further increasing local residents' fear of crime (Branas et al., 2018; Mills, 1990). Anecdotal evidence also suggests that potential offenders are drawn to blighted or abandoned properties because they can act as safe havens, hangouts or staging areas for criminal activities and also be used as “cuts” to escape police detection (Spelman, 1993; Tita, 1999). Therefore, good physical maintenance of a neighborhood should be associated with decreasing the risk of victimization and inhibiting criminal opportunities (Branas et al., 2012, 2018; Eck and Spelman, 1987; Hackworth, 2016; Herbert, 2018; Kondo et al., 2015). Research has also revealed that blighted properties not only impact crime patterns but also influence public health (e.g., sexually transmitted disease, mental health, physical well-being, etc.) (Kondo et al., 2015, 2018). Blighted properties also inflict an economic penalty on a community by decreasing the local housing market, driving up insurance premiums, and lowering the overall equity and wealth of a neighborhood (Han, 2014). 1.1.3. Theory of Risky Places Places at greater risk of experiencing crime are the consequence of the combined influence of spatial vulnerability and exposure to crime incidents (Caplan and Kennedy, 2016). That is, the coalescence of environmental features which facilitate criminal behavior along with the presence of repetitive crime incidents over a short time period (i.e., local exposure) and being subject to high concentrations of crime (i.e., global exposure) are what make a place risky. Thus, the Theory of Risky Places suggests that an area's spatial vulnerability to crime, its risk level, can be discerned from the interplay of these three factors. Risk is then defined under the Theory of Risky Places “as a consideration of the probabilities of particular outcomes” (Caplan and Kennedy, 2016: p. 52). Risky places are specific spaces which are considered to have a greater likelihood of experiencing some particular outcome (e.g., crime, homicide) which is represented with a value that can be used for comparison with other places across a physical landscape. Furthermore, risk is not a static value but will change over time and space as additional crimes transpire, through police interventions, as local residents' perceptions about the physical environment shift and as the routine activities of motivated offenders and suitable targets are modified. As outlined by Kennedy et al. (2016) the Theory of Risky Places contains three propositions (see also Caplan and Kennedy, 2016). First, all places have some risk of experiencing a crime incident, however, some places are riskier than others due to the spatial influence of particular criminogenic features. For instance, places where there is a high concentration of bars or night clubs may have a greater risk of experiencing a violent crime (e.g., aggravated assault) than places where the concentration of drinking establishments is low. Thus, the geographic concentration of the criminogenic feature, bars or night clubs, exerts more influence on risk for places that are clustered by criminogenic feature than places where a criminogenic feature is dispersed. Second, the combined spatial influence of multiple criminogenic features increases a place's risk level making them more vulnerable to criminal victimization. The interaction between criminogenic features results in a place with a heightened vulnerability for risk of experiencing a criminal incident. Third, risky places emerge from not only spatial vulnerability but also exposure to criminal incidents. A place may be vulnerable to crime, however, if a crime has not occurred in that place before the likelihood of a future incident is low. In contrast, a vulnerable place that has been exposed to crime is much more likely to experience future criminal incidents. Thus, not all places are exposed to crime to the same extent, but having increased levels of vulnerability does highlight places which have a greater probability of experiencing criminal offending and victimization. Thus, the Theory of Risky Places provides a vulnerability-exposure framework to not only better understand where crime is more likely to occur in space but also presents the features of the physical environment that are influencing crime, rendering possible avenues for intervention. Kennedy et al. (2016) tested the propositions in the Theory of Risky Places by examining the appropriateness of this vulnerability-exposure framework on aggravated assaults through RTM. Their findings revealed robust and consistent support for all three tenets, clearly demonstrating the strong connections between vulnerability and exposure to crime (Kennedy et al., 2016). 189

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1.1.4. Spatial distribution of homicide and risk terrain modeling A steadfast topic in the field of criminology is how space influences the spatial distribution of crime and violence in an environment (i.e., a city, neighborhood, etc.). In the early 1990s with the desktop geographic information system (GIS) software being readily available, scholars, policymakers, and practitioners are finally able to expediently map crime distributions and analyze their relationship with other features within a community (Anselin et al., 2000). GIS advancement coincided with a homicide epidemic as the annual number of homicides in the United States increased to a high of 24,700 in 1991 (Cooper and Smith, 2011). With these improvements in GIS software researchers are able to examine the diffusion of violence, particularly homicide throughout urban areas (Cohen and Tita, 1999; Morenoff and Sampson, 1997; Tita and Cohen, 2004). The application of spatial analytic tools is important to understanding crime, homicide in particular, due to the non-randomness of violence in urban environments. It is important to recognize that neighborhoods do not exist as urban islands, in isolation from each other. As such, it is meaningful to incorporate a spatial orientation when examining patterns of violence and homicide (Chamberlain and Hipp, 2015; Morenoff et al., 2001). Land et al. (1990) pivotal study of homicide across a variety of areal units (i.e., cities, metropolitan areas, and states) finding that regardless of the unit of analysis, resource deprivation, population structure, and divorce rates consistently explained trends in homicide. Similarly, researchers find that homicide rates are elevated in neighborhoods in or near areas of concentrated disadvantage (Giménez-Santana et al., 2018; Mears and Bhati, 2006; Jones-Webb and Wall, 2008) and with moderate levels of residential instability (Morenoff and Sampson, 1997; Rosenfeld et al., 1999; Thompson and Gartner, 2014). McCall, Land, and Parker (2010) reaffirm these finding with more recent research. Research also reveals that homicide tends to cluster in space and is not randomly distributed across a cityscape but concentrated to particular areas in the urban environment (Braga et al., 2010; Griffiths and Chavez, 2004; Rosenfeld et al., 1999; Sampson and Morenoff, 2004; Valasik et al., 2017). Additionally, Papachristos and Wildeman (2014) highlight that even with neighborhoods that are economically disadvantaged the risk of being victimized in a homicide is directly tied to an individual's social networks. That is, victims of homicide are close in social proximity to other homicide victims. The combination of both social and spatial data to better understand violence highlights how spatial analysis techniques, such as RTM, can be employed to identify risky places. Caplan and Kennedy (2016: p. 12) developed RTM as a systematic approach to “statistically valid way to articulate vulnerable places” through the creation of risk of crime score for an area that is based upon the criminogenic characteristics present in the physical landscape, including the built environment. In this case, RTM is able to identify both the environmental risk factors associated with homicides in a particular area and assess how the spatial influences of these risk factors collocate to increase an area's vulnerability of experiencing a future incident of lethal violence. The physical presence of an environmental risk factor in addition to that feature's spatial influence is what contributes to a place's risk level (Caplan and Kennedy, 2016). RTM has been used successfully to analyze a range of crime types which include: aggravated assaults (Kennedy et al., 2016; Thomas and Drawve, 2018), burglary (Moreto et al., 2014; Dugato et al., 2018); carjackings (Lersch, 2017), felonious battery to police officers (Caplan et al., 2014); gang assaults and gang homicides (Valasik, 2018), gun crimes (Caplan et al., 2011; Drawve et al., 2014); motor vehicle thefts (Piza et al., 2016), organized crime homicides (Dugato et al., 2017), robbery (Caplan et al., 2017; Garnier et al., 2018), and overall violent crime (Drawve et al., 2016; Gerell, 2018). Analyzing spatial and social data with RTM allows for a better understanding of where “social processes responsible for ‘neighborhood effects’” related to lethal violence are transpiring and can be addressed by local stakeholders, policymakers and law enforcement (Tita and Greenbaum, 2009: p. 167). 1.2. Current study The present study examines the environmental risk factors that are positively associated homicides in the city of Baton Rouge, Louisiana. While recent research has utilized RTM to investigate patterns in homicide (see Giménez-Santana et al., 2018) prior studies have instead focused on patterns in overall violent crime (Drawve et al., 2016; Piza and Gilchrist, 2018) or disaggregated homicide types (e.g., gang-related, organized crime) (Dugato et al., 2017; Valasik, 2018). The current study builds upon this budding RTM literature examining violent crime by including a unique environmental risk factor that has yet to be utilized in any prior research, blighted properties. The subsequent analysis is broken up into two steps. First, the RTMDx Utility is used to distinguish which environmental risk factors are significantly associated with homicide and assess their spatial influence. Second, the predictive power of the produced risk map is evaluated through a Poisson regression with the risk values as the independent variable and the number of homicides in 2017 as the dependent variable. 2. Methodology 2.1. Research site The city of Baton Rouge is the capital city of Louisiana, and occupies a 74.8 square mile region on the east bank of the Mississippi River. It is the second largest city and metropolitan area in the state, with a population of 228,694 living in 57 community-designated neighborhoods (Priola and Kron, 2018; U.S. Census Bureau, 2017). Just over half of the residents are Black (54.9%) while white residents make up about a third (36.6%). Only a third (32.3%) of residents 25 years or older have a bachelor's degree or higher and overall residential mobility is high in Baton Rouge with approximately a fifth (18.8%) of residents living at a different address than the year before. Furthermore, just under half (49.5%) of residents own their home. A quarter of residents (26%) live below the poverty line with the median household income for a Baton Rouge resident being $39,969 in 2016, $17,000 less than the national median income. The unemployment rate (8.8%) is nearly double the national average (4.9%). Aside from its high unemployment rate 190

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Fig. 1. The homicide rate for the city of Baton Rouge, East Baton Rouge Parish and the United States from 1985 to 2017.

and residential mobility, the city has higher than average income inequality, with a Gini coefficient of 0.53, falling more than 1.6 standard deviations above the U.S. average of 0.48 (U.S. Census Bureau, 2017). Since the 1990s patterns of violence have symmetrically wavered in East Baton Rouge Parish and the city of Baton Rouge, but overall the city remains one of the most violent in America (Johnson, 2013; Kaplan, 2018; Thomas, 2017). Fig. 1 shows that the homicide rate in Baton Rouge increased from 14.6 in 1985, already quite high being approximately double the national rate of 7.9, to a near all-time high in 1993 with 32.3 then declining to a low of 18.1 in 2003 and yo-yoing to an all-time high of 38.7 in 2017 (Kaplan, 2018). Comparatively the United States national average in 2017 was eight times lower with a rate of 4.7 (Kaplan, 2018). Moreover, for 28 years, Baton Rouge occupied a top spot among state per capita murder rates (Lane, 2017; Fuchs, 2013; Grillot, 2012). In an effort to combat the excessive prevalence of violence in the city, Baton Rouge has hosted a number of interventions including Office of Juvenile Justice and Delinquency Prevention (OJJDP) sponsored initiatives including a Partnership to Reduce Juvenile Gun Violence Program operating between 1997 and 1999 (Sheppard et al., 2000) and between 2012 and 2015 the Baton Rouge Area Violence Elimination Project (BRAVE), a local version of Operation Ceasefire aiming to reduce group-based violence (Barthelemy et al., 2016; Guin et al., 2017). While these strategies were able to inhibit violent crime, including homicide, during implementation of these programs and immediately following their cessation, however, long-term reductions in violence have not taken hold with violence remaining a fixture of the city as Baton Rouge's homicide rate exceeded Chicago's in 2017 (Tooey et al., 2018). As such, Baton Rouge's unenviable violence-ridden context makes it an ideal research site to examine the relationship between the environmental landscape and lethal violence. Given that the city limits of Baton Rouge are generally quite porous, besides the Mississippi River being an unyielding barrier on the city's western jurisdictional boundary, the current study includes all zip codes that are contained within and intersect with the city limits. 2.2. Data Data for the current study come from three sources. All known homicides that occurred in East Baton Rouge Parish from 2016 to 2017 were provided by the East Baton Rouge District Attorney's Office. Each homicide was geocoded and made public record the following January. In 2017, 125 homicides transpired in East Baton Rouge Parish, of which 111 occurred within the study area and are geocoded. All homicide incidents were geocoded with a 20 ft offset to the street centerline file. Second, XY-coordinate data identifying bus stops, parks, public high schools, and blighted properties are obtained from the East Baton Rouge Parish GIS Open Data Map Portal (Priola and Kron, 2018). Lastly, the XY-coordinate data for all other spatial risk factors (e.g., banks/credit unions, service stations, etc.) are acquired from Infogroup1 (http://www.infogroup.com), a provider of commercial and residential information (see Barnum et al., 2017; Caplan et al., 2017; Piza et al., 2016, 2018). 2.3. Spatial risk factors Homicides in Baton Rouge are the objects of the RTM forecast. Eighteen environmental risk factors are identified to be included in the RTM: banks/credit unions, bars/cocktail lounges, blighted properties, bus stops, convenience stores2, food markets, fringe banks3, 1

InfoGroup is a data and marketing services provider. Twelve of the convenience stores are co-located with a service station. Removing these twelve businesses did not impact model fit. 3 Fringe banks are considered to be self-managed lenders such as check cashers, payday lenders and pawnshops (see for greater detail Kubrin and Hipp, 2016). 2

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laundromats, liquor stores, parks, public high schools, public housing, restaurants, recreational centers, service stations, and tobacco shops. These spatial risk factors are based upon the existing literature and guided by personal knowledge of the study area (Bernasco and Block, 2011; Brantingham and Brantingham, 1995; Costanza et al., 2001; Drawve et al., 2016; Gerell, 2018; Groff, 2011; Levine et al., 1986; McCord et al., 2007; Parker, 1995; Pridemore and Grubesic, 2012; Roncek and Maier, 1991; Spelman, 1993). While the dependent variable for the current study is 2017 homicides, an outcome event is necessary to generate a RTM. This study utilized the location of the prior year (2016)’s homicides as an outcome event. This outcome event is used to determine the spatial operationalization and spatial influence of the included environmental risk factors on homicide to ascertain the appropriate level of risk. In 2016, 79 homicide took place in East Baton Rouge Parish, 71 of which occurred within the study area, and all of which are geocoded. All 2016 homicides were also geocoded with a 20 ft offset to the street centerline file. 2.4. Analytic strategy RTM is an analytical approach to forecast where crime, or some other type of incident (e.g., child maltreatment), will transpire based upon the spatial location of environmental risk factors. Developed by the Rutgers Center on Public Security, the RTMDx Utility is used to conduct the current study's RTM analysis (Caplan et al., 2013). RTM uses a set of statistical tests to systematically determine the suitable operationalization of each risk factor included, ascertains the appropriate type of model (negative binomial or Poisson), and identifies the environmental risk factors that significantly influence outcomes (i.e., homicides) in a particular areal unit (Caplan and Kennedy, 2016; Heffner, 2013). The risk factor's spatial influence is geographically limited to only a few blocks, with the current study using the average block length as the practical areal unit of analysis (Caplan and Kennedy, 2016; Kennedy et al., 2011). Given the average block length of the Baton Rouge street system measures at approximately 500 feet (as measured by ArcGIS 10.6), the current study creates a grid 500 ft × 500 ft cells (N = 98,702). The RTMDx Utility employs a penalized regression model with crime counts, for this study homicides, as the dependent variable. The independent variables are the specified operationalizations of environmental risk factors which the RTMDx Utility measures to determine if each of the 98,702 cells are in a highly concentrated area of a risk factor (i.e., density) or within a specified distance of a risk factor (i.e., proximity). The current study uses an “aggravating” model since it is ascertaining which spatial risk factors are positively associated with a homicide (Caplan et al., 2013). The spatial influence parameter is set at the maximum of 4 blocks with all risk factors being tested at half-block increments. For example, ½ block (250 ft), 1 block (500 ft), 1½ blocks (750 ft), 2 blocks (1000 ft), etc. As recommended by Caplan and Kennedy (2016), nearest neighbor analyses are conducted for all risk factors to determine their spatial operationalization. Table 1 displays the results of these nearest neighbor analyses and the operationalization parameter indicated by the test. Risk factors that significantly cluster and whose observed mean distance is less than or equal to the nearest neighbor threshold are operationalized as both proximity and density, as Caplan and Kennedy (2016) suggest.4 Risk factors that fail to significantly cluster or the observed mean distances are greater than the nearest neighbor threshold are operationalized as proximity. Guided by this analysis strategy, the 18 environmental risk factors produced 200 variables to empirically test for significance (see Table 1). The RTMDx Utility determines the most appropriate model based upon the operationalization decisions and the risk factors included in the analysis (for greater detail on the statistics guiding the RTMDx process see Heffner, 2013). 3. Results The optimal model identified by the RTMDx Utility is a negative binomial regression model for the RTM analysis of homicides in 2016. The RTMDx Utility evaluates the proposed 18 environmental risk factors and determines that there are two significant risk factors for homicide in Baton Rouge: one risk factor is operationalized as density and one operationalized as proximity. In order of diminishing importance, the two risk factors are blighted properties and convenience stores. These two risk factors, their operationalization, the greatest extent of their spatial influence, and their relative risk values (RRVs)5 is displayed in Table 2. For example, the risk of a homicide taking place is significantly higher within close proximity, 1750 ft, of a convenience store. That is, being within a three in a half block radius of a convenience store increases the risk of an individual being a victim of a homicide more than fivefold (5.467). The most influential environmental risk factor associated with homicide is the concentration of blighted properties. The RRV indicates that being within 1250 ft (approximately two in a half blocks) increases the likelihood of being victimized in a homicide by nearly 13 times (12.613) compared to the vast majority of cells that have a risk value of 1. As such, while a substantial number of places in Baton Rouge present some risk of being victimized in a homicide, the spatial influence of these two particular environmental features, the concentration of blighted properties and proximity to a convenience store, significantly increases the risk of some spaces. The final risk map with homicides in 2017 is displayed in Fig. 2. Each of the 98,702 cells have a RRS calculated from a negative binomial model by the RTMDx Utility. The map indicates that just 2504 cells are considered to be very high risk. That is, the probability of a homicide taking place in a very high risk cell is at least 25 times more likely to occur than in a cell with no risk factors present, but can reach as high as 69 times greater. The map, overall, classifies approximately 3 percent of Baton Rouge as being at an extreme risk of experiencing a homicide in the future. Fig. 2 also identifies several clusters of very high risk areas in the center and 4 As suggested by Caplan and Kennedy (2016) the nearest neighbor threshold is calculated as 2 × (Block Length x Number of Analysis Increments). For this study the nearest neighborhood threshold is 3000 (2 × (500 × 3)). 5 A risk factor's exponentiated model coefficient is denoted by the relative risk value (RRV).

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Table 1 Nearest neighbor analysis results and corresponding risk factor operationalization. Risk Factor

n

Observed distance (Ft.)

P-value

Spatial Pattern

Operationalization

Banks/credit unions Bars/night clubs Blighted properties Bus stops Convenience stores Discount stores Food markets Fringe banks Laundromats Liquor stores Parks Public high schools Public housing Recreational centers Restaurants Service stations Thrift shops Tobacco shops Total

90 75 8037 1639 128 21 43 127 19 14 133 20 12 28 65 78 17 17

2358.97 2990.34 141.08 258.69 2554.66 7453.22 4280.69 1288.79 6212.90 9520.22 3151.50 7732.26 6563.57 6322.02 3053.49 2892.46 6242.02 7789.83

0.00 0.00 0.00 0.00 0.00 0.04 0.48 0.00 0.46 0.00 0.00 0.20 0.02 0.59 0.05 0.00 0.10 0.22

Clustered Clustered Clustered Clustered Clustered Dispersed Random Clustered Random Dispersed Dispersed Random Dispersed Random Dispersed Clustered Random Random

Both proximity Both proximity Both proximity Both proximity Both proximity Proximity Proximity Both proximity Proximity Proximity Proximity Proximity Proximity Proximity Proximity Both proximity Proximity Proximity

and and and and and

density density density density density

and density

and density

Spatial Influence (maximum)

Increments

Total

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

Half Half Half Half Half Half Half Half Half Half Half Half Half Half Half Half Half Half

16 16 16 16 16 8 8 16 8 8 8 8 8 8 8 16 8 8 200

Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks Blocks

northern parts of the city. Particularly in the communities of Brookstown, Fairfields, Istrouma, Old South Baton Rouge, and Scottlandville where violence has a protracted history, see Fig. 3. In fact, many of these high risk clusters are within the prior treatment areas of OJJDP sponsored initiatives to combat violence (Barthelemy et al., 2016; Guin et al., 2017; Sheppard et al., 2000). In Table 3 a comparison is made to evaluate the reliability and predictive power of the RTMDx Utility's risk forecast and homicides that occurred in 2017. About 23 percent of homicides committed in 2017 are taking place in very high risk cells. Thus, approximately one-fourth of all 2017 homicides in Baton Rouge are transpiring in under 3 percent of the physical landscape, which was predicted to have very high risk of experiencing a homicide in 2017. This finding conforms with Theory of Risky Places second proposition, that the combined spatial influence of multiple criminogenic risk factors (i.e., convenience stores and blighted properties) increases the likelihood that crime (i.e., homicide) will emerge in these more spatially vulnerable areas (Caplan and Kennedy, 2016; Kennedy et al., 2016). This finding highlights the impressive predictive validity of RTM's analytical techniques and the predictive accuracy of the environmental risk factors being used in the current analysis to forecast a substantial portion of lethal violence within such a small area. A Poisson regression is used to highlight the changes in the likelihood of future homicides between the different risk levels (see Table 4). The dependent variable in the model is the number of homicides in 2017 (N = 111). Two dichotomous variables, very high risk and medium risk, indicating the risk level for each cell are the independent variables. Low risk is the reference category and is omitted from the model.6 The findings reveal that the limited number of medium and very high risk cells (approximately 19 percent of Baton Rouge's physical landscape) are significantly more likely to experience a homicide compared to a cell that is designated as being low risk. That probability for a very high risk cell is nearly 23 times higher than compared to a low risk cell. Furthermore, the probability for even medium risk cells have over a 7 times greater likelihood of experiencing a homicide. The results clearly identify that the combination of specific environmental risk factors, the concentration of blighted properties and close proximity to a convenience store, make particular areas of Baton Rouge vulnerable to experiencing a future homicide. RTM highlights that while most of Baton Rouge is at low risk of experiencing a homicide, several very high risk clusters are present (e.g., see Fig. 3). Even though only one other RTM study to date has explicitly examined the influence of environmental risk factors on total homicides, the patterns observed in the current study are consistent to homicide patterns observed in Bogotá, Colombia by GiménezSantana et al. (2018) where the top 5 percent of cells with the highest levels of risk experienced 18 percent of all homicides transpiring in the city. Similar patterns have also been observed in research utilizing RTM to examine the influence of environmental risk factors on shooting (Caplan, 2011; Caplan and Kennedy, 2016; Caplan et al., 2011). Even though the physical landscape for Baton Rouge is substantially different from Bogotá, yet, with a turn of the crime risk kaleidoscope (see Barnum et al., 2017) a unique combination of environmental risk factors and situational contexts are assembled and able to successfully forecast future acts of lethal violence. The current study's findings also support all three propositions presented in the Theory of Risky Places (Caplan and Kennedy, 2016; Kennedy et al., 2016). Lastly, the predictive capacity of RTM to forecast future homicide incidents is confirmed, supporting Caplan and Kennedy (2016: p. 27) assertion that “very accurate place-based forecasts can be made when the attractors of criminal behavior are diagnosed with RTM.” 6 The risk map does not categorize any cells as being high risk, between 1 and 2 standard deviations above the mean. As such, this category is withheld from the analysis.

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Table 2 Best fitting risk terrain model for homicide. Risk Factor

Operationalization

Spatial Influence (Ft.)

Coefficient

Relative risk value

Blighted properties Convenience stores Intercept

Density Proximity –

1250 1750 –

2.535 1.699 −8.600

12.613 5.467 –

Note. All variables included in the model demonstrate a p-value of ≤ .001. Model: Negative binomial type II; BIC: 974.79.

4. Discussion Bruinsma and Johnson (2018) highlight that interest in the geography of crime has been substantially increasing in the field of criminology and RTM is just one approach that builds upon criminologist's appeal in how spatial patterns of crime are impacted by the physical environment. The importance of how an individual's activity patterns interact with the context of the criminal event has been stressed over the years by Brantingham and Brantingham (1995). That is, crime generators and crime attractors are directly influenced by characteristics of the physical environment (Brantingham and Brantingham, 1995). Furthermore, Drawve (2016) highlights that compared to retroactive techniques (e.g., hot-spot analysis), RTM reliably predicts crime across micro-units. In regards to crime forecasting an ongoing debate remains about whether the benefits of predictive analytics outweigh the potential pitfalls, particularly the concern of racially biased policing (see Brantingham et al., 2018; Brayne, 2017; Ferguson, 2017; Jefferson, 2017). Such a concern is most evident when predictive policing techniques are used to identify potential offenders or victims prior to the commission (Berk, 2009; Degeling and Berendt, 2017), a process reminiscent of Philip K. Dick's The Minority Report but without the precognizant psychics. In contrast, predictive policing that uses place-based methods, such as RTM, which forecast the location and time of where a future crime is most likely to transpire, presents different concerns. In particular, there is concern that place-based policing will either facilitate the use of racially biased policing practices in specific areas or amplify the biases of law enforcement officers when patrolling spatially vulnerable areas (Brantingham, 2018; Ferguson, 2012). That being said, Brantingham (2018) evaluated the place-based methods employed in the Los Angeles predictive policing experiment (see Mohler et al., 2015) finding that the forecasting of risky places at the micro-level does not generate racially biased arrests. That is, the “arrest rates for black and Latino individuals were not impacted, positively or negatively, by using predictive policing” (Brantingham et al., 2018: p. 5). These findings are encouraging given that RTM is different from other forecasting techniques in that it does not rely on criminal event histories to predict where future crimes are going to transpire but instead only requires environmental risk factors to be included in the model (Caplan and Kennedy, 2016; Drawve, 2016). Thus, RTM is able to avoid infringing upon the constitutional protections of citizens by not relying upon prior criminal events, which could be influenced by police officers’ beliefs or bias, to empirically identify areas that are both high risk and spatially vulnerable to endure a future criminal event (Ferguson, 2012; Koss, 2015). In addition to discerning which places are more vulnerable to crime, RTM is also able to determine why these particular places are at greater risk of experiencing crime, unlike other place-based approaches to forecast crime (Caplan and Kennedy, 2016). As Lum (2009) points out crime management strategies that target places are more effective than policing strategies that focus on particular individuals. It is due to all of these described qualities that RTM has been professed as “a major step forward in predictive policing” (Brantingham, 2011: p. 202). The current study uses RTM's systematic approach to discern not only where a homicide is more likely to transpire but also understanding what makes an area more spatially vulnerable to experiencing lethal violence in Louisiana's capital city of Baton Rouge. Overall, there remains only handful of studies that used RTM to examine the environmental risk factors associated with violent crime (Drawve et al., 2016; Piza and Gilchrist, 2018) or homicide (Dugato et al., 2017; Giménez-Santana et al., 2018; Valasik, 2018) more specifically. Additionally, this study is the first to incorporate a unique environmental risk factor, blighted properties, in ascertaining the spatial correlates associated with homicide. The RTM in the current study identifies which of the eighteen environmental risk factors collate in areas of high risk to impact the prevalence of lethal violence. The two statistically significant factors include (in order of decreasing importance) the concentration of blighted properties and the proximity to a convenience store. Prior research has affirmed that the presence of distressed properties in large numbers encourage criminal activity (Boessen and Chamberlain, 2017; Skogan, 1990; Wilson and Kelling, 1982). Additionally, blighted properties impose an external cost by the lowering market values of nearby properties and increasing insurance premiums, reducing a neighborhood's equity and wealth. Over time, municipalities begin to feel economic pressure, suffering reduced tax revenues or being required to raise taxes. These strains make them less competitive to surrounding cities and reduce their financial resources (Accordino and Johnson, 2000). Furthermore, as distressed properties and crime proliferate, an outward flow of residents occurs, usually to surrounding communities signaling a municipality's economic struggles and undesirability, resulting in the overall decline of a city's socio-economic status (Hipp and Kane, 2017). Just like other major urban centers (e.g., Chicago, New Orleans, etc.) with communities that have been economically depressed for decades the cumulative results of crime and violence can produce an environment that is not conducive to economic development (Greenbaum and Tita, 2004). Such a situation is not uncommon in metropolitan areas in Louisiana (see Ulmer et al., 2012), easily producing a “food desert”, where supermarkets are absent, with local residents solely relying upon smaller convenience stores for 194

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Fig. 2. Final risk map and 2017 homicides in Baton Rouge, LA.

groceries, home goods, tobacco products and alcoholic beverages. As such, convenience stores become the central hubs of the community, however, these locations can serve as forums for conflict between residents and even with law enforcement (Fausset et al., 2016; Hitchens, 2017). Furthermore, prior research suggests that the built environment surrounding a convenience store could influence patterns of violent crime. For instance, D'Alessio and Stolzenberg (1990: 265) find that convenience stores with smaller parking lots and that lack self-service gasoline pumps are more likely to experience a robbery and suggest that these environmental 195

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Fig. 3. Very high risk communities and 2017 homicides in Baton Rouge communities.

features facilitate less informal surveillance, which would discourage criminal activity as “the prospects for identification and apprehension” increase with greater visibility. Additionally, store design, corporate management practices, hours of operation and security operations could further impact crime generation or attraction to an area (White and Katz, 2013). RTM in the current study is able to identify that most areas in Baton Rouge have a low risk of experiencing a homicide, however, several very high risk clusters are present, particularly in of Brookstown, Fairfields, Istrouma, Old South Baton Rouge, and 196

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Table 3 Number of cells by relative risk score and homicides occurring in 2017. Relative risk score

Cells

%

% Cumulative

Homicides

%

% Cumulative

Very high risk High risk Medium risk Low risk Total

2504 0 15884 80314 98702

2.54 0.00 16.09 81.37 100.00

2.54 2.54 18.63 100.00

25 0 51 35 111

22.52 0.00 45.95 31.53 100.00

22.52 22.52 68.47 100.00

Table 4 Poisson regression models assessing the predictive power of the final risk map on 2017 homicides (N = 98,702). Relative risk score

Coefficient

Robust SE

Z

P > |z|

Incidence rate ratio

Very high risk High risk Medium risk Constant

3.132*** – 1.997*** −7.739***

0.282 – 0.234 0.183

11.11 – 8.55 −42.31

0.000 – 0.000 0.000

22.910 – 7.368 0.000

Note. Dependent variable: 2017 homicides. Reference category: Low risk. Wald χ2(11) = 134.80; log pseudo-likelihood = −869.291; Bayesian information criterion (BIC) = 1623.258; prob > χ2 = 0.000; pseudo R2 = 0.086. *p ≤ .05. **p ≤ .01. ***p ≤ .001.

Scottlandville neighborhoods (See Fig. 3). The results of the current study could be utilized by policymakers, practitioners and law enforcement to develop a risk narrative, an account of how crime relates to the environmental risk factors that make a place vulnerable to future criminal acts, to provide greater context surrounding homicide events (Caplan and Kennedy, 2016). Furthermore, developing a risk narrative assists in communicating a crime problem, in this case homicide, to policymakers, practitioners and law enforcement in a manner that supports the testing of preconceived notions about the relationship between homicide and environmental landscape. Constructing a risk narrative that is informed by RTM provides a blueprint empowering local stakeholders to appropriately manage the risk in areas, particularly those places most vulnerable to experiencing chronic violence. Many of the communities identified by RTM as having clusters of very high areas have an extended history of experiencing lethal violence in spite of multiple interventions aimed at reducing homicides (Barthelemy et al., 2016; Guin et al., 2017; Sheppard et al., 2000). Incorporating the history of these communities into the risk narrative will further enhance local stakeholders understanding of how homicide is associated with places that have high concentrations of blighted properties and are in close proximity to convenience stores. Ascertaining what connections are present between homicide and the spatial features in a high risk area is a much needed step to developing a plan that can successfully reduce homicides in vulnerable places. After assessing which high risk areas are the most acute, policymakers, practitioners and law enforcement can develop interventions that are targeted to affect the opportunities for crime and violence is through altering the built environment, generally referred to as crime prevention through environmental design (CPTED) (Barton & Gruner, 2016; Jeffery, 1971; MacDonald, 2015; Newman, 1972; Taylor and Gottfredson, 1986). For instance, convenience stores that are in close proximity to concentrated areas of blight could make sure to provide adequate lighting in parking lots, reduce clutter in storefront windows, limit access to alcohol, and increase the use of CCTVs. These are all straightforward measures that would likely affect crime patterns (Piza et al., 2016; White and Katz, 2013). From a law enforcement standpoint, implementing proactive enforcement and increased police visibility around these stores would also likely be effective crime reduction measures (White and Katz, 2013). In regards to blight, prior studies have found that “criminal behavior in urban environments is sensitive to, and is optimized on, housing quality signals” (Price, 2016: p. 217). Thus, if crime is to be reduced around these areas with high concentrations of distressed and vacant properties it is necessary to secure and/or eliminate these abandoned properties (Branas et al., 2016; Eck and Spelman, 1987; Price, 2016; Spelman, 1993). For example, the New Briarfield Apartments in Newport News, Virginia was a federally subsidized low-income apartment complex that had high crime rates where about 25 percent of the occupied households were burglarized and robberies and disorderly youth were routine problems (Eck and Spelman, 1987). Yet, after the management company boarded up and secured the vacant units, the burglary rate decreased 35 percent. Furthermore, these results were found to not only be immediate, but were also found to be permanent. Adjacent areas did not experience an increase in crime, suggesting that crime was not displaced into the surrounding neighborhoods (Eck and Spelman, 1987). Such an approach would seem to be viable to address the patterns of lethal violence associated with the concentrations of blighted properties in Baton Rouge. Implementation of even a minimal blight remediation program, including repairs of doors, windows and facades as well as general exterior cleanup and maintenance is likely to see substantial reductions not only in firearm related violence around the remediated properties but also overall crime rates along with reduced perceptions of crime by local residents (Branas et al., 2016, 2018; Kondo et al., 2015, 2018). Remediation even has support from an economic standpoint: a cost-benefit analysis reveals that the remediated properties returned conservative estimates of $5.00 to $26.00 in net benefits to taxpayers within the first year, and between $79.00 to $333.00 in net benefits to society at large for every dollar invested (Branas et al., 2016). Overall, by removing the signs of physical distress and improving housing stock quality through minimal property remediation can significantly reduce the likelihood of violent crime 197

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incidents, inhibit crime displacement and provide fiscal relief to the treated area. Intelligently reinvesting financial returns into distressed areas of Baton Rouge, a properly maintained blight elimination program could prove to be self-sustaining. While this research contributes to both the research on the relationship between environmental risk factors, particularly blight, and crime and the use of RTM more generally, the study is not without its limitations. The current study's focus is centered around the city of Baton Rouge, a medium-sized city in the Southern United States. As such, the generalizability of the results may be restricted to jurisdictions more analogous to Baton Rouge. In regards to the RTM analysis, the incorporation and inclusion of environmental risk factors to produce an exhaustive list is guided by the extant literature and personal knowledge of Baton Rouge. It is possible, however, that important spatial risk factors are overlooked and not included in the analyses and the models suffer from omitted variable bias. The findings presented in the current continue to build on the growing literature that employ RTM to investigate crime, particularly lethal violence (see Drawve et al., 2016; Dugato et al., 2017; Giménez-Santana et al., 2018; Valasik, 2018). The current study identifies a limited number of environmental risk factors that compound with other risk factors to produce clusters at very high risk of experiencing a homicide in the near future. Guided by a constructed risk narrative, law enforcement could utilize their limited resources more efficiently by focusing interventions in neighborhood that are the spatially at high risk and most vulnerable to experiencing future acts of violent victimization. As Barnum et al. (2017) highlight how each jurisdiction has a unique setting, turning the crime risk kaleidoscope to produce a different combination of environmental risk factors and situational contexts that are predictive of criminal events, it is important for future studies to continue investigating the influence of blight properties and other similar environmental risk factors on violent crime in other cities and communities. Since blighted properties and the location of high-crime convenience stores are routinely in economically depressed areas (Block and Block, 1995) it would be advantageous to utilize Drawve et al’. (2016; Thomas and Drawve, 2018) aggregated neighborhood risk of crime (ANROC) measure to capture the impact of the built environment's influence on violence and crime along with traditional social structural measures of a neighborhood (e.g., percent unemployed, etc.). Expanding the use of RTM across a variety of jurisdictions of varying sizes not only refines the list of potential environmental predictors to be considered as predictors of future criminal incidents but help establish a continuum of possible crime prevention strategies that focus on the physical environment of places most at risk in an area. Lastly, the current study highlights how the collaboration between academics and practitioners are able to better understand a criminogenic problem through empirically supported research (see also Drawve et al., 2018). As more and more police agencies endeavor to utilize predictive analytics as a means of maximizing the allocation of limited resources, the current study provides an example of how using cutting-edge geo-spatial analytical techniques can be applied to forecast violent crime with the goal of enhancing public safety. 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