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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Child Youth Care Forum. 2020 Sep 4;50(2):247–259. doi: 10.1007/s10566-020-09572-3

Is a school’s neighborhood physical disorder related to its academic outcomes?

Mieka Smart 1, Julia Felton 1, Cristian Meghea 2, Zachary Buchalski 1, Leah Maschino 1, Richard Sadler 1
PMCID: PMC7987135  NIHMSID: NIHMS1626549  PMID: 33767573

Abstract

Background:

Neighborhood environment for student residences has been linked to differences in academic outcomes. However, school neighborhood has not been studied as a potential additional environmental factor in academic outcomes.

Objective:

The goal of this study was to explore the association between school neighborhood disorder and academic outcomes.

Methods:

School neighborhood physical disorder data were paired with school academic achievement and attendance data. Using regression analyses, we examined whether academic achievement and attendance were predicted using NIfETy neighborhood physical disorder scores for the 21 schools within the boundaries of Flint.

Results:

Neighborhood physical disorder was significantly negatively associated with mathematics scores (β=−7.71707, p=0.0430425), but not with English Language Arts (ELA) scores (β=−4.35, p=0.13). We found a significant curvilinear relationship between neighborhood physical disorder and attendance.

Conclusions:

This study supplements existing literature by focusing on neighborhood physical disorder at the school. We found evidence that school neighborhood may impact academic achievement. These findings complement previous research showing that neighborhood of residence factors, such as structural disadvantage, impact school performance. Students exposed to economically disadvantaged neighborhoods at school, regardless of where they live, may have poorer academic skills.

Keywords: Neighborhood physical disorder, NIfETy scores, Youth outcomes, Schools, Academic achievement, Attendance

Introduction

Structural characteristics of neighborhoods can undermine social control and increase levels of violence, crime, and other negative outcomes (Sampson and Raudenbush 2001). For example, multiple studies have indicated that a strong correlation exists between vacancies, due to home foreclosures, and violent crime on nearby blocks (Sackett 2016). A 2003 study demonstrated that boarded-up housing remained a predictor of gonorrhea rates, all-cause premature mortality, and premature mortality related to diabetes, homicide, and more (Cohen et al. 2003). Minority populations are more susceptible to experiencing a variety of mutually reinforcing structural hardships (e.g. poor housing conditions, poor quality of roads, and food deserts) that give rise to poor health outcomes (Brown et al. 2019).

Neighborhood physical disorder, which refers to the observed and perceived physical and social characteristics evidencing social control, is associated with a host of deleterious physical effects in children, including reduced physical activity (Molnar et al. 2004) and youth increased screen time (Carson and Janssen 2012). One study found a correlation between neighborhood disadvantage and poorer sleep, including childhood obstructive sleep apnea (Spilsbury et al. 2006). Another has shown a correlation between poorly structured neighborhoods and unhealthy dietary habits (Lee and Cubbin 2002). An increase in the odds of childhood obesity was significantly correlated with neighborhood physical disorder (Singh et al. 2010).

Neighborhood disorder affects youth psychologically, as well. Neighborhood disorder is associated with poor mental health outcomes, including externalizing behaviors (Jocson and McLoyd 2015), Major Depressive Disorder (Massey et al. 2004), and substance use (Wilson et al. 2005). Additionally, there is mounting evidence that neighborhood factors influence physiological stress response in youth. First, neighborhood risks may contribute to high biologically mediated stress levels identifiable in adolescents (Theall et al. 2012). Furthermore, children living in high disordered neighborhoods have significantly shorter telomeres, which correlate with higher stress levels (Theall et al. 2013). Neighborhood stressors, including violent crime, domestic violence, liquor stores, and convenience stores, are significantly associated with biological stress, shorter telomeres, lower acute cortisol levels, and other negative outcomes in children (Theall et al. 2017).

Conversely, various interventions deliberately designed to address neighborhood physical disorder have seen positive effects on outcomes like stigma, collective efficacy, and life expectancy (Tebes et al. 2015; Furr-Holden et al. 2019). In neighborhoods of high vacancy, negative mental health could be buffered against by way of strong social ties (Pearson et al. 2019). For instance, the Porch Light Program; a community-level public art intervention in Philadelphia, caused a decrease in feelings of stigma towards individuals with mental health and substance use disorders, and an increase in collective efficacy and perceptions of aesthetic quality and safety (Tebes et al. 2015). Some cities have even enacted legislation designed to remedy neighborhood physical disorder. For example, in Baltimore, MD, it was once required that no liquor store be located within 300 feet of a school. This policy was modified by a new zoning code, TransForm Baltimore, where it is now required that all new liquor stores be at least 300 feet from any other alcohol outlet. Since then, evaluation of TransForm Baltimore has found a negative bivariate association between liquor store density and average life expectancy (Furr-Holden et al. 2019).

In Flint, the presence of neighborhood physical disorder arises from a combination of factors, including industry disinvestment, increased crime/poverty rates, the outmigration of residents, and the effects of the Flint Water Crisis. Well before major employers like General Motors disinvested tens of thousands of jobs from the area, racism in housing and white flight drove residents out of the city, increasing regional socio-spatial inequalities (Sadler and Highsmith 2016). The neighborhoods that suffered the most from racially motivated suburban flight were targeted areas of blockbusting. These neighborhoods flipped from being 100% white and middle class to >90% African American within just a few years, which itself increased vacancies, as fewer residents moved in than moved out (Sadler and Lafreniere 2017). Today, these areas often have higher crime, though the most heavily vacant neighborhoods see crime diminish as beautification efforts supplant blighted landscapes (Sadler et al. 2017). The net effect has been that Flint is now predominantly black and impoverished, and paradoxically bears the brunt of responsibility for population and infrastructure decline. Targeted programming towards youth in Flint provides many resources, some of which are holistic, while simultaneously providing services like a school-based Women Infants and Children (WIC) clinic, health navigator, nutrition education, physical activity programs, literacy tutoring, sports, and staff support (Flint Neighborhoods United 2015). Despite the relevant national evidence that neighborhood order and disorder are strongly related to many health outcomes, there are relatively few examples in the United States of interventions that perform wraparound physical and social intervention at the neighborhood level. Such interventions are expensive. One famous example is the Harlem Children’s Zone, which is estimated to cost $16,000 per child per year, with a portion (<$5,000 per year) going to neighborhood interventions (Jobson 2017).

With a $20,166 per student budget for the 2016–17 school year, according to the Michigan Department of Education (2018), Flint schools are not currently underfunded, relative to the Harlem Children’s Zone. Flint is in a position to make significant strides in student achievement, as there is evidence that the built environment is associated with students’ academic outcomes (Szapocznik 2006). Prior research also indicates that academic achievement is associated with the condition of the building in which students learn, mediated by the social climate and student attendance (Maxwell, 2016). Moreover, there is evidence that residential neighborhood social and physical environmental safety has been linked to youth academic achievement in at least one other urban center (Milam 2010).

Because no prior research in the extant literature focuses on school neighborhood disorder as a potential factor contributing to youth academic achievement, this research sought to explore whether the objective construct of physical disorder was associated with academic achievement across Flint schools. We hypothesized that neighborhood physical disorder was inversely associated with school-related youth outcomes.

Methods

Data Sources

The research for this manuscript did not involve human subjects, and was covered under a “not human subjects” research designation given by our university Institutional Review Board.

School data

School data were sourced from the publicly available Michigan School database in January 2019. Our unit of analysis was the grade level, meaning that each grade level within any school was treated as a distinct unit. Across the 21 schools in Flint, this approach resulted in a final sample of 107 grade levels with available attendance data. Scaled mean English Language Arts (ELA) and math scores from Michigan Student Test of Educational Progress (M-STEP) assessments were available online for 53 grade levels. The M-STEP is an online exam given to students in 3rd–8th grade to measure their proficiency of ELA and mathematics in comparison to state standards (Michigan Department of Education 2020). The M-STEP assessments aggregate level reports provide performance data based on groups of students—grouped by grade, school, district, Intermediate School District (ISD), and state. Data for the 2017–18 school year were used to maximize temporal proximity to the neighborhood data (MI School Data 2018).

Neighborhood data

The neighborhood data were collected using the Neighborhood Inventory for Environmental Typology (NIfETy) method in May-August 2018 (Smart et al. 2019). The NIfETy method was designed to characterize physical and social order and disorder. 15 data collectors were extensively trained and required to pass a field test, or complete remediation training, before going out into the field. In addition, data collectors were required to attend bi-weekly team meetings and were carefully supervised throughout the entire process. Pairs of raters performed data collection by employing the NIfETy method with high fidelity. Interrater reliability via intraclass correlation coefficient (ICC) for the NIfETy Physical Disorder subscale (ICC ~ .83) is in the almost perfect range (Furr-Holden, et al., 2010). A thorough description of the NIfETy method can be found in Furr-Holden et al. (2008). A description of the metric properties of the instrument can be found in Furr-Holden et al. (2010).

Flint has roughly 6,400 block faces, which are residential unit block street segments taken from a Geographic Information Systems road segment layer, where both sides of each segment have a combined potential address range of 1–100 units. NIfETy data were collected for 440 randomly selected block faces. For this paper, we used Kriging to create a surface derived from areal interpolation that estimates the predicted NIfETy score at any point in the city. For each point, scores were derived from many nearby NIfETy observations, with nearer observations given greater weight.

Spatial and statistical analysis

All model building, plotting, and data manipulation was done in R version 3.5.1 with the packages psych, lme4, and nFactors (R Core Team 2019). Data for 41 NIfETy variables related to neighborhood physical disorder were reduced to create a single score for each of the 440 block faces. This was created via a factor analysis, using the oblimin rotation method and the Thurstone weighting method. Using the Kernel Density tool in ArcGIS, we created a continuous raster layer interpolated from the neighborhood physical disorder scores for each of the 440 sampled blocks in the city of Flint. We then created a separate school layer by geocoding the 21 area schools. We used the Spatial Analyst Extract by Points tool to extract the raster cell value at each geocoded school point, the result of which is the neighborhood physical disorder score for each school.

The neighborhood physical disorder scores were used as predictors of mean math and language arts achievement test scores (n=53) in a random intercept mixed-effects linear regression model, with grade-level used as a random effect. The neighborhood physical disorder scores were also used as predictors of attendance (n=107) in a random intercept mixed-effects linear regression model, again with grade-level used as a random effect, and including a quadratic term. Betas and t-statistics were generated, and p-values were calculated using Satterthwaite’s method (1946). An alpha level of <.05 was used as the threshold for significance.

Results

Regression Analysis

Table 1 shows the Michigan Student Test of Educational Progress (M-STEP) results for Flint area schools. Throughout this community, schools vary in terms of grade levels, for instance Kindergarten-2nd grade, Kindergarten-8th grade, or even 7th grade-12th grade. For the 13 schools in which data could be obtained, English Language Arts (ELA) scores ranged from 1262.3–1775.5. In addition, mean math scores ranged from 1226.6–1774.1 for the 13 schools in which data was collected. There are 17 schools with available attendance data, and attendance rates ranged from 54.73–96.95.

Table 1:

M-STEP and attendance score ranges by school

School Grade Minimum and Maximum of ELA Mean Scaled Scores Minimum and Maximum of Math Mean Scaled Scores Minimum and Maximum of Attendance Rates (%)
Richfield Early Learning Academy K N/A N/A 78.39
Brownell STEM Academy K-2 N/A N/A 82.84 – 86.59
Doyle/Ryder School K-6 1267.2 – 1569.8 1270.4 – 1555.5 83.4 – 89.87
Eagle’s Nest Academy K-6 1267 – 1566.6 1269.6 – 1562.6 87.25 – 95.53
Eisenhower School K-6 1273.7 – 1555.1 1273.7 – 1552.9 76.6 – 91.08
Freeman School K-6 1268.9 – 1578.5 1264.2 – 1564.3 86.46 – 91.2
Neithercut Elementary School K-6 1262.9 – 1571.1 1226.6 – 1559.3 87.64 – 92.82
Pierce School K-6 1282.1 – 1576.4 1284.6 – 1577 87.66 – 92.18
Durant Turri Mott School K-7 1286.1 – 1675.1 1267.8 – 1664.7 88.61 – 91.85
Potter School K-8 1262.3 – 1775.5 1267.9 – 1766.3 81.21 – 83.52
International Academy of Flint K-12 1274.7 – 1770.1 1276.7 – 1774.1 86.85 – 93.46
Holmes STEM Academy 3–8 1272.9 – 1761.3 1272.9 – 1756.9 84.5 – 89.22
Accelerated Learning Academy 7–12 1661.3 – 1754.2 1648.2 – 1751.4 59 – 90.3
Southwestern Classical Academy 7–12 1663.6 – 1762.4 1663.8 – 1757.4 82.24 – 88.24
WAY Academy 7–12 1671.9 – 1773.3 1661.2 – 1770.5 54.73 – 70.61
Gateway to College – Mott Community College 9–12 N/A N/A 100
Genesee Early College 9–12 N/A N/A 90.32 – 96.95
Mott Middle College High School 9–12 N/A N/A 72.32 – 93.65
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Table 2 shows the results of the factor analysis utilized to create a score for block faces throughout Flint. These results do not necessarily reflect measures taken directly at the school—a kernel density interpolation was used to estimate physical disorder scores at the point of each school’s location. The factor loading gives an indication of how much weight each physical disorder indicator has in the overall score.

Table 2:

Variables used in creation of NIfETy physical disorder score

Variable
 Prevalence Rate
 Factor Loadings
Total # of Broken Windows 0.27 0.68
# of Un-boarded Abandoned Bldgs. 0.21 0.68
Vacant Lots 0.52 0.68
Un-Maintained Property 0.52 0.67
Trash in Other Open Spaces 0.47 0.67
# of Boarded Abandoned Bldgs. 0.38 0.57
Broken Bottles 0.37 0.51
Trash in Street 0.26 0.38
Damaged Sidewalks 0.78 0.37
# of Vacant Houses 0.34 0.31
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Table 3 reports the linear model regression results and reports the standard deviation for the grade level random effect. The NIfETy neighborhood physical disorder indicators are negatively associated with both Mean ELA and Math M-STEP scores (β=−7.707 and β=−4.352, respectively). However, while the association is significant for math scores (p=0.043), it is not for ELA scores (p=0.132). The grade level explained much of the variance, with an adjusted ICC of 0.999 for the ELA model and an adjusted ICC of 0.998 for the math model. Figure 1 shows a visual representation of the regression of attendance by grade and the line of best fit. The plot shows a significant curvilinear relationship (F2,106=13.1, p<.001), reflecting an apparent reversal in the direction of the association between neighborhood physical disorder and attendance at the intermediate region of the disorder continuum. The quadratic model is also significantly more predictive than a candidate linear model (F1,106=23.3, p<0.01).

Table 3:

Association between academic outcomes and school neighborhood physical disorder assessments

Response Variable Predictor Regression Estimate
(95% CI)		  t (Pr(>| t |)) SD of Random Effect
(95% CI)
Mean Math MERPS Score Order −7.71 (0.402, 15.021) 2.087 (0.0425) –
Grade Level – – 185.18 (104.94, 339.02)
Mean ELA MERPS Score Order −4.35 (−1.260, 9.969) 1.534 (0.132) –
Grade Level – – 185.77 (105.29, 340.08)
Attendance Rate Order2 29.25 (17.24, 41.26) 4.828 (4.66×10−6) –
Order −23.08 (14.03, 32.14) 5.054 (1.82×10−6) –
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For mixed effect models, the Satterthwaite method of approximating denominator degrees of freedom was used. Order refers to the NIfETy physical disorder scores.

Fig 1. Association between attendance rate and neighborhood disorder.

Fig 1

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There is a significant curvilinear relationship, with a reversal in the direction of the association after the intermediate region of the disorder continuum. Shading refers to a 95% confidence interval for the regression curve.

Discussion

This evidence supports the existing literature regarding the negative impacts of neighborhood physical disorder on youth outcomes, but we are the first to objectively evaluate and find a significant relationship between the school neighborhood physical environment and its impact on academically related youth outcomes. Understanding the effect of neighborhood conditions on child and youth academic outcomes is important for considering potential physical environmental interventions in the school’s neighborhood environment (the physical environment that surrounds a school), an environment that has not received adequate scientific attention. With this research, we found evidence that neighborhood physical disorder may be correlated with the academic success of Flint children. We found significant relationships between 1) neighborhood physical disorder and attendance and 2) neighborhood physical disorder and academic achievement in mathematics. Because this our physical disorder data is based not on opinion/perception of the neighborhood, but on counts of observable indicators by raters, this research also provides an objective accounting of the conditions of the communities that children are exposed to while at school and while in transit to school.

The study findings are of interest because this evidence supports the existing literature regarding the negative impacts of neighborhood disorder on youth outcomes (Molnar et al. 2004; Carson and Janssen 2012; Spilsbury et al. 2006; Lee and Cubbin 2002; Singh et al. 2010; Theall et al. 2012, 2013, and 2017). However, we are the first to objectively evaluate and find a significant relationship between the school neighborhood environment and academically related youth outcomes.

Two findings merit additional consideration. First, we found that neighborhood physical disorder had a curvilinear relation with attendance in Flint. It appears as if both highest and lowest neighborhood physical disorder are associated with higher rates of attendance. Curvilinear relationships in school data are also found in other areas. For example, a 2019 study indicated a curvilinear trend regarding behavioral school engagement among Chinese students (Zhu et al. 2019). Another 2019 study indicated curvilinear trends regarding the personality-wellbeing relationship of personality traits in school children (Perret et al. 2019). In 2019, a study showed curvilinear trends regarding an increase in reliance on SNAP meal benefits (school breakfast and lunch program) towards the end of the month among participating school children (Laurito and Schwartz 2019). It is plausible, then, that efforts and interventions designed to maximize attendance are utilized most at the highest and lowest ends of the neighborhood physical disorder spectrum. Second, we find that neighborhood physical disorder predicts academic achievement in mathematics but not ELA scores. It may be the case that the relationship between neighborhood physical disorder and ELA scores are moderated by teacher instruction style, which has been demonstrated to be significantly related to English Language Arts (ELA) achievement but not mathematics achievement (Supovitz et al. 2010).

Several important study limitations merit attention. This work did not account for the different ways that children are influenced by their school neighborhood environments. A 2000 study found that children living in high violence neighborhoods possess a more negative perception (e.g. felt more unsafe playing outside, more distrustful of police, etc.) than children living in low violence neighborhoods (Farver et al. 2000). Moreover, not all children will perceive a given exposure to (dis)order in a homogenous way. Studies employing person-centered techniques to research neighborhood perception have found that the subjective experiences that residents may have within their communities are highly variable (Booth et al. 2018; Dupéré and Perkins 2007).

Although we found significant evidence that neighborhood physical disorder is associated with math scores and attendance, the result for ELA scores only trended towards significant, which may be due to small sample size. Another potential limitation is interviewer bias introduced during the collection of the NIfETy data—that those data collectors completing the inventories were implicitly biased toward having neighborhoods profiled in such a way as to produce either positive or negative effects. Extensive data collector training, team meetings, and careful supervision should have addressed this issue, but it is still a possibility.

Additionally, we could not control for sex or other demographics because we do not have individual data, therefore no demographic information on the schools exist in the data set. Grade level is used as a random effect because there is a strong relationship between grade level and test score. Without controlling for grade level, no significant effect is seen between school achievement and neighborhood disorder. Reducing the variant blocking by grade level allows us to see the relationships between school neighborhood and school achievement outcomes. Finally, several other important factors not included in this analyses that would have strengthened this work. Physical condition of the school building and social climate within the school are known correlates with academic outcomes (Maxwell, 2016). In addition neighborhood social environment is a potential factor that would strengthen this research, and should be included in future research.

There is potential for research that builds from these findings. For example, while the positive relationship between residential neighborhood order and academic outcomes is well-established in the literature (Turner and Berube 2009; DeLuca and Rosenblatt 2010; Sard and Rice 2016), introduction of school neighborhood as an additional factor might elucidate how school neighborhood interacts with residential neighborhood to impact academic outcomes. In terms of potential opportunities for intervention, these findings indicate that investment in solutions to neighborhood physical disorder might improve learning outcomes for Flint area schoolchildren. Obtaining individual data from area education programs would allow us to understand how residential neighborhoods and school neighborhoods might interact to impact academic outcomes.

Acknowledgements

The authors wish to acknowledge the project’s funding source: the National Institute on Minority Health and Health Disparities award number U54MD011227 (PI: Furr-Holden). We acknowledge the following individuals and organizations whose efforts and ideas contributed to this manuscript: Samantha Cardenas, Jordan Johnson, Megan Mulheron, Neletha Skelton and the NIfETy field data collection team.

Funding: The National Institute on Minority Health and Health Disparities award number U54MD011227

Footnotes

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

a) Declarations

Conflicts of interest/Competing interests: Not applicable

Availability of data and material: The datasets generated and/or analyzed during the current study can be made available by request to flintareastudy@msu.edu.

Code availability: Not applicable

Ethics approval: The research for this original manuscript did not involve human subjects, and is covered under a “not human subjects” research designation given by the Michigan State University Institutional Review Board.

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