Introduction

For this project, I am analyzing the rate of homelessness in Baltimore County and Baltimore City over a the span of 2007 to 2016. I will be looking to see if there is an increase or decrease in the rate of homelessness in these counties, and if there are any years that particularly standout in terms of outstanding amounts of homeless individuals in Baltimore City & County. I am also analyzing the measures at which homelessness is occurring in Baltimore in both the city and the county, looking at those who are deemed “Chronically Homeless Individuals”. Taking a deeper look into this with the help of an interactive map, as well as a bar graph will help one to understand what the trend seen in homelessness may be in these counties. I retrieved my data from Kaggle https://www.kaggle.com/adamschroeder/homelessness in order to gather this information.

First, I download all necessary packages needed for this:

library(tidyverse)
library(tibble)
library(knitr)

Tableau Map!

Below, you will see the interactive map I created in Tableau to see the total population of chronically homeless individuals over the span of 2007 to 2016 by using the slider to see the state’s color change and the number of homeless citizens

Looking at this map, it is clear to see that the number of homeless citizens clearly changes over the almost decade long period, but it can be a bit confusing to understand what exactly is going on when looking at trends of homelessness in Baltimore City and County during the years of 2007 to 2016.

Lets visualize this another way…

I generated a bar graph (below) as well as a map that clearly lays out which years homelessness was at it’s highest and lowest for these two counties

When looking at this bar graph, it is clear to see that 2011, 2015, and 2016 were outstanding years in terms of looking at homelessness trends. While there is not linear trend, it is clear to see that Baltimore City an County have a relatively upward trend of homelessness as the years become more present (2015 and 2016). There are significantly less chronically homeless individuals in 2007 (456 people) than in 2016 (1,426 people). Looking at the bar graph, it is clear to see that the amount of homeless people almost doubles in less than a decade.

I have also provided a table to lay this out clearly as well.

First I created a variable called “homelessness” and continued to narrow down which states and counties I looked at from there.

homelessness <- read_csv("~/downloads/2007-2016-Homelessness-USA (1).csv")

homelessness %>%
  filter(State %in% "MD")-> MD_homelessness

When looking at “MD_homelessness”, I narrowed my search down even more to the two counties I was studying.

MD_homelessness %>%
  filter(CoCName %in% c("Baltimore County CoC" , "Baltimore City CoC"))-> Bmore_homelessness

With the “Bmore_homelessness” variable, I was able to look closely at the rates and trends at which homelessness was clearly rising in Baltimore City and County and created a table to lay this out as well.

Bmore_homelessness%>%
  filter(Measures %in% "Chronically Homeless Individuals")%>%
  select(-CoCNumber)%>%
  arrange(desc(Count))%>%
  knitr::kable()
Year State CoCName Measures Count
1/1/09 MD Baltimore City CoC Chronically Homeless Individuals 853
1/1/10 MD Baltimore City CoC Chronically Homeless Individuals 853
1/1/16 MD Baltimore City CoC Chronically Homeless Individuals 585
1/1/15 MD Baltimore City CoC Chronically Homeless Individuals 541
1/1/11 MD Baltimore City CoC Chronically Homeless Individuals 519
1/1/14 MD Baltimore City CoC Chronically Homeless Individuals 475
1/1/07 MD Baltimore City CoC Chronically Homeless Individuals 410
1/1/08 MD Baltimore City CoC Chronically Homeless Individuals 410
1/1/12 MD Baltimore City CoC Chronically Homeless Individuals 308
1/1/13 MD Baltimore County CoC Chronically Homeless Individuals 245
1/1/12 MD Baltimore County CoC Chronically Homeless Individuals 217
1/1/13 MD Baltimore City CoC Chronically Homeless Individuals 211
1/1/15 MD Baltimore County CoC Chronically Homeless Individuals 164
1/1/11 MD Baltimore County CoC Chronically Homeless Individuals 145
1/1/10 MD Baltimore County CoC Chronically Homeless Individuals 139
1/1/14 MD Baltimore County CoC Chronically Homeless Individuals 105
1/1/16 MD Baltimore County CoC Chronically Homeless Individuals 87
1/1/09 MD Baltimore County CoC Chronically Homeless Individuals 76
1/1/08 MD Baltimore County CoC Chronically Homeless Individuals 72
1/1/07 MD Baltimore County CoC Chronically Homeless Individuals 46

Looking at this table paired with the bar graph created in Tableau, in it clear to see the count of how many people were homeless in the years 2007-2016 given from the Kaggle data set. It is clear to see that as years become more recent, homelessness ramps up. This could be due to a multitude of reasons, but 2015 stands out most to me because this was when Baltimore struggled with rebuilding after the riots occurred. The data trends I saw between 2015 and 2016 were not entirely surprising knowing what had happened in Baltimore in 2015, but I was surprised to see the rate of homelessness almost double from 2007 to 2016 in total.

Conclusion

With the data I analyzed I found that homelessness in Baltimore does not really being to see strong trends in increasing homelessness until after 2011. I found that 2015 and 2016’s homelessness counts were specifically significant because this occurred in light of and following the Baltimore riots of 2015, which hurt many financially who lived in the areas where it happened. According to this article https://www.nydailynews.com/news/national/baltimore-woman-homeless-jobless-due-rioters-article-1.2202301, which was one example out of many, many Baltimore citizens lost their jobs after the riots occurred and the city was trying to rebuild. This caused many to become homeless, as there was no income coming in for families that needed it, which can explain the spike in chronically homeless individuals seen in 2015 and 2016. Overall, the data was very interesting to look at on a larger scale, but homelessness does not seem to skyrocket until 2011, and then later on in 2015.