Race and Social Problems

, Volume 5, Issue 1, pp 57–64 | Cite as

Which Factor has More Impact? An Examination of the Effects of Income Level, Perceived Neighborhood Disorder, and Crime on Community Care and Vigilance Among Low-Income African American Residents

Article

Abstract

This study investigated predictors of community care and vigilance among 70 African American residents living in high-crime, low-income neighborhoods. A stratified random sampling procedure was employed to select residents who completed a 20-item questionnaire assessing their sense of community care and vigilance and perceptions of perceived neighborhood physical and social disorder. We used police crime reports to assess the levels of property and violent offenses in the targeted neighborhoods. Our goal was to determine which of these variables best predicted community care and vigilance. The results of this study showed that social disorder and violent offenses negatively predicted community care and vigilance. Interestingly, the results also indicated that residents who reported the lowest income expressed the highest levels of community care and vigilance. Implications for community practice are discussed.

Keywords

Income level Neighborhood violence African American Disorder Crime 

Introduction

African Americans are disproportionately affected by neighborhood crime and violence (Bellair and Kowalski 2011), which is a significant public health concern. Such violence leads to resident disengagement and often can erode the overall perceptions of neighborhood safety and sense of community (Taylor 2002). Understanding the dynamics that affect African American residents’ perceptions of neighborhood collective efficacy could be one key to helping them work together to deter crime. To date, much of the research has focused on examining which variables best predict neighborhood fear and crime, and on how these variables lead to maintenance or increases in neighborhood crime (Cobbina et al. 2008; Jarrett and Jefferson 2003; Parker and Onyekwuluje 1991, 1992; Pitner et al. 2011; Reed et al. 2009). Such a focus is important because understanding these dynamics could help residents work toward building and promoting a sense of community and neighborhood care. In fact, research suggests that a strong sense of community and neighborhood care protects residents from fear and crime (Sampson 2004). However, research has seldom examined the obverse—namely how levels of neighborhood crime and disorder predict community and neighborhood care. The purpose of this study was to examine which variables best predict neighborhood care and vigilance.

Collective efficacy is an agency-oriented perspective focused on residents taking active roles in shaping their neighborhood communities (Sampson and Raudenbush 1999). This approach, by default, requires a high level of community cohesion. Such cohesion militates against residents’ fear of neighborhood crime (Sampson 2004; Sampson and Raudenbush 1999). Voluminous studies have used the collective efficacy framework and suggest an inverse relationship between perceptions of neighborhood safety and levels of community cohesion (Ferguson and Mindel 2007; Franzini et al. 2005; Sampson 2004; Sampson and Graif 2009; Sampson et al. 1999; Sampson and Raudenbush 1999; Wells et al. 2006). Place attachment focuses on the bonds between residents and their social and physical settings (Brown et al. 2004). In many ways, this concept is similar to collective efficacy in that it emphasizes the importance of neighborhood cohesion on residents’ perceptions of safety. Yet, it also places emphasis on residents’ feelings of pride in their home and neighborhood appearance, and residents’ tenure in their neighborhoods (Brown and Werner 1985; Brown et al. 1992). Brown et al. (2004) suggest that increased place attachments can directly increase collective efficacy.

The current study used the concept of community care and vigilance, which we conceptualized as a variation of collective efficacy and place attachment. We defined community care and vigilance as residents’ pride in their neighborhoods and sense of community as well as their willingness to take action to protect their neighborhoods. In previous research, we showed that broken window theory, collective efficacy and place attachments all play a role in predicting neighborhood safety (Pitner et al. 2012). We also suggested that community practitioners and public health professionals should create neighborhood interventions that will increase collective efficacy and place attachment by increasing residents’ perceptions of neighborhood safety. Nevertheless, there is a paucity of research that comprehensively examines the impact of neighborhood crime, and perceptions of neighborhood disorder, on sense of community care and vigilance among low-income African American residents.

As mentioned, previous studies have focused mainly on investigating how crime and perceptions of disorder predict neighborhood safety, and this is particularly true for African American neighborhoods. For example, Parker and Onyekwuluje (1991) showed that neighborhood crime was the strongest predictor of neighborhood safety among African American residents. In addition, Griffin et al. (2007) reported that higher perceived neighborhood decay was negatively associated with African American residents’ perceptions of neighborhood safety. This is an important point because identifying what role crime and perceived neighborhood disorder play in affecting residents’ sense of community and neighborhood pride could be the first step in developing sustainable neighborhood safety interventions. Yet, these studies did not directly examine how crime, or perceived neighborhood disorder, affects residents’ sense of community care and vigilance.

In this study, we examined three possible predictors of community care and vigilance: perceived neighborhood disorder, neighborhood crime (i.e., violent offenses and property offenses), and income level. We used the term disorder to describe residents’ perceptions of the physical and social conditions of their neighborhoods. Specifically, physical and social cues serve as implicit markers for unsafe and violence-prone neighborhoods (Astor et al. 2001; Eck and Weisburd 1995; Perkins et al. 1990, 1992; Pitner and Astor 2008; Pitner et al. 2011, 2012; Taylor 1994, 1997; White 1990). These areas are characterized by physical disorder (e.g., vandalism, graffiti, and debris in yards) and social disorder (e.g., noisy neighbors, prostitution, drug trafficking, and gang-related activity) (Perkins et al. 1993). Research suggests that increased neighborhood disorder invokes perceptions of crime among residents and potential offenders (Perkins et al. 1992; Taylor 1999; Taylor and Gottfredson 1986), which may lead to lower perceptions community care and vigilance.

High levels of neighborhood crime can affect one’s perceptions of the well-being and social landscape of the neighborhood (Elo et al. 2009; Sampson and Raudenbush 1999). It may also make residents feel unsafe in their neighborhoods, which may lower perceptions of community care and vigilance. While property offenses (e.g., burglary, larceny-theft, arson, vandalism, motor vehicle theft) are the more common type of offense, violent offenses (e.g., murder, battery, and assault) are the more serious type. Would one of these types of crime play a greater role than the other in decreasing residents’ perceptions of community care and vigilance? Or, would they both be likely to decrease residents’ perceptions of community care and vigilance? The current study examined this issue.

Human capital is defined as the educational level, ethnic identity, and social ties within a neighborhood (Putnam 2000). Ferguson and Dickens (1999) suggested that human capital is typically based on an individual’s socioeconomic status (SES). In the current study, we look specifically at one important aspect of SES, namely income level. Income level is a concept that is interrelated with Putnam’s notion of bonding and bridging capital. Bonding capital refers to characteristics of homogenous social networks, whereas bridging capital refers to characteristics of heterogeneous networks (Lewin et al. 2010). More specifically, bonding capital is seen when residents are closely connected to other residents within their neighborhood, with less connections to outside networks. Bridging capital is characterized by residents being more strongly connected to outside networks and less connected to networks within their own neighborhood. Because African American residents have been disproportionately situated in the lower income stratum, research tends to indicate higher bonding capital for African American residents, and higher bridging capital for white residents (Liu et al. 2009). However, in predominately African American neighborhoods, one would expect variation in the prevalence of bonding and bridging capital among African American residents, which may be due, in part, to variations in income level. There is a dearth of research that examines the association between income level and African American residents’ perceptions of community care and vigilance.

Hypotheses

The purpose of this study was to look more closely at the predictors of neighborhood community care and vigilance by focusing specifically on residents who live in low-income, high-crime neighborhoods. As mentioned, we conceptualized community care and vigilance as residents’ pride in their neighborhoods and sense of community as well as their willingness to take action to protect their neighborhoods. Following directly from the supporting research findings mentioned above, three hypotheses were tested in this study. First, we hypothesized that both higher perceived physical and social disorder would predict lower levels of neighborhood community care and vigilance. Second, we hypothesized that higher levels of crime (i.e., police crime reports) would predict community care and vigilance, and that this would vary by type of crime (i.e., property offenses versus violent offenses). Finally, we hypothesized that annual income would predict community care and vigilance, which would be evidenced by variations income level. It is important to mention that the first hypothesis is directional because previous research suggests a negative relationship between perceived disorder and collective efficacy (e.g., Reisig and Cancino 2004), and between perceived disorder and place attachment (e.g., Brown, Perkins, and Brown). In this study, we use the term community care and vigilance as a variation of collective efficacy and place attachment. Thus, we predict a negative relationship between perceived disorder and community care and vigilance. Previous research, however, does not provide clarity about the issues raised in the second and third hypotheses. Consequently, these hypotheses are left non-directional.

Methods

Sample and Participant Selection Procedures

This study received university-level approval from the Institutional Review Board. The study was conducted in an urban city within a large Midwestern, metropolitan area. We surveyed 70 African American residents, among whom the majority were females (83 %). On average, participants lived in their current residence for 15.5 years (SD = 14.1), with a range of 3 months to 51 years. The average age was 52.8 (SD = 18.1), ranging from 20 to 88. Annual household income ranged from 1 (less than $10,000) to 7 ($75,000 or above), with the mean lying between $10,001 and $20,000. Specifically, 81 % of the respondents earned less than $20,000. Detailed characteristics of the study sample are provided in Table 1.
Table 1

Descriptive information

 

Range

Overall sample (N = 70)

Male (n = 12)

Female (n = 58)

Mean

SD

%

Mean

SD

%

Mean

SD

%

Dependent variable

          

 Community care and vigilance

6–30

21.4

6.0

 

22.8

7.1

 

21.1

5.8

 

Demographic variables

          

 Age

20–88

52.8

18.1

 

52.0

20.3

 

53.0

17.8

 

 Years of living in current resident

0.25–51

15.5

14.1

 

17.2

16.7

 

15.2

13.7

 

 Annual income

1–7

1.9

1.5

 

1.9

1.6

 

1.9

1.4

 

  $10,000 or less (1)

   

61.8

  

60.0

  

62.1

  $10,001– $20,000 (2)

   

19.1

  

20.0

  

19.0

  $20,001–$30,000 (3)

   

4.4

  

10.0

  

3.4

  $30,001 or more (4, 5, 6, 7)

   

14.7

  

10.0

  

15.5

Perceived neighborhood disorder variables

          

 Physical disorder

0–6

3.3

1.8

 

2.2

1.6

 

3.6*

1.8

 

 Social disorder

0–4

1.8

1.1

 

1.6

0.9

 

1.8

1.2

 

Crime variables

          

 Violent offenses

2–5

3.6

1.2

 

2.9

1.2

 

3.7*

1.2

 

 Property offenses

1–5

1.8

0.7

 

1.8

0.4

 

1.8

0.7

 

p < 0.05 between males and females

Participants were selected because they received services from a non-profit organization that focused on helping residents who live in high-crime areas feel safer in their neighborhoods. We examined a 6-month period (April 2002 to September 2002) for which the non-profit organization provided services to residents. During that period, 496 residents received services. The target city was segmented into 9 police districts. Given this, we employed a stratified random sampling procedure. Within each of these 9 districts, the sample was stratified by month of service delivery (April through September). Residents were then selected randomly by strata. A total of 24 residents were selected randomly per district, with the exception of District 4 and District 9. For these two districts, few residents received services from the non-profit organization (2 in each district). Thus, all 4 residents were selected for the sample. Overall, 172 residents were selected, and at least 50 % of the selected sample participated from each police district. Of those who participated, 70 were African American. The current study focuses on this group.

We used a mixed data collection procedure that consisted of telephone interviews and mailed surveys. Residents who did not complete the mailed survey were contacted by telephone, and they then completed the survey on the telephone. Given that we used a stratified random sampling procedure, our study participants were representative of the overall group of residents who received services during the months of April and September.

Instrument

For the current study, we used a 20-item questionnaire, measuring community care and vigilance (6 items; the dependent measure), perceived neighborhood disorder: physical disorder (6 items) and social disorder (4 items), as well as demographic information. Community care and vigilance items were as follows: people watch out for each other in my neighborhood; if I witnessed a crime in my neighborhood, I would report it; people in my neighborhood care about the area; my neighborhood feels like a community; I feel good about living in my neighborhood; and there is not a lot of crime in my neighborhood (Cronbach’s alpha = 0.79). Physical disorder items pertained to perceptions of neighborhood safety due to decayed and run-down buildings, abandoned buildings, vacant lots, debris in the streets, graffiti, and poor lighting (Cronbach’s alpha = 0.71). Finally, social disorder items pertained to perceptions of neighborhood safety due to gangs, drug traffickers, homeless people, and nuisance and problem neighbors making the neighborhood unsafe (Cronbach’s alpha = 0.46). Items for each of the summed measures were rated on a Likert scale ranging from “strongly disagree = 1” to “strongly agree = 5.” We understand that the internal consistency reliability value of social disorder in this sample is low. However, we believe that we have captured the key variables that cluster in this category, as this has been supported by numerous prior studies (Brown et al. 2004; Elo et al. 2009; Perkins et al. 1990, 1992; Pitner et al. 2011; Reisig and Cancino 2004; Taylor 1999).

We also collected police crime data for the 9 districts. The crime categories (i.e., total violent offenses and total property offenses) were coded, where lower numbers denoted lower levels of crime and higher numbers denoted higher levels.

Data Analysis

Independent t tests were performed to examine whether demographic variables such as age and length of residency, physical and social disorder, and crime variables differed between males and females. Nonparametric Mann–Whitney tests were employed to examine the differences in skewed variables between the two groups, such as annual income (skewness = 1.9) and violent offenses (skewness = 2.4). Fisher’s exact tests were employed to examine the associations between gender and disorder. Zero-order correlations were employed to assess associations between continuous study variables. Finally, a forward stepwise multiple linear regression analysis was conducted to investigate which of the independent variables (i.e., demographics, physical and/or social disorder, and crime variables) significantly predicted the dependent variable (i.e., community care and vigilance). Analyses were conducted using SAS 9.2 (SAS Institute Inc, 2010). A priori power analysis was conducted to determine a minimum sample size for this study. Using GPower 3 (Faul et al. 2007), and based on an alpha set at 0.05 (Cohen 1988), a medium effect size set at f2 = 0.20, and the number of predictors set at 4, a minimum total sample was calculated at 65 participants with a statistical power of 0.80 (Chase and Tucker 1976; Cohen 1992).

Results

Descriptive Information

Descriptive information of participants is presented in Table 1. Overall, participants reported an average score of 21.4 (SD = 6.0) for community care and vigilance, with a range of 6–30; higher numbers indicated higher levels of community care and vigilance. Perceived neighborhood disorder variables consisted of items assessing physical and social disorder. The higher an individual scored on these items, the stronger the endorsement of the item. Participants indicated the average number of physical disorder items endorsed was 3.3 (SD = 1.8), with a range of 0–6; and the average number of social disorder items endorsed was 1.8 (SD = 1.1), with a range of 0–4. In terms of crime variables, violent offenses ranged from 2 to 5; and property offenses ranged from 1 to 5, where higher numbers indicated higher rates of crime. The mode for violent offenses was both level 2 (35.7 %) and 4 (35.7 %), while the mode for property offenses was level 2 (73 %). The average rate for violent offenses was 3.6 (SD = 1.2), while the rate for property offenses was 1.8 (SD = 0.7).

Compared to male participants, female participants reported significantly higher physical disorder (t = 2.51, p = 0.014) and violent offenses (t = 2.05, p = 0.045). There were no gender differences in terms of age, the length of residency, annual income, social disorder, property offenses, and community care and vigilance.

Table 2 presents the percentage of participants who identified specific disorder items. Interestingly, almost all of the participants (99 %) identified at least one type of disorder that they perceived as making their neighborhood unsafe. Regarding specific disorder items, 91 % identified at least 1 physical disorder item, and 77 % identified at least 1 social disorder item that makes their neighborhood unsafe. Among the subtypes of physical disorder items, a greater percentage of participants reported that decayed buildings made their neighborhoods unsafe (71 %), followed by abandon buildings (67 %), vacant lots (67 %), poor lighting (56 %), debris (46 %), and graffiti (24 %). Among the subtypes of social disorder items, a greater percentage reported that drug traffickers and addicts made their neighborhoods unsafe (70 %), followed by gangs (50 %), nuisance and problem neighbors (30 %), and homeless people (23 %).
Table 2

Percentage of sample reporting disorder items

 

Overall sample (N = 70) (%)

Male (n = 12) (%)

Female (n = 58) (%)

Any disorder

98.6

100.0

98.3

Any physical disorder

91.4

85.3

93.1

 Decayed and run-down buildings

71.4

50.0

75.9

 Abandon buildings

67.1

50.0

70.7

 Poor lighting

55.7

41.7

58.6

 Vacant lots

67.1

41.7

72.4*

 Debris

45.7

16.7

51.7*

 Graffiti

24.3

16.7

25.9

Any social disorder

77.1

75.0

77.6

 Drug traffickers and addicts

69.8

74.4

65.1

 Gangs

50.0

50.0

50.0

 Nuisance and problem neighbors

30.0

25.0

31.0

 Homeless people

22.9

8.3

25.9

p < 0.05: Fisher’s exact test

Overall, female residents identified higher rates of physical and social disorder items than their male counterparts. Two significant relationships were found between gender and disorder: vacant lots (χ2 = 4.3, p < 0.039) and debris (χ2 = 4.9, p < 0.027).

Bivariate Correlations of Study Variables

As shown in Table 3, higher levels of community care and vigilance were correlated positively with age and years of living in current resident, and negatively with annual income. There were positive correlations between the two perceived neighborhood disorder variables: physical disorder and social disorder. Age was positively and highly correlated with length of residency, while it was negatively correlated with annual income. These correlations were statistically significant, with r values ranging from 0.27 to 0.59.
Table 3

Correlations of study variables (N = 70)

 

(A)

(B)

(C)

(D)

(E)

(F)

(A) Age

1.00

     

(B) Years of living in current resident

0.59***

1.00

    

(C) Annual income

−0.31*

−0.16

1.00

   

(D) Physical disorder

0.05

0.02

−0.11

1.00

  

(E) Social disorder

−0.13

−0.21

0.14

0.40***

1.00

 

(F) Community care and vigilance

0.35**

0.27*

−0.51***

−0.22

−0.42***

1.00

Only continuous variables were included for correlation analysis

p < 0.05; ** p < 0.01; *** p < 0.001

Stepwise Multiple Linear Regression Analyses for Predicting Community Care and Vigilance

As shown in Table 4, we used a forward stepwise multiple linear regression that began with an initial model and then compared the explanatory power of incrementally larger models. Results showed that there were four significant predictors of community care and vigilance. At step 1, annual income was retained and explained 26 % of the variance in community care and vigilance (R2 = 0.26). At step 2, social disorder was accepted and explained another 13 % of the variance (Partial R2 = 0.13). At step 3, the violent offense variable was added to the model and explained another 4 % of the variance (Partial R2 = 0.04). The explained variance was raised to 43 % (R2 = 0.43). Last, at step 4, age was added and explained another 3 % of the variance (Partial R2 = 0.03), as its p value was slightly greater than 0.05 (p = 0.053). Overall, in this study, annual income, social disorder, and violent offenses negatively predicted community care and vigilance, after controlling for age.
Table 4

Stepwise regression analysis for predicting community care and vigilance among African American residents (N = 68)

Predictor

β

SE

F

p value

Model statistics

Change statistics

F

p value

R square

Partial R square

Partial Rp value

Step 1

         

 Annual income

−0.45

0.44

23.3

<0.001

23.3

<0.001

0.26

0.26

<0.001

Step 2

         

 Annual income

−0.42

0.41

22.1

<0.001

20.4

<0.001

0.39

0.13

<0.001

 Social disorder

−0.33

0.53

13.2

<0.001

     

Step 3

         

 Annual income

−0.46

0.40

22.7

<0.001

15.9

<0.001

0.43

0.04

0.035

 Social disorder

−0.37

0.52

15.0

<0.001

     

 Violent offenses

−0.20

0.47

4.7

0.035

     

Step 4

         

 Annual income

−0.40

0.41

16.8

<0.001

13.6

<0.001

0.46

0.03

0.053

 Social disorder

−0.34

0.51

13.4

<0.001

     

 Violent offenses

−0.19

0.46

4.9

0.040

     

 Age

0.19

0.03

3.9

0.053

     

β Standardized estimate parameter, SE standard error

Discussion

African Americans are disproportionately affected by high neighborhood crime rates, which may affect their overall sense of neighborhood collective efficacy. The purpose of this study was to examine what variables best predict community care and vigilance among African Americans living in high-crime, low-income neighborhoods. Overall, our findings support our stated hypotheses and suggest that income level is the strongest predictor of community care and vigilance. In addition, perceived social disorder, crimes against person, and the age of the residents living in the neighborhood were significant predictors of community care and vigilance.

In this study, residents who had the lowest incomes had the highest levels of community care and vigilance. This finding is interesting because all of the participants lived in low-income neighborhoods. However, those participants with lower income levels expressed the higher levels of neighborhood care, sense of community, and willingness to take action to protect their neighborhoods. This higher level of community care and vigilance complements Putnam’s (2000) notion of bonding/bridging capital and may indicate higher levels of bonding capital among lower income residents. Higher income residents, on the other hand, displayed lower levels of community care and vigilance. A possible explanation for this could be that this group has a higher social mobility than the lower income residents. Thus, living in low-income neighborhoods may be just a “stepping stone” to moving to higher income neighborhoods. As a consequence, this group’s lower connections and attachment to their current neighbors (and neighborhoods) may indicate higher levels of bridging capital among this group. Our study also showed that older residents had higher levels community care and vigilance than younger residents, which may also indicate higher levels of bonding capital for this group. Future research should more comprehensively explore the role that income level plays in residents’ overall perceptions of community care and vigilance.

Crime, and perceptions of neighborhood disorder, can decrease residents’ sense of attachment to their neighborhood and their overall community care and vigilance. Our findings showed that higher crime rates predicted lower levels of community care and vigilance among residents; however, this varied by type of crime committed. While higher incidence of violent offenses (e.g., battery, assault) predicted lower levels of resident community care and vigilance, higher incidence of property offenses (e.g., arson, vandalism) did not. However, the incidences of neighborhood crime are only part of the equation. Perceived neighborhood disorder is another very important factor that can decrease residents’ overall sense of community care and vigilance. We hypothesized that higher perceived physical and social disorder would predict lower community care and vigilance. This hypothesis was partially supported in that higher perceived social disorder (e.g., gang-related activity) predicted a lower sense of community care and vigilance for participants in this study; however, higher perceived physical disorder (graffiti, abandoned buildings) was not a significant predictor. These findings appear to map onto the findings for the incidence of violent and property offenses. Specifically, it could be that higher perceptions of gang-related activity (a social disorder) may make residents more concerned that they may possibly become a victim of a violent offense, whereas higher perceptions of graffiti (a physical disorder) do not elicit this same concern. It is important to note that previous research suggest that physical disorder increases residents’ concerns about neighborhood safety (Pitner et al. 2012). Nevertheless, our findings suggest that higher perceived physical disorder may not necessarily decrease community care and vigilance for African American residents living in high-crime, low-income neighborhoods.

Implications

Overall, this study shows that although crime and perceived neighborhood disorder can decrease African American residents’ sense of community care and vigilance, income level is the strongest predictor of community care and vigilance. Knowing this information provides an avenue for developing community-level interventions designed to increase the level of cohesion among all residents living in high-crime, low-income areas. Such interventions may not necessarily be able to increase income levels of residents in the area. However, they can be focused more on increasing the level of bonding capital among residents, which may ultimately increase the level of community care and vigilance. The overarching implication of our findings is that community workers, community organizers, and public health professionals should focus on building African American residents’ sense of pride in their neighborhoods and mobilizing them into action to improve community conditions, which may directly decrease their concerns about neighborhood safety and increase their sense community care and vigilance. This might be accomplished by creating more volunteer opportunities for residents of all ages. For example, community partnerships can be formed through senior centers that serve as volunteer opportunities for younger residents (i.e., elementary school-aged, teenagers, young and middle-aged adults). In addition, neighborhood watch programs could include residents of all ages. Community collaborations have been shown to be beneficial to health and well-being of residents (Bolda et al. 2005; Freudenberg et al. 2011; Trickett et al. 2011). Such efforts could also foster a greater sense of community care and vigilance.

Study Limitations

Our findings add to the knowledge base on African American residents’ sense of community care and vigilance and their perceptions of neighborhood crime and safety. However, this study is not without limitations, and our findings should be interpreted within a context of these limitations. First, the cross-sectional nature of the data restricts causal interpretations. Second, perceived neighborhood disorder variables were based on self-reports. Thus, response bias could have affected some findings, such as actual presence of physical and social disorder. Conducting an environmental inventory would have helped corroborate the overall level of neighborhood physical and social disorder items. Third, our sample was small. Although the sample was representative of the residents who received services from the non-profit organization, it may not have been representative of residents living in the larger area. This limits the generalizability of our findings. Moreover, the small sample size may have contributed to the lower than expected internal consistency reliability value for social disorder. Finally, we measured community care and vigilance, which is not identical to measures of collective efficacy and place attachments/territoriality. This may have affected some of our findings. Despite these limitations, we believe that our study yields some novel information, thus adding to the literature base on this important topic about African American residents who live in high-crime, low-income neighborhoods.

Conclusion

Because African American residents are disproportionately affected by high crime and neighborhood violence, understanding the dynamics that affect their perceptions of community care and vigilance could be one key to helping them work together to deter crime and to feel safer in their neighborhoods. Our findings suggest that community workers and public health professionals should explore the avenue of building levels of cohesion among African American residents who live in high-crime, low-income neighborhoods. This, we contend, would create more sustainable levels of community care and vigilance.

References

  1. Astor, R., Meyer, H., & Pitner, R. (2001). Elementary and middle school students’ perceptions of violence-prone school sub-contexts. The Elementary School Journal, 101, 511–528.CrossRefGoogle Scholar
  2. Bellair, P., & Kowalski, B. (2011). Low-skill employment opportunity and African American-White differences in recidivism. Journal of Research in Crime and Delinquency, 48, 176–208.CrossRefGoogle Scholar
  3. Bolda, E., Lowe, J., Maddox, G., & Patnaik, B. (2005). Community partnerships for older adults: A case study. Families in Society, 86, 411–418.Google Scholar
  4. Brown, B., Perkins, D., & Brown, G. (2004). Incivilities, place attachments and crime: Block and individual effects. Journal of Environmental Psychology, 24, 359–371.CrossRefGoogle Scholar
  5. Brown, B., Perkins, D., & Douglas, D. (1992). Disruptions in place attachments. Human Behavior & Environment: Advances in theory & research, 12, 279–304.Google Scholar
  6. Brown, B., & Werner, C. (1985). Social cohesiveness, territoriality, and holiday decorations: The influence of cul-de-sacs. Environment and Behavior, 17, 539–565.CrossRefGoogle Scholar
  7. Chase, L. J., & Tucker, R. K. (1976). Statistical power: Derivation, development and data-analytic implications. Psychological Record, 26, 473–486.Google Scholar
  8. Cobbina, J., Miller, J., & Brunson, R. (2008). Gender, neighborhood danger, and risk-avoidance strategies among urban African American youths. Criminology, 46, 673–709.CrossRefGoogle Scholar
  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
  10. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159.CrossRefGoogle Scholar
  11. Eck, J., & Weisburd, D. (1995). Crime places in crime theory. In J. Eck & D. Weisburd (Eds.), Crime prevention studies: Crime and place (Vol. 4, p. 1034). Monsey, NY: Criminal Justice Press.Google Scholar
  12. Elo, I., Mykyta, L., Margolis, R., & Culhone, J. (2009). Perceptions of neighborhood disorder: The role of individual and neighborhood characteristics. Social Science Quarterly, 90, 1298–1320.CrossRefGoogle Scholar
  13. Faul, F., Erdfelder, E., Lang, A., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.CrossRefGoogle Scholar
  14. Ferguson, R., & Dickens, W. (1999). Urban problems and community development. Washington, DC: Brooking Institution Press.Google Scholar
  15. Ferguson, K., & Mindel, C. (2007). Modeling fear of crime in Dallas neighborhoods: A test of social capital theory. Crime & Delinquency, 53, 322–349.CrossRefGoogle Scholar
  16. Franzini, L., Caughy, M., Spears, W., & Esquer, M. (2005). Neighborhood economic conditions, social processes, and self-rated health in low-income neighborhoods in Texas: A multilevel latent variable model. Social Science and Medicine, 61, 1135–1150.CrossRefGoogle Scholar
  17. Freudenberg, N., Pastor, M., & Israel, B. (2011). Strengthening community capacity to participate in making decisions to reduce disproportionate environmental exposures. American Journal of Public Health, 101, 123–130.CrossRefGoogle Scholar
  18. Griffin, S., Wilson, D., Wilcox, S., Buck, J., & Ainsworth, B. (2007). Physical activity influences in a disadvantaged African American community and the communities’ proposed solutions. Health Promotion Practice, 9, 180–190.CrossRefGoogle Scholar
  19. Jarrett, R., & Jefferson, S. (2003). A good mother got to fight for her kids: Maternal management strategies in a high-risk, African American neighborhood. Journal of Children and Poverty, 9, 21–39.CrossRefGoogle Scholar
  20. Lewin, A., Mitchell, S., Rasmussen, A., Sanders-Phillips, K., & Joseph, J. (2010). Do human social capital protect young African American mothers from depression associated with ethnic discrimination and violence exposure? Journal of Black Psychology, 37, 286–310.CrossRefGoogle Scholar
  21. Liu, B., Wright, S., & Orey, B. (2009). Church attendance, social capital, and black voting participation. Social Science Quarterly, 90, 576–592.CrossRefGoogle Scholar
  22. Parker, K., & Onyekwuluje, A. (1991). African Americans perceptions of violent crime: A multivariate analysis. The Western Journal of Black Studies, 15, 138–143.Google Scholar
  23. Parker, K., & Onyekwuluje, A. (1992). The influence of demographic and economic factors on fear of crime among African Americans. The Western Journal of Black Studies, 16, 132–140.Google Scholar
  24. Perkins, D., Florin, P., Rich, R., Wandersman, A., & Chavis, D. (1990). Participation and the social and physical environment of residential blocks: Crime and community context. American Journal of Community Psychology, 18, 83–115.CrossRefGoogle Scholar
  25. Perkins, D., Meeks, J., & Taylor, R. (1992). The physical environment of street blocks and resident perceptions of crime and disorder: Implications for theory and measurement. Journal of Environmental Psychology, 12, 21–34.CrossRefGoogle Scholar
  26. Perkins, D., Wandersman, A., Rich, R., & Taylor, R. (1993). The physical environment of street crime: Defensible space, territoriality and incivilities. Journal of Environmental Psychology, 13, 29–49.CrossRefGoogle Scholar
  27. Pitner, R., & Astor, R. (2008). Children’s reasoning about poverty, physical deterioration, danger, and retribution in neighborhood contexts. Journal of Environmental Psychology, 28, 327–338.CrossRefGoogle Scholar
  28. Pitner, R., Yu, M., & Brown, E. (2011). Exploring the dynamics of middle aged and older adult residents’ perceptions of neighborhood safety. Journal of Gerontological Social Work, 54, 511–527.CrossRefGoogle Scholar
  29. Pitner, R., Yu, M., & Brown, E. (2012). Making neighborhoods safer: Examining predictors of residents’ concerns about neighborhood safety. Journal of Environmental Psychology, 32, 43–49.CrossRefGoogle Scholar
  30. Putnam, R. (2000). Bowling alone: The collapse and revival of American community. New York, NY: Simon & Schuster.Google Scholar
  31. Reed, E., Silverman, J., Welles, S., Santan, M., Missmer, S., & Raj, A. (2009). Associations between perceptions and involvement in neighborhood violence and intimate partner violence perpetrations among urban, African American men. Journal of Community Health, 34, 328–335.CrossRefGoogle Scholar
  32. Reisig, M., & Cancino, J. (2004). Incivilities in nonmetropolitan communities: The effects of structural constraints, social conditions, and crime. Journal of Criminal Justice, 32, 15–29.CrossRefGoogle Scholar
  33. Sampson, R. (2004). Neighborhood and community: Collective efficacy and community safety. New Economy, 11, 106–113.CrossRefGoogle Scholar
  34. Sampson, R., & Graif, C. (2009). Neighborhood social capital as differential social organization: Resident and leadership dimensions. American Behavioral Scientist, 52, 1579–1605.Google Scholar
  35. Sampson, R., Morenoff, J., & Earls, F. (1999). Beyond social capital: Spatial dynamics of collective efficacy for children. American Sociological Review, 64, 633–660.CrossRefGoogle Scholar
  36. Sampson, R., & Raudenbush, S. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105, 603–651.CrossRefGoogle Scholar
  37. Taylor, R. (1994). Research methods in criminal justice. New York: McGraw-Hill.Google Scholar
  38. Taylor, R. (1997). Social order and disorder of street blocks and neighborhoods: Ecology, microecology and the systemic model of social disorganization. Journal of Research in Crime and Delinquency, 33, 113–155.CrossRefGoogle Scholar
  39. Taylor, R. (1999). The incivilities thesis: Theory, measurement and policy. In R. Langworthy (Ed.), Measuring what matters (pp. 65–88). Washington, DC: National Institute of Justice, Office of Community Oriented Policing Services.Google Scholar
  40. Taylor, R. (2002). Crime prevention through environmental design (CPTED): Yes, no, maybe, unknowable, and all of the above. In R. Bechtel & A. Churchman (Eds.), Handbook of environmental psychology (pp. 413–426). New York: Wiley.Google Scholar
  41. Taylor, R., & Gottfredson, S. (1986). Environmental design, crime and prevention: An examination of community dynamics. In A. J. Reiss Jr & M. Tonry (Eds.), Communities and crime (pp. 387–416). Chicago: University of Chicago Press.Google Scholar
  42. Trickett, E., Beehler, S., Deutsch, C., Green, L., Hawe, P., McLeroy, K., et al. (2011). Advancing the science of community-level interventions. American Journal of Public Health, 101, 1410–1419.CrossRefGoogle Scholar
  43. Wells, W., Schafer, J., Varano, S., & Bynum, T. (2006). Neighborhood residents’ production of order: The effects of collective efficacy on responses to neighborhood problems. Crime & Delinquency, 52, 523–550.CrossRefGoogle Scholar
  44. White, G. (1990). Neighborhood permeability and burglary rates. Justice Quarterly, 7, 57–68.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.College of Social WorkUniversity of South CarolinaColumbiaUSA
  2. 2.School of Social Work and Public Health ProgramUniversity of MissouriColumbiaUSA
  3. 3.Department of Human Development and Family StudiesUniversity of ConnecticutStorrsUSA

Personalised recommendations