US Polling Places 2012-2020

Tracking Changes in US Polling Places from 2012 to 2020

This project aims to analyze the changes in US polling places over the period from 2012 to 2020, examining trends and shifts in polling location availability at the county level.

Author

Affiliation

DataDuo

School of Information, University of Arizona

Dataset

#| label: load-dataset
#| message: false
polling_places <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-16/polling_places.csv')


dim(polling_places)

[1] 461445     15

names(polling_places)

 [1] "election_date"     "state"             "county_name"      
 [4] "jurisdiction"      "jurisdiction_type" "precinct_id"      
 [7] "precinct_name"     "polling_place_id"  "location_type"    
[10] "name"              "address"           "notes"            
[13] "source"            "source_date"       "source_notes"

A brief description of your dataset including its provenance, dimensions, etc. as well as the reason why you chose this dataset.

Make sure to load the data and use inline code for some of this information.

Dataset Description

The dataset contains information on US polling places from 2012 to 2020, including locations, years active, demographic information of precincts, voter turnout, and changes in polling place locations over time. The dataset has rows and columns. It was chosen to analyze trends and factors influencing polling place changes and voter accessibility

Questions

The two questions we want to answer.

Question 1 : What are the trends in the number of polling places over time, both nationally and across different states from 2012 to 2020?

Question 2 : How does the availability of polling places vary between urban and rural areas, and what impact does this disparity have on voter turnout?

Analysis plan

A plan for answering each of the questions including the variables involved, variables to be created (if any), and external data to be merged in (if any).

Analysis plan for question 1 :

The Variables involved: Independent variable - year (election_date), Dependent Variable: Number of polling places(count), State, Location Type, Location Details (precinct_name).
Preparation of the data: Extract the year from the “election_date” variable to create the “year” variable. And next group the data by year and count the number of unique polling places for each year.
Analysis: Plot with years on the x-axis and number of polling places on the y-axis to visualize trends and also analyze changes in polling places across states.

Analysis plan for question 2 :

The Variables involved: Independent variable - area type (urban/Rural) , Dependent variable - Number of polling Places(count), state, election_date
Preparation of the data: Classifying each polling place as either urban or rural based on its location. Group the data by area type and count the number of unique polling places for each combination of area type, state, and election date.
Analysis: Explore the distribution of polling places between urban and rural areas. And then calculate the voter turnout for urban and rural areas and compare them.

#Plan of Attack

    | Task Name            | Status      | Assignee      | Due  | Priority | Summary/Comments                                      |
    |----------------------|-------------|---------------|------|----------|-----------------------|
    | Select Dataset       | Complete    | Luis /Jeevana | 5/28 | High     | Uploaded files to data folder                         |
    | Proposal             | In Progress | Luis/Jeevana  | 6/3  | High     | Continue updating proposal                            |
    | Explore Data         | In Progress | Luis/Jeevana  | 6/1  | Moderate | Identify important variables                          |
    | Create New Variables | In Progress | Luis/Jeevana  | 5/31 | Moderate | Select and review variables by due date.              |
    | Visualization Draft  | Not Started | Luis/Jeevana  | 6/3  | Moderate | Attempt first visualization                           |
    | Presentation Draft   | Not Started | Luis/Jeevana  | 6/7  | Moderate | Complete first presentation draft with visualizations |
    | Website Draft        | Not Started | Luis/Jeevana  | 6/7  | Moderate | Draft Website                                         |
    | Write Up Draft       | Not Started | Luis/Jeevana  | 6/14 | Moderate | Complete first write-up draft                         |
    | Final Draft (All)    | No started  | Luis/Jeevana  | 6/21 | High     | Complete documents                                    |
    | Final Presentation   | Not started | Luis/Jeevana  | 6/24 | High     | Deliver/Submit Presentation, write-up, and website.   |

--- title: "US Polling Places 2012-2020" subtitle: "Tracking Changes in US Polling Places from 2012 to 2020" author: - name: "DataDuo" affiliations: - name: "School of Information, University of Arizona" description: "This project aims to analyze the changes in US polling places over the period from 2012 to 2020, examining trends and shifts in polling location availability at the county level." format: html: code-tools: true code-overflow: wrap code-line-numbers: true embed-resources: true editor: visual code-annotations: hover execute: warning: false --- ```{r} #| label: load-pkgs #| message: false #| include: false if (!require("pacman")) install.packages("pacman") # use this line for installing/loading pacman::p_load(tidyverse, lubridate) # set width of code output options(width = 65) # set figure parameters for knitr knitr::opts_chunk$set( warning = FALSE, message = FALSE, fig.width = 7, # 7" width fig.asp = 0.618, # the golden ratio fig.retina = 3, # dpi multiplier for displaying HTML output on retina fig.align = "center", # center align figures dpi = 300 # higher dpi, sharper image ) ``` ## Dataset ```{r} #| label: load-dataset #| message: false polling_places <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-16/polling_places.csv') dim(polling_places) names(polling_places) ``` A brief description of your dataset including its provenance, dimensions, etc. as well as the reason why you chose this dataset. Make sure to load the data and use inline code for some of this information. ## Dataset Description The dataset contains information on US polling places from 2012 to 2020, including locations, years active, demographic information of precincts, voter turnout, and changes in polling place locations over time. The dataset has rows and columns. It was chosen to analyze trends and factors influencing polling place changes and voter accessibility ## Questions The two questions we want to answer. Question 1 : What are the trends in the number of polling places over time, both nationally and across different states from 2012 to 2020? Question 2 : How does the availability of polling places vary between urban and rural areas, and what impact does this disparity have on voter turnout? ## Analysis plan - A plan for answering each of the questions including the variables involved, variables to be created (if any), and external data to be merged in (if any). ### Analysis plan for question 1 : - The Variables involved: Independent variable - year (election_date), Dependent Variable: Number of polling places(count), State, Location Type, Location Details (precinct_name). - Preparation of the data: Extract the year from the "election_date" variable to create the "year" variable. And next group the data by year and count the number of unique polling places for each year. - Analysis: Plot with years on the x-axis and number of polling places on the y-axis to visualize trends and also analyze changes in polling places across states. ### Analysis plan for question 2 : - The Variables involved: Independent variable - area type (urban/Rural) , Dependent variable - Number of polling Places(count), state, election_date - Preparation of the data: Classifying each polling place as either urban or rural based on its location. Group the data by area type and count the number of unique polling places for each combination of area type, state, and election date. - Analysis: Explore the distribution of polling places between urban and rural areas. And then calculate the voter turnout for urban and rural areas and compare them. #Plan of Attack ``` | Task Name | Status | Assignee | Due | Priority | Summary/Comments | |----------------------|-------------|---------------|------|----------|-----------------------| | Select Dataset | Complete | Luis /Jeevana | 5/28 | High | Uploaded files to data folder | | Proposal | In Progress | Luis/Jeevana | 6/3 | High | Continue updating proposal | | Explore Data | In Progress | Luis/Jeevana | 6/1 | Moderate | Identify important variables | | Create New Variables | In Progress | Luis/Jeevana | 5/31 | Moderate | Select and review variables by due date. | | Visualization Draft | Not Started | Luis/Jeevana | 6/3 | Moderate | Attempt first visualization | | Presentation Draft | Not Started | Luis/Jeevana | 6/7 | Moderate | Complete first presentation draft with visualizations | | Website Draft | Not Started | Luis/Jeevana | 6/7 | Moderate | Draft Website | | Write Up Draft | Not Started | Luis/Jeevana | 6/14 | Moderate | Complete first write-up draft | | Final Draft (All) | No started | Luis/Jeevana | 6/21 | High | Complete documents | | Final Presentation | Not started | Luis/Jeevana | 6/24 | High | Deliver/Submit Presentation, write-up, and website. | ```