Examining difference in residential characteristics

For this week, I expanded my analysis of the character of residential properties and culture. I wanted to continue exploring the effect of having “cultural” sites in a neighborhood. Cultural sites include schools, churches, and libraries. I will continue working on this definition of cultural sites and will make some adjustments moving forward. It is likely that the inclusion of universities skews some of the results.

I began by making a new binary variable for whether a neighborhood had greater than 1 cultural site, or had 1 or fewer.

TADCT <- read.csv(“/TADCT.csv”)
TADCT$CULT_YES <- ifelse(TADCT$Cult_count > 1, 1, 0)
summary(TADCT$CULT_YES)

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
0.0000  0.0000  1.0000  0.5899  1.0000  1.0000

The summary shows that just about 60% of census tracts in Boston have more than 1 cultural site.

I next ran a t.test to see the effect of cultural sites on median residential value.

t.test(resValue_median~CULT_YES, data=TADCT)

data:  resValue_median by CULT_YES
t = 1.3661, df = 78.846, p-value = 0.1758
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-43396.78 233278.77
sample estimates:
mean in group 0 mean in group 1
512173.2        417232.2

The test shows that there is a difference in means between census tracts with more than one cultural site. Confirming earlier analysis, tracts with more cultural sites have lower median home values. However, the t.test has a p-value of 0.18, which is not statistically significant.

Next I conducted an ANOVA statistical test to compare means of residential parcel values by neighborhood.

Anova <- aov(resValue_median~BRA_PD, data=TADCT)

summary(anova)
Df         Sum Sq      Mean Sq F value   Pr(>F)
BRA_PD       16  6540283795839 408767737240   3.274 0.000065 ***
Residuals   156 19475545536842 124843240621

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
5 observations deleted due to missingness

The summary of the ANOVA shows that there are statistically significant differences in means of residential properties between neighborhoods. The Tukey test conducts pairwise analysis between neighborhoods. The sample below shows some of the neighborhoods have higher or lower mean residential property values.

Fenway/Kenmore-East Boston                  119815.833
Hyde Park-East Boston                               -16012.500
Jamaica Plain-East Boston                         117075.449
Mattapan-East Boston                                 -8616.667
North Dorchester-East Boston                   -21960.417
Roslindale-East Boston                                31747.756
Roxbury-East Boston                                   -14513.640

As expected, Fenway has higher value residences than East Boston, as does Jamaica Plain. Mattapan and Roxbury have lower values than East Boston.

I next constructed a chart to compare the residential home values.

melted <- melt(TADCT[c(15,13)], id.vars=c(“BRA_PD”))
means <- aggregate(value~BRA_PD,data=melted,mean)
names(means)[2] <- “mean”
ggplot(data=means, aes(x=BRA_PD, y=mean)) + geom_bar(stat=”identity”,position=”dodge”, fill=”blue”) + ylab(“Mean”)
ses <- aggregate(value~BRA_PD,data=melted, function(x) sd(x, na.rm=TRUE)/sqrt(length(!is.na(x))))
names(ses)[2]<-‘se’
means <- merge(means,ses,by=’BRA_PD’)
means <- transform(means, lower=mean-se, upper=mean+se)
levels(means$Type)<-c(“Downtown”,”Industrial/Institutional”,”Park”,”Residential”)
bar <- ggplot(data=means, aes(x=BRA_PD, y=mean)) + geom_bar(stat=”identity”,position=”dodge”, fill=”blue”) + xlab(“Neighborhood”) + ylab(“Mean”) + ggtitle(“Residential Parcel Values by Neighborhood”) + theme(axis.text.x = element_text(angle = 90, hjust = 1))
bar + geom_errorbar(aes(ymax=upper, ymin=lower),position=position_dodge(.9))

Maron module 10

This graph shows the range of means of residential parcels by neighborhood. The South End and Back Bay each stand out for having very high total mean values, but a wide variance.

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