The document summarizes a 2021 Florida Data Science for Social Good Big Reveal event. It describes three projects completed by interns: 1) analyzing emergency department utilization data from Barnabas Center to identify high utilizers and estimate cost savings from health services, 2) using Jewish Family and Community Services counseling scheduling data to identify trends, and 3) examining Florida voter participation data from the League of Women Voters of Florida to find insights. It also provides an in-depth overview of the Barnabas Center project, which found that establishing patients at the Center was associated with a 10-20% reduction in emergency department visits.
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2021 Florida Data Science for Social Good Big Reveal
1. 2021 Florida Data Science for Social Good Big Reveal
August 17, 2021
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Data Science Meets Community
Impact
2. 2021 Florida Data Science for Social Good Big Reveal
August 17, 2021
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FL-DSSG Team
Program Directors
Dr. Dan Richard
Associate Professor of Psychology,
University of North Florida (UNF)
Dr. Karthikeyan Umapathy
Associate Professor of Computing,
Distinguished FIS Professor,
University of North Florida (UNF)
Advisory Board Members
Arri Landsman-Roos
Vice President of Decision Science
Jacksonville Jaguars
Robert Marsh
Chief Technology Officer
NLP Logix
Candace Dorn
Cybersecurity Analytics
JEA
3. 2021 Florida Data Science for Social Good Big Reveal
August 17, 2021
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Chad Gardner
Data Analyst,
NLP Logix
Jay Lewis
Digital Insights &
Analytics Manager,
TIAA Bank
Victor C. Li
Advanced Analytics Developer,
Jacksonville Jaguars
David Garfunkel
President
LIFT JAX
Industry Sherpas
Andrew Pantazi
Founding Editor at The
Tributary: A Northeast Florida
Journalism Collective
Laurel Wainwright
Environmental Services,
JEA
James Healy
Actuarial Analyst / Data Analyst,
JEA
4. 2021 Florida Data Science for Social Good Big Reveal
August 17, 2021
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Dr. Haiyan Huang
Computer Information Systems
Flagler College
Dr. Beyza Aslan
Mathematics & Statistics, UNF
Dr. Gordon Ratika
Anthropology, UNF
Faculty Leads
Dr. Xudong Liu
Computing, UNF
Dr. Amanda Pascale
Higher Education Administration,
UNF
Dr. Sandeep Reddivari
Computing, UNF
Dr. Michelle DeDeo
Mathematics & Statistics, UNF
Special thanks to Dr. Mike Binder (Political
Science) and Dr. Hana Hamadi (Health
Administration)
5. 2021 Florida Data Science for Social Good Big Reveal
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2021 FL-DSSG Interns
Payal Agarwal
Computer Science,
Master of Science,
UNF
Thomas Hunt
Mathematical Science - Statistics,
Master of Science Student,
UNF
Joyner Johnson
Computing and Information Sciences
Master of Science Student,
UNF
Madisen Reasonover
Psychological Sciences,
Master of Science Student,
UNF
Lindsay Regan
Psychology,
Bachelor of Science Student
UNF
Wanyue Xiao
Computer Science,
Master of Science Student,
Brandeis University
Mark Zamani
Biostatistics,
Doctor of Philosophy Student,
FSU
6. 2021 Florida Data Science for Social Good Big Reveal
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Summer
internship
program open to
all UNF students
(undergraduate
and graduate)
Social Sciences:
15
UNF: 28
Masters: 24
Duval: 14
Nassau: 2
Orange: 1
Palm Beach: 1
38 18
2017
Florida Data
Science for Social
Good
(FL-DSSG)
7. 2021 Florida Data Science for Social Good Big Reveal
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DSSG Project
during Academic
Semesters
30-Year Retrospective
Study
Generational Impact of the Affordable
Housing Program on Partner Families
Dr. Tes Tuason
UNF Public Health
Dr. Emily Maiden
Public Administration
Dr. Richard Buttimer
Economics
Anita Fuglestad
Psychology
Athlene Jones & Lital Israel
Computing
Jacob Perez, Kelly Perniciaro, &
Darlene Ramirez
Public Health
8. 2021 Florida Data Science for Social Good Big Reveal
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2021 FL-DSSG Summer Internship Projects
1. Barnabas Center - Health Service Impacts on Emergency Department Utilization
2. Jewish Family and Community Services - Identifying Trends in Patient
Scheduling for Counseling Services
3. League of Women Voters of Florida – Finding Insights in Florida Voter
Participation
9. 2021 Florida Data Science for Social Good Big Reveal
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Fuel of 21st
Century
Problem of
Demand & Supply
A Lucrative Career
Data Science is
Future
Data Science is
Changing the World
Data Science is Hard!
Why Learn
Data Science?
Source: https://data-flair.training/blogs/why-learn-data-science/
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Identify a Nonprofit or Public
sector organization with a
”Wicked Problem”
Gather Data and
Formulate a Plan
Analyze the
Data
Improve Decision
Making Process
for the Client
Data Science for Social Good (DSSG)
DSSG concept formed and started at the University of Chicago in 2013.
11. 2021 Florida Data Science for Social Good Big Reveal
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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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Other DSSG Programs
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UNF College of Arts & Sciences
FIS Computing Distinguished Professorship Award
Jaguars Foundation
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Deirdre Conner
Nonprofit Center Senior Director
Dr. Sherif Elfayoumy
UNF School of Computing Director
Dr. Lori Lange
UNF Department of Psychology Chair
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Dr. Karen Patterson
UNF Provost
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2021 FL-DSSG Projects
1. Barnabas Center - Health Service Impacts on Emergency Department Utilization
2. Jewish Family and Community Services - Identifying Trends in Patient
Scheduling for Counseling Services
3. League of Women Voters of Florida – Finding Insights in Florida Voter
Participation
17. 2021 Florida Data Science for Social Good Big Reveal
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Health Service Impacts on
Emergency Department Utilization
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Wicked Problem: Emergency Department (ED)
Utilization
$27.5b
Southern hospitals:
Aggregate ED visit
costs, 2017
144.8m
Nationwide ED visits
$76.3b
Nationwide aggregate ED
visit costs, 2017
Source: Healthcare Cost and Utilization Project (HCUP), 2017
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Access to Healthcare
• In 2017, Nassau County identified as a Health
Professional Shortage Area (HPSA) due to lack
of primary care services (Health Resources and
Services Administration, 2017)
Sources: Nassau County Community Health Status Assessment, 2018
Hilliard
Yulee
Callahan
Healthcare Facilities
Census Pop. 2010
0 - 4000
40001 - 6500
6501 - 9500
9501 - 14437
Health Department
Hospital
I-95
City Limit
H
H
Fernandina Beach
• Nassau County has significantly fewer
Licensed Physicians compared to FL
133.1
274.6
0 50 100 150 200 250 300
NASSAU
FLORIDA
Licensed Physicians (per 100,000)
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Poverty Rates in Nassau County
Sources: Florida Department of Health, 2021; Nassau County, FL ALICE Report,
2020; Office of Economic and Demographic Research, 2021
Unemployment
Rate (2019)
Nassau Florida
5.9% 3.3%
Households
Receiving Cash
Public Assistance
(2019)
Nassau Florida
10.1% 14.1%
Population
Below Poverty
Level
(2019)
Nassau Florida
10.5% 14%
ALICE
Households
(2018)
Nassau
Florid
a
26% 33%
45%
uninsured!
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Dental
Care
Food
Assistance
Crisis
Assistance
Retail
Store
Funding
Barnabas Center Services
Services targeted towards
vulnerable populations:
- Uninsured
- Below Federal Poverty Line
Partner
with
Baptist
Primary
Health
Care
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Project Goals
Identify high
utilizers of the ED
Determine % reduction in
ED visits for established
Barnabas patients
Compare ED visit rates with
non-Barnabas patients
Recommend
improvements to health
service utilization
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Data Received
- ED visit data 2014-
2020
- Barnabas + non-
Barnabas patients
- ICD codes
- 12,000 rows
01
- All visits 2014-2020
- Clinic visit dates
- Encounter types
- Demographic
information
- 25,000 rows
02
- Clinic visit dates
- Zip Code
- Demographic
information
- Some duplicates with
office visit file
- 3,300 rows
03
Standardized patient identifier across all 3 data types
Baptist ED Barnabas
Clinic Visits
New Patient
Visits
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Nassau County
Duval County
Hilliard
Jacksonville
Callahan
Yulee
Fernandina Beach
Jacksonville Beach
Middleburg
Hilliard
Jacksonville
Callahan
Yulee
Fernandina Beach
Jacksonville Beach
Middleburg
Barnabas Weaver Center
Barnabas
Satellite Clinic
Barnabas
Dental Clinic
Hilliard
Jacksonville
Callahan
Yulee
Fernandina Beach
Jacksonville Beach
Middleburg
Barnabas Weaver Center
Barnabas
Satellite Clinic
Barnabas
Dental Clinic
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Patient Demographics
Clinic visits ED visits
1256 Barnabas Patients
50 = M age
Key Point
1737 Unique Patient IDs
51 = M age
Key Point
2605 Unique Patient IDs
49 = M age
~59% female
~63% medical fee rate $10
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Undifferentiated
Average ED
Visit Cost
Average Cost
across years to
create cost in
2019 $
Goal:
Undifferentiated
Cost based on
Top 4 ED
Diagnoses, 2016-
2019
Goal: Differentiated
Cost based on ICD
Codes
ED Cost Analysis for Nassau County
Actual Cost
Data
Unavailable
$5,072
Acute upper respiratory
infection, unspecified
Chest pain, unspecified
Other chest pain
Urinary tract infection,
site not specified
$1,561
$6,982
$7,891
$3,855
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ED Cost Savings
5.2 M
8.2 M
5.1 M
6.3 M
6.8 M 6.9 M
7.0 M
2014 2015 2016 2017 2018 2019 2020
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Identify high
utilizers of the ED
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High Utilizer ED Savings
High ED Utilizer
>4 ED visits in a year
Unique Patients
356
$ 10.5 million
2019 $ 1.1 million
2018 $ 1.5 million
2017 $ 2.0 million
2016 $ 1.9 million
2015 $ 1.6 million
2014 $ 1.7 million
2020 $ 0.8 million
Average Age
50
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High Utilizer ED Visit Time Heat Map
Majority of visits in
that 10 AM - 5 PM
timeframe
Significant amount of
weekend visits during
non-Barnabas hours
Least common hours
for visiting were very
early morning or late
night
Barnabas Clinic Hours
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ED Visits Time Heat Map
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Determine % reduction in ED visits
for established Barnabas patients
Compare ED visit rates with
non-Barnabas patients
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Barnabas
Non-Barnabas
Selection
Bias:
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Barnabas vs. Non-Barnabas Patients
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Comorbidity Scores
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Barnabas
Non-Barnabas
Selection
Bias:
Match on
Age
Year of first ED visit
Comorbidity Scores
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Matching Non-Barnabas to Barnabas Patients
Feature
Sampling
Matching on Common
Characteristics
Category Matching
296 Matched Pairs
Logistic Regression
Odds of Showing High
Utilization
Compare ED Visits
One Year Before, During & After
New Patient Date for Barnabas
Low Model Fit
R 2 = .05
Best Predictor:
Year of First ED Visit, 35% Odds
01
Probability of HU 9%
for Non-Barnabas & Barnabas Groups
02
03
04
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Reduction in ED Visits Post New Patient
1.48
1.19
1.81
1.29
Before After Before After
Barnabas
20%
29%
• Approximate 10% reduction in ED
visits when Barnabas Clinic new
patient visits are present
• May represent a lower bound estimate
• Additional time past one year may
show continued benefits
• Additional research needed on patient
characteristics for better matching
criteria
Non-Barnabas
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Recommend
improvements to health
service utilization
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Recommendations to Barnabas Center
01 02 03 04
Modifying Hours Weekends Intake Forms Referrals
Open early on weekdays
to service general ED
utilizers
Open midday on
weekends to service
high ED utilizers
Modify intake forms
to assess
comorbidity risk of
patients
Improve referral
process to better serve
patient needs and
quality of care
Improved health service utilization
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Identifying Trends in Patient Scheduling for
Counseling Services
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Financial
Strain
Patient and
Therapist
Schedules Childcare
Stigma
Criminal
Justice
System
Addiction
Obstacles to Patient Care
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v
v
Jewish Family
and Community
Services (JFCS)
Dupont
Counseling Group
Circle of Security
Parenting Classes
Substance
Abuse
Counseling
Mental Health
Counseling
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Project Focus
JFCS Challenges
Text Mining & Visualization
Investigating and visualizing factors that
influence “No Show” rate
Client’s “No Show” Rate
Searching ways to reduce “No Show” rate
Business Process
Generating a tailored scheduling process
Recommendations
Providing recommendation report with
in-depth research
Billing Methods & Procedures
Consistent billing across programs
Patient Scheduling Process
Seeking measures for consistent
therapy appointments
DSSG Goals
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Data Received
ECR Records
Patient therapy services,
length of service, and
reason for discharge
Caseloads and
Scheduling Statistics
Aggregated information by
therapists
Google
Calendar
9 therapists
July 2017 to July 2021
16,775 appointments
17
Therapist name
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Calendar Data: Unstructured to Structured
D
C
B
Data Merging
Combing individual therapist
appointment information
Text Mining
Using Keywords detection and
co-reference resolution
Tableau Dashboard
Visualizing data to identify trends
Data Issues
• Missing data, errors, and
unstructured data
• Various ways to record status
of appointments
• Inconsistent reporting
mechanism on no-shows and
cancellation
A
Data Extraction
Appointment information
from ICS files
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No-show and Cancellation with Intake Program
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No-show and Cancellation without Intake Program
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No-show and Cancellation by Weekday
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Unsuccessful Discharge Reasons
Count of Services 396
Total Length of
Service
63,252
Avg. Length of
Service
159.7
Count of
Therapists
5
107
37
20
11
3
2
0
20
40
60
80
100
120
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Transforming from Implicit to Explicit Process
Zoom Meetings
JFCS provided overview of
current scheduling process
Extraction of Process Elements
Documenting the process - from referral to
therapist assignment
Review and Refine
Scheduling process was modified and
refined based on JFCS feedback
Identifying Areas of Improvement
Recommendations for scheduling process
efficiencies and consistent data collection
procedures
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Segment of Scheduling Workflow
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Recommendations
Referrals
Records Check
Verify Patient
Particulars
Prior: use Google form for patient intake info
Suggestion: Use Google forms to connect patient ECR records
Allows for quicker verification of patient info by employing simplicity in the Google form search
Prior: search ECR for patient records
Suggestion: Create identification system for patient records
Saves times on records search by insuring no duplicate patients
No-show
process
No-show records
Prior: patient data deleted from calendar upon no-show
Suggestion: Add key phrase to differentiate calendar entries
Allows for pattern recognition in patient attendance
Suggestion: Standardize no-show procedures and data collection
system
Allows for a quick reference on how no-shows work and streamlines the process
No-show
Practice
s
Prior: lack of consistency in no-show reporting
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Finding Insights in Florida Voter Participation
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29
1920
LWV-US founded ~
6 months before
the passage of the
19th Amendment
Florida League
founded
Florida chapters
increasing “participation in
government” plus
education on public issues
Nonpartisan: No political party affiliation
League of Women Voters (LWV)
1939
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League of Women Voters - Florida
Legislative
priorities
centered around
policy issues
Registering returning
citizens: Amendment
4
Voter services
including
registration and
education: Vote
411 text campaign
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From Wicked Problem
Access to voting
An important part
of participation in
government
Access to information
Transformation through
data
Maximizing impact
Prospective voters might not
know the requirements of
keeping their registration or
active status maintained
By using data, the league can
make decisions with more
complete information
With more data open to
the public, our client can
have a larger impact
to Opportunity
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Project Focus
Voter registration and
turnout in Florida
Geographic focus on
voting
Recommendations to increase
voter registration and turnout
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• County code
• Voter ID
Common
Items
Raw Data Source
Data from Florida Division of Elections
2 types of voter data
Based on 67 county files each
Thanks to
Dr. Mike Binder
for sharing data
files
• File size – based on
population [range of file size –
~1.6M]
• Total columns – 38 [Address,
Mailing Address, Gender,
DOB, Last Registration Date,
etc]
Voter Registration
• Election info [2006 - 2020]
• File size – based on voter’s
participation in all elections
[range – ~10M]
• Total columns – 5 [Election
Type , Election Date, History
Code]
Voter History
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Data Preparation -
Removed unwanted
variables
• Voter’s name
• Address
• DOB etc.
Used panda's
framework for splitting
large files
• E.g. :- Miami Dade,
Broward
Extracted non-
identifying variables
• Voter ID
• County Code
• Residential Zip
code etc.
Data Cleaning Splitting Large Files Data Extraction
Voter
Registration
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Data Preparation -Voter
History
Data
Sorting
Organized county
files with unique
voter ID
Data
Filtration
Based on federal
general elections
held in 2012 -
2020
Data
Extraction
Identified voting
types used by
each voter for
general elections
Generate
Flat Files
Used pandas
framework to
generate flat files
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Data Preparation Challenges
Remove
Duplicates
Semi
Duplicates
Recode
Vote Type
Data
Merging
Remove the rows with exact
duplicates
VoterID with misclassified
vote types in same election
Isolated the semi duplicates
and recoded as M
Used left merge operation to
merge voter registration
and voter history files
01 02 03 04
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• General elections data between 2012 – 2020
• 67 files were generated
The columns are:
Voter ID
Registration county code
Residential zip code
Sex
Ethnicity
Age
Precinct coding
District coding
[2012 … 2020] VoteType [see table]
[2012 … 2020] Vote indicator [1 or blank]
[2012 … 2020] County code
Voting History
Code
History Description
Y Voted at Polls
A Voted by Mail
E Voted Early
B Vote-by-Mail Ballot Not Counted
P Provisional Ballot Not Counted
N
Did Not Vote (did not consider by all
counties)
M Misclassified
Data Preparation
- Voter
Registration
+ Voter
History
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Merged Metropolitan/Micropolitan and Rural
Areas
• Used Pandas framework
to combined merged
county files based on
metro and micropolitan
areas
• Total 28 metro/micro and
rural areas encompassing
the entire state
• Adjacent rural areas were
grouped as Panhandle:
Jackson - Liberty and
Panhandle: Madison -
Suwannee Sources: https://www.whitehouse.gov/wp-
content/uploads/2020/03/Bulletin-20-01.pdf
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Voter Profile Generation
• Classifying the VoterID based on the eligibility and total vote counts on each
general election held between 2012 – 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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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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Micropolitan Statistical Areas – Voter Profile Analysis
33040
Key West MSA
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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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Regional Analysis – Rural MSAs
Panhandle: Jackson - Liberty Panhandle: Madison - Suwannee
32428
32060
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Target recently inactive voters
based on voter registration
data
Conduct data collection
about local chapter
activities
Targeted campaigns for
citizens below 40 years of
age
Start chapters in
micropolitan and
rural areas of the
state
Recommendations
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What is the project’s big reveal from your perspective?
What will be the impact of the project for your organization?
What are the next steps for your organization?
1
2
3
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Barnabas Center
Jewish Family and Community
Services
Eric Forestal Bill Nolan
Chief Operations Officer Chief Operations Officer
League of Women Voters of Florida
Leah Nash
Executive Director
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FL-DSSG
is seeking
sponsorships
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August 17, 2021
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Getting Ready for FL-DSSG 2022
Submit Proposal
Application in
January 2022
Identify Data
Sources and
associated
variables
Get Commitment
from everyone
involved
Identify a Wicked
Problem and its
Social Goodness
79. 2021 Florida Data Science for Social Good Big Reveal
August 17, 2021
79