Homework 2 - Exploratory Data Analysis

Florida Homelessness by County 2018-2020

homework_2

shelton

homelessness

Author

Dane Shelton

Published

October 18, 2022

Homelessness in Florida

Homelessness is a complex living situation with several qualifying conditions; at its most simple state, the U.S Dept. of Housing and Urban Development defines it as lacking a fixed, regular nighttime residence (not a shelter) or having a nighttime residence not designed for human accommodation¹.

On a single night in 2020, over 500,000² people experienced homelessness in the United States. Florida, with the third largest state population , had the fourth largest homeless population of 2020 with 27,487².

Florida counties represent a large age range and varying demographic profiles; the state is a hub to a variety of industries including tourism, defense, agriculture, and information technology. Investigating homelessness in Florida counties with robust data can lead to several conclusions about who is being impacted where, and how state policy is failing groups of a diverse population.

Carole Zugazaga’s 2004 study of 54 single homeless men, 54 single homeless women, and 54 homeless women with children in the Central Florida area investigated stressful life events common among homeless people. The interviews revealed that women were more likely to have been sexually or physically assaulted, while men were more likely to have been incarcerated or abuse drugs/alcohol. Homeless women with children were more likely to be in foster care as a youth.

Nearly a decade later,county-level data can be used to investigate the relationship between Zugazaga’s reported stressful life events (incarceration, drug arrests, poverty, forcible sex…)³ and homelessness counts.

Research Question

Do particular life stressors increase a population’s vulnerability to homelessness?

Homelessness is not a new issue in the United States, yet homeless policy targets elimination via criminalization rather than prevention. Despite state and federal governments being aware of the circumstances that increase vulnerability to homelessness for decades, I anticipate all of the variables to remain significant in a model relating stressors to Florida homelessness counts 2018-2020.

Research Hypothesis

H₀: All stressors are insignificant in predicting homelessness counts ( B_i = 0 for i=0,1,2,…n )

H_A: At least one stressor B_i is significant in predicting homelessness counts

The data florida_1820.csv⁴ describes population, homelessness counts, poverty counts and several other demographic indicators³ at the county level for 2018-2020. All 67 Florida counties have observations for the 3 years giving us 201 observations of 15 variables. Each observation provides a count of each variables from a single county for a year within 2018-2020.

The data were collected from the Florida Department of Health. Variable names³ were used as search indicators to produce counts for Florida counties. Unfortunately, we cannot accurately analyze the effect of COVID-19 as data is incomplete for the majority of counties in 2021.

Intro to Data

    County               Year      Homeless (Count)   Population     
 Length:201         Min.   :2018   Min.   :   0.0   Min.   :   8367  
 Class :character   1st Qu.:2018   1st Qu.:  11.0   1st Qu.:  28089  
 Mode  :character   Median :2019   Median : 151.0   Median : 130642  
                    Mean   :2019   Mean   : 427.8   Mean   : 317746  
                    3rd Qu.:2020   3rd Qu.: 563.0   3rd Qu.: 367471  
                    Max.   :2020   Max.   :3516.0   Max.   :2864600  
                                                                     
 Unemployment Rate   Median Inc    Incarceration (Rateper1000) Poverty (Count) 
 Min.   : 2.100    Min.   :34583   Min.   : 0.60               Min.   :   906  
 1st Qu.: 3.400    1st Qu.:41401   1st Qu.: 2.50               1st Qu.:  4901  
 Median : 4.000    Median :50640   Median : 3.40               Median : 16210  
 Mean   : 4.697    Mean   :51116   Mean   : 3.84               Mean   : 42922  
 3rd Qu.: 5.600    3rd Qu.:58093   3rd Qu.: 4.50               3rd Qu.: 46034  
 Max.   :13.500    Max.   :83803   Max.   :18.60               Max.   :482656  
                                                                               
 Drug Arrests (Count) Relocated (Rate) Sub Abuse Enrollment (Count)
 Min.   :   13        Min.   : 4.689   Min.   :   5.0              
 1st Qu.:  225        1st Qu.:11.244   1st Qu.:  76.0              
 Median :  729        Median :12.700   Median : 250.0              
 Mean   : 1558        Mean   :13.288   Mean   : 877.6              
 3rd Qu.: 1903        3rd Qu.:14.544   3rd Qu.:1030.0              
 Max.   :13038        Max.   :22.553   Max.   :6272.0              
                                                                   
 Adult Pysch Beds (Count) Severe Housing Problems (Rate) Forcible Sex (Count)
 Min.   :  0.00           Min.   : 9.6                   Min.   :   0.0      
 1st Qu.:  0.00           1st Qu.:13.3                   1st Qu.:  14.0      
 Median :  0.00           Median :15.4                   Median :  45.0      
 Mean   : 66.26           Mean   :15.8                   Mean   : 170.5      
 3rd Qu.: 84.00           3rd Qu.:17.3                   3rd Qu.: 225.0      
 Max.   :778.00           Max.   :29.8                   Max.   :1408.0      
                          NA's   :134                                        
 Foster Care (Count)
 Min.   :   3.0     
 1st Qu.:  33.0     
 Median : 153.0     
 Mean   : 326.1     
 3rd Qu.: 353.0     
 Max.   :2289.0

Expanding Intro to Data exposes summary statistics including mean, range, quantiles, and standard deviation for all 15 variables. The table below the summaries provides arranged figures for basic parameters of interest grouped by county.

LATER: Plots, Isolate more variables of interest with grouping, group by year?

While the data is great illustration of homelessness in Florida by county, there are improvements that could be made to both data collection and the research question itself to further the study.

Data:

Unfortunately, FL Health Charts did not provide demographic breakdown for the homeless population (Age, Sex, Race), which would drastically widen the scope of the analysis, leading to far more interesting conclusions.
There is only have data for a three year period; this is too small of a range to make a strong statement about the impact of homeless policy on Florida counties or how the relevance of certain stressors has changed over time. For a more in depth study I would begin with a 10 year range.

Research Question:

Demographic breakdown of stressors’ impact (Age, Sex, Race)
Extend the question to the entire country, providing a breakdown by state
Compare to foreign countries to contrast governments’ approaches to homelessness and leading causes of homelessness around the world.

LATER: Variable Definitions and Collection Methods here

Later: Carol Zugazaga

Footnotes

_{1.) Homeless Definition}

_{2.) US Interagency Council on Homelessness}

_{3.) Explanation of variables and collection method in Codebook tab}

_{4.) This data was created from several .csv files.; it was cleaned and put in a tidy format in another script; manipulations were messy/ inefficient (brute force) so I did not include the cleaning file.}

--- title: "Homework 2 - Exploratory Data Analysis" subtitle: "Florida Homelessness by County 2018-2020" author: "Dane Shelton" desription: "Research Question, Hypothesis, and Exploratory Data Analysis" date: "10/18/2022" format: html: callout-appearance: "simple" callout-icon: FALSE df-print: paged toc: true code-fold: true code-copy: true code-tools: true categories: - homework_2 - shelton - homelessness --- ```{r} #| label: setup #| include: false #| warning: false library(tidyverse) knitr::opts_chunk$set(echo = FALSE, warning=FALSE, message = FALSE) ``` ## Homelessness in Florida Homelessness is a complex living situation with several qualifying conditions; at its most simple state, the U.S Dept. of Housing and Urban Development defines it as lacking a fixed, regular nighttime residence (not a shelter) or having a nighttime residence not designed for human accommodation^1^. On a single night in 2020, over 500,000^2^ people experienced homelessness in the United States. Florida, with the third largest state population , had the fourth largest homeless population of 2020 with 27,487^2^. Florida counties represent a large age range and varying demographic profiles; the state is a hub to a variety of industries including tourism, defense, agriculture, and information technology. Investigating homelessness in Florida counties with robust data can lead to several conclusions about who is being impacted where, and how state policy is failing groups of a diverse population. ::: panel-tabset ## Research Question Carole Zugazaga's 2004 study of 54 single homeless men, 54 single homeless women, and 54 homeless women with children in the Central Florida area investigated stressful life events common among homeless people. The interviews revealed that women were more likely to have been sexually or physically assaulted, while men were more likely to have been incarcerated or abuse drugs/alcohol. Homeless women with children were more likely to be in foster care as a youth. Nearly a decade later,county-level data can be used to investigate the relationship between Zugazaga's reported stressful life events (incarceration, drug arrests, poverty, forcible sex...)^3^ and homelessness counts. ::: callout-note ## Research Question Do particular life stressors increase a population's vulnerability to homelessness? ::: ## Hypothesis Homelessness is not a new issue in the United States, yet homeless policy targets elimination via criminalization rather than prevention. Despite state and federal governments being aware of the circumstances that increase vulnerability to homelessness for decades, I anticipate all of the variables to remain significant in a model relating stressors to Florida homelessness counts 2018-2020. ::: callout-note ## Research Hypothesis **H~0~:** All stressors are insignificant in predicting homelessness counts **(** B~i~ = 0 for i=0,1,2,...n **)** **H~A~:** At least one stressor **B~i~** is significant in predicting homelessness counts ::: ## Introduction to Data ```{r} #| label: loading florida_1820 #| include: FALSE # This data was cleaned and formatted to a tidy .csv in another .qmd file, the manipulations were messy and probably inefficient (brute force); can upload if needed florida_og <- readr::read_csv('_data/florida_1820.csv', show_col_types = FALSE) ``` The data `florida_1820.csv`^4^ describes population, homelessness counts, poverty counts and several other demographic indicators^3^ at the county level for 2018-2020. All 67 Florida counties have observations for the 3 years giving us 201 observations of 15 variables. Each observation provides a count of each variables from a single county for a year within 2018-2020. The data were collected from the [Florida Department of Health](https://www.flhealthcharts.gov/charts/default.aspx). Variable names^3^ were used as search indicators to produce counts for Florida counties. Unfortunately, we cannot accurately analyze the effect of COVID-19 as data is incomplete for the majority of counties in 2021. ::: {.callout-note collapse="true"} ## Intro to Data ```{r} #| label: EDA #| output: TRUE head(florida_og) summary(florida_og) florida_county <- florida_og %>% group_by(County) florida_county %>% summarize('Mean Population' = mean(Population), 'Mean Homeless' = mean(`Homeless (Count)`), 'Avg Homeless Rate' = mean(`Homeless (Count)`)/mean(Population), 'Avg Median Income'= mean(`Median Inc`), 'Mean Poverty' = mean(`Poverty (Count)`), 'Avg Poverty Rate' = mean(`Poverty (Count)`)/mean(Population)) %>% arrange( desc(`Mean Population`), desc(`Mean Homeless`), desc(`Avg Median Income`))%>% mutate(across(c(2:3, 5:6), round, 0)) ``` ::: Expanding **Intro to Data** exposes summary statistics including mean, range, quantiles, and standard deviation for all 15 variables. The table below the summaries provides arranged figures for basic parameters of interest grouped by county. LATER: Plots, Isolate more variables of interest with grouping, group by year? ## Improvements While the data is great illustration of homelessness in Florida by county, there are improvements that could be made to both data collection and the research question itself to further the study. **Data:** - Unfortunately, [FL Health Charts](https://www.flhealthcharts.gov/charts/default.aspx) did not provide demographic breakdown for the homeless population (Age, Sex, Race), which would drastically widen the scope of the analysis, leading to far more interesting conclusions. - There is only have data for a three year period; this is too small of a range to make a strong statement about the impact of homeless policy on Florida counties or how the relevance of certain stressors has changed over time. For a more in depth study I would begin with a 10 year range. **Research Question:** - Demographic breakdown of stressors' impact (Age, Sex, Race) - Extend the question to the entire country, providing a breakdown by state - Compare to foreign countries to contrast governments' approaches to homelessness and leading causes of homelessness around the world. ## Codebook LATER: Variable Definitions and Collection Methods here ## References Later: Carol Zugazaga ::: ##### Footnotes ~1.) [Homeless Definition](https://www.law.cornell.edu/uscode/text/42/11302)~ ~2.) [US Interagency Council on Homelessness](https://www.usich.gov/tools-for-action/2020-point-in-time-count/)~ ~3.) Explanation of variables and collection method in Codebook tab~ ~4.) This data was\ created\ from\ several\ .csv\ files.;\ it\ was\ cleaned and put in a tidy format in another script; manipulations were messy/ inefficient (brute force) so I did not include the cleaning file.~