In previous weeks, manifest variables of residential and public unit square foot-per-person (USFPP) were created to measure neighborhood overcrowding. An OLS regression analysis of residential USFPP suggested that census tracts with higher percentages of homeownership and median assessed value percent change tend to have larger amounts of residential space, holding all other factors constant. However, this analysis omitted distinctly omitted neighborhood demographic characteristics, which previous research has shown to influence urban housing distributions. In particular, Ruth Peterson and Laurie Krivo (2010) found that racial segregation affects levels of poverty, joblessness, low-wage jobs, and female-headed families such that exclusively black and/or Latino neighborhoods suffer from constellations of disadvantage. Given that residential space increases with income, it is also likely that the racial composition of neighborhoods affects the mean residential USFPP; the negative correlation of public and residential USFPP in turn suggests that public USFPP is similarly affected by neighborhood composition, but in the opposite direction.
To test differences in mean residential and public USFPP among neighborhoods that are relatively segregated or integrated, t tests were used. Following Peterson and Krivo (2010), neighborhood segregation was defined as dichotomous variable where neighborhoods with over 70% of a single racial group were considered segregated and those below that threshold were labeled integrated. Out of 174 census tracts in Boston, 110 were integrated and 63 were segregated (with one missing relevant data). Interestingly, integrated neighborhoods have a mean residential USFPP of 538, in comparison to 740 for segregated neighborhoods, a difference that was statistically significant (p < 0.001). Considering that 83% of segregated neighborhoods were overwhelmingly white, this finding is consistent with those of Peterson and Krivo (2010). The difference in means for public USFPP (554 for integrated, 607 for segregated), however, was not significant, suggesting that the racial composition of a neighborhood is related more to residential than public USFPP.
Peterson and Krivo (2010) replicated their results on neighborhood segregation, examining the effects of what they term “hypersegregation,” which is defined as neighborhoods comprised of 90% or more of a single race. Following their work, neighborhood segregation was separated into three groups: integrated (70% single race or below, N = 110), segregated (70%-90% single race, N=50), and hypersegregated (>90% single race, N= 13). A one-way ANOVA was used to test for differences in residential and public USFPP among these three groups. Mean residential space was significantly different among the levels of neighborhood segregation (F = 19.93, p <0.001); posthoc Tukey tests indicate that both levels of segregation differ from integrated neighborhoods, yet no significant difference exists between segregated and hypersegregated neighborhoods. While this may be an issue related to low power (hypersegregated neighborhoods had a very small sample size), it is also possible that the effects of segregation are nonlinear and thus diminish after reaching a certain threshold. Following the results of the t test, there were no significant differences among segregation categories for public USFPP (F=0.39, p =0.675). The graph below clearly illustrates the differences (or lack thereof) among means for both residential and public USFPP. While public space remains similar across neighborhoods of differing levels of segregation, residential space increases for more segregated neighborhoods (probably due to the dominant race in most of these areas being white).
Peterson, R. and Krivo, L. (2010). Divergent social worlds: Neighborhood crime and the racial-spatial divide. American Sociological Association.
#Read in data
All <-read.csv(“~/Desktop/PPUA/Aggregated_CT_11_6.csv”, header=TRUE)
#Create categorical measures of neighborhood segregation and hypersegregation
All$Segregated <- ifelse((All$White >.70 | All$Black >.70 | All$Hispanic >.70 | All$Asian >.70),1,0)
All$Hypersegregated <- ifelse((All$White >.90 | All$Black >.90 | All$Hispanic >.90 | All$Asian >.90),1,0)
All$Neighborhood_segregation <- ifelse(All$Segregated == 1, 1, 0)
All$Neighborhood_segregation <- ifelse(All$Hypersegregated == 1, 2, All$Neighborhood_segregation)
All$Neighborhood_segregation <- as.factor(All$Neighborhood_segregation)
#Perform t-test for USFPP measures based on segregation variable
white <- All[which(All$Segregated==1 & All$White >.70),]
#Perform anova for USFPP measures based on neighborhood_segregation variable
anova <-aov(Residential_USFPP~Neighborhood_segregation, data=All)
anova2 <-aov(Public_USFPP~Neighborhood_segregation, data=All)
means3 <- aggregate(value~Neighborhood_segregation+variable, data=melted3, mean, na.rm=TRUE)
ses<-aggregate(value~Neighborhood_segregation+variable,data=melted3, function(x) sd(x, na.rm=TRUE)/sqrt(length(!is.na(x))))
merge<-transform(merge, lower=mean-se, upper=mean+se)
levels(merge$variable)<-c(“Residential USFPP”,”Public USFPP”)
graph <-ggplot(data=merge, aes(x=Neighborhood_segregation, y=mean, fill=variable)) + geom_bar(stat=”identity”,position=”dodge”) + geom_errorbar(aes(ymax=upper, ymin=lower),position=position_dodge(.9)) + ylab(“Mean”)
graph + labs(x = “Level of Neighborhood Segregation”, y = “Mean Unit Square Foot-Per-Person”, title = “Comparison of Residential and Public USFPP by Neighborhood Segregation”)