In this research, we analyzed voter registration and elections data re-leased by the Florida Division of Elections to investigate the profile of Florida voter participation. The utilized data was associated with federal general elec-tions from 2014 to 2020. Data preparation issues were resolved during the data merging, including exact duplicates, multiple associated vote types, and misclas-sified vote types. The merged data consisted of voter ID, registration county code, zip code, sex, ethnicity, age, vote type for each general election year, voting in-dicator for each general election year, and county code. Boosted Tree model (with a misclassification rate of 0.22) identified zip code, age, and voter status are key factors that influence voter participation. Based on voter eligibility and total vote counts in each general election held between 2014 and 2020, voters were classi-fied into the following profile categories: always-voted (participated in all elec-tions), increasing-in-voting (participated in recent elections but not in the past elections), intermittent-in-voting (participated in some elections but not all), de-creasing-in-voting (participated past elections but not recently), never-voted (didn’t participate in the elections), and not-eligible (registered but under 18 years of age). Voter profile counts information was merged with Census demo-graphic information at the zip code level. To find insights into the voter profiles, we created Tableau dashboards to view voter profiles, voting methods, and the effect of census variables on voter turnout at the zip code level. We hope this dashboard helps organizations like the League of Women Voters of Florida target their voter participation and engagement activities at the zip code level.

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Finding Insights in Florida Voter Participation

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Research Team
Dan Richard
Associate Professor of
Psychology,
UNF
FL-DSSG Co-Director
Adhithyan Rangarajan
Business Analytics and
Information Systems,
Master of Science
University of South Florida (USF)
Tampa, FL
2022 FL-DSSG Intern
Thomas Hunt
Mathematical Science - Statistics,
Master of Science
UNF
2021 FL-DSSG Intern
Leah Nash
Executive Director
League of Women Voters of Florida
Orlando, FL
Joyner Johnson
Computing and Information Sciences
Master of Science
University of North Florida (UNF)
2021 FL-DSSG Intern
Payal Agarwal
Computer Science
Master of Science
UNF
2021 FL-DSSG Intern
Mahmoud Elbatouty
Data Science
Bachelor of Science
UNF
2022 FL-DSSG Intern
Dr. Karthikeyan Umapathy
Associate Professor of Computing,
University of North Florida (UNF)
Jacksonville, FL
FL-DSSG Co-Director
(Presenter)

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Gaining actionable
insights from data
Helping Public Sector
and Nonprofit
Organizations make
data-driven decisions
Training data scientists
with social conscious
Florida Data Science for Social Good (FL-DSSG)
“Social Trustees of
Knowledge”

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Summer
internship
program open to
all UNF students
(undergraduate
and graduate)
Social Sciences:
17
UNF: 30
Masters: 28
Duval: 16
Nassau: 2
Orange: 2
Palm Beach: 1
44 21
2017
Florida Data
Science for Social
Good
(FL-DSSG)

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FL-DSSG Project
League of Women Voters of Florida – Dashboard for
Interacting with Florida Voter Profile Classifications
Research and data science solution development was conducted
during summer of 2021 and 2022

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League of Women Voters of Florida (LWVFL)
Nonpartisan organization
encouraging informed and
active participation in
government
100+
Years of service
empowering
voters
29 Florida chapters
25+
Pieces of legislation
passed in 2021 relating
to key issues

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From Wicked Problem to Opportunity
02
03
Voter Access and
Participation
Voters keeping registration
active and continuously
participating
Data Analysis
Manipulating and extracting
data points, allowing for
analysis and visualizations,
such as dashboards
Data Access
Public data available
regarding voting history,
voter registration, and
Census
Improvement
Create a plan of action to
address the problem
04
01

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Project Focus
Analyze voter
registration and
turnout
Mapping results at the
zip code level
Empower Florida
chapters in
decision-making

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Methodology
Data science process – OSEMN framework

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Raw Data Source
Voter Registration
• 67 county files
• Rows– ranges from
30K-2M
• Columns – 38
• Name
• Voter ID
• Gender
• Race
• DOB
etc.
Voter History
• 67 county files
• Election info [2006-
2021]
• Rows – ranges from
100k-10M
• Columns – 5
• County Code
• Voter ID
• Election Date
• Election Type
• Voting Method
Census
• 8 files
• Available data from 2016,
2018, and 2020 Census
updates
• Rows - 983 Florida zip
codes
• Information on:
Children, Demographics,
Education, Foreign,
Household Type, Income,
Sex/Age, and Work
Status
 Voting files received from Dr. Mike Binder, UNF Political Science Department
 Census files obtained from census.gov

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Data Preparation -
Removed sensitive
information
• Voter’s name
• Districting
• Mailing Address etc.
Removed publicly
exempted records
Extracted important
variables for future
merging
• Voter ID
• County Code
• Residential Zip
code etc.
Data Cleaning Stage 1 Data Cleaning Stage 2 Data Extraction
Voter Registration

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Data Preparation - Voter History
Data
Cleaning
Remove sensitive PI,
public exemptions,
perform feature
engineering
Data
Filtration
Based on federal
general elections
held in 2014 -
2020
Data
Extraction
Identified voting
methods used by
each voter for
general elections
Generate
Flat Files
Created flat files
using python
pandas to
aggregate voting
method at zip
code level.

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Voter Profile Generation
Classifying the Voter IDs based on the eligibility and total vote counts on each
general election held between 2014 – 2020
Profile Code Voter Profile Description
1 Always Eligible voters participated in all general elections
2 Increasing
Eligible voters participated recent in general elections but
not in the past elections
3 Intermittent Eligible voters participated in some elections but not all
4 Decreasing
Eligible voters participated past general elections but not
recently
5 Never Voted Eligible voters didn’t participate in the elections
6 Not Eligible Registered but under 18 years of age

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Merge Voter Registration + Voter History
Generate Voter Profiles

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Frequency Table Voter Profile
Rural

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Tallahassee MSA
32304
Miami-Ft. Lauderdale-
Pompano Beach MSA
33411
33311
33023
33024
33025
Orlando-Kissimmee-
Sanford MSA
34787
Tampa-St. Petersburg-
Clearwater MSA
33578
33647
34668
Metropolitan Statistical Areas – Voter Profile Analysis

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Metropolitan Area Comparison – Boosted Tree
Orlando – Kissimmee-
Sanford
34%
16%
15%
16%
Zip Code
2018 Vote Type
Voter Status
Age
30%
14%
18%
15%
Learning Rate: 0.1
Tampa-St. Petersburg-
Clearwater
Misclassification Rate: 0.22
Learning Rate: 0.1
Misclassification Rate: 0.22
Predict:
2020 Participation
Inputs:
2012 to 2018 voter
registration and history data
40%
8%
10%
30%
Miami – Ft. Lauderdale –
Pompano Beach
Learning Rate: 0.1
Misclassification Rate: 0.23
Over 75%
Column Contributions

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Registered Voters
Age 18 to 40
Miami – Ft. Lauderdale
– Pompano Beach MSA
33186
33157
33033
33311
33024
33025

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Data Preparation - Census
Data Cleaning 01
Removing zip codes
from Census that are
PO Boxes,
Correctional Facilities,
or Military Housing
Data
Extraction
02
Data Filtration 03
Data
Merging 04
Extracting necessary
variables [citizen
population, age %,
race %, etc.] Filtering out zip codes with
low total population counts
(<200)
Merging all the variable
files collected by zip code
using python pandas
framework

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Merge Voter Registration + Voter History
Generate Voter Profiles
Merge with
Census Data
Dashboard 1
Voter Profile Analysis
Observe voting increasing and
decreasing patterns across
demographics
Dashboard 2
Voting Method Analysis
Observe voting methods in 2020
Dashboard 3
Census Analysis
Observe voting percentage and
socio-demographic dimensions

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Dashboard 1 – Voter
Profile Analysis
Main Use Case-
Understand Voter
Profile behavior at the
Zip code level.
Dashboard 2 – Voting
Method Analysis
Main Use Case-
Understand Voting
Method behavior at the
Zip code level.
Dashboard 3 – Effect
of Census Socio-
Economic factors on
Voting Percentage
Main Use Case-
Understand effect of
Census variables on
Voter Turnout.
TABLEAU
DASHBOARDS
https://tabsoft.co/3fPgjAe
Dashboards can be accessed at:

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Target specific zip codes, based on
filtering selections in dashboards
Conduct data collection about
local chapter activities
FL local chapters to collaborate around
mutual goals
Consider statewide
planning
Empowering
LWVFL

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