Recidivism Rates In Iowa

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Iowa’s Convicted Felons and Recidivism Rates:

How can the State Address the Growing Issue?

Report Author: Thomas Fite

Assignment: Individual Project – Step 5

Class: MSC 622-01

Instructor: Dr. Laird Burns

Date Submitted: 12/03/2020

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Executive Summary

I conducted this analysis to analyze trends and patterns in the criminal statistics and

recidivism rates in Iowa from 2010 to 2015. There are several goals for this project, which

include: identifying which demographics in the state commit the most criminal offenses by sub-

type, determine the average length of time it takes a convicted felon to return to prison, identify

which judicial district or judicial districts account for the highest number of offenders by

different demographics and finally, identify which judicial district or districts in Iowa have the

highest recidivism rates. The primary data set was published by the Iowa Department of

Corrections and retrieved from the Iowa Data Portal. Due to formatting issues, spelling mistakes,

and duplicate values (which initially led to problems when importing data into Tableau), I had to

clean the data in excel. I built a second data source to populate the longitude and latitude

coordinates for each of Iowa’s ninety-nine counties.

For my analysis method, I solely used Tableau to conduct descriptive, predictive, and

prescriptive analytics across several different factors allowing me to gain a more in-depth insight

into the problem at hand. I used descriptive analytics for most of my data to precisely pinpoint

the primary offending group by various demographics and judicial districts. Additionally, due to

the volume of data, I used averages to determine the number of days offenders stay out of prison.

I included several different trend lines and interactive maps highlighting the current trends for

my predictive and prescriptive analytics, allowing me to draw relevant conclusions on what

could happen in Iowa.

Several trends and patterns have emerged from the data set and which I explain in detail

in this report; the following is a snapshot of these trends and patterns. First, this report finds that

90% of all convicted felons in the state of Iowa are male. Next, the report found that one-third of

all convicted felons are between 25 and 34. Third, White Non-Hispanics account for two-thirds

of all offenders. Moreover, Black Non-Hispanics are disproportionally incarcerated based on

population size. The first pattern observed in my analysis is that several of the state’s

rehabilitation programs are actively reducing the average number of days it takes a felon to

return to prison due to a new criminal conviction or a violation of probation or parole. The

second pattern detected is that judicial district 5, the state’s capital region, accounts for the most

convicted felons, most of all criminal offenses committed from 2010 to 2015, and having the

highest recidivism rate. The recommendations discussed in this report include reviewing the

state’s current criminal justice policies, evaluating the state’s rehabilitation programs, and assess

and address the various factors, such as education, income level, and employment, across the

judicial districts that play a role in higher recidivism rates.

Upon completing my report and my analysis, I have found several limitations in the data

that may influence the outcome. These limitations include prior criminal history, employment

status, living condition, income level, educational attainment, and mental capacity. State officials

may need to conduct further analysis to see the effects of these additional metrics on Iowa’s

recidivism rates.

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Table of Contents

Executive Summary ………………………………………………………………………………………………… ii

Introduction ………………………………………………………………………………………………………….. 1

Convicted Felons Across Iowa …………………………………………………………………………………… 2

Recidivism Rates …………………………………………………………………………………………………….. 3

Target Population by Offense Sub-Type (i.e. Participant in a Rehabilitation Program) ………… 3

Number of Convicted Felons Across Iowa by Original Offense Sub-Type ……………………………. 4

Recidivism by New Offense Sub-Type …………………………………………………………………………. 5

Averages Days Felons took to Return to Prison ……………………………………………………………. 5

Average Days Felons took to Return to Prison by Offense Sub-Type …………………………………. 6

Iowa Judicial Districts and Total Percentage of Felons …………………………………………………… 6

Change in Number of Felons Across the Districts ………………………………………………………….. 7

Change in Original Offense Sub-Type by Districts …………………………………………………………. 7

Change in Recidivism Rates by Districts …………………………..………………………………………….. 7

Conclusion and Recommendations …………………………………………………………………………….. 8

References…………………………………………………………………………………………………………… 11

Appendix A …………………………………………………………………………………………………………. A-1

Criminal Statistics in Iowa: Evaluating Sex, Age, Race/Ethnicity and Recidivism Rates Across Iowa…

……………………………………………………………………………………………………………………………….. A-1

Convicted Felons Across Iowa ……………………………………………………………………………………….. A-2

Recidivism Rates ………………………………………………………………………………………………………… A-2

Target Population by Offense Sub-Type (i.e. Participant in a Rehabilitation Program) ……………… A-3

Number of Convicted Felons Across Iowa by Original Offense Sub-Type ……………………………….. A-3

Recidivism by New Offense Sub-Type …………………………………………………………………………….. A-4

Averages Days Felons took to Return to Prison ………………………………………………………………… A-4

Average Days Felons took to Return to Prison by Offense Sub-Type …………………………………….. A-5

Iowa Judicial Districts and Total Percentage of Felons ……………………………………………………….. A-5

Change in Number of Felons Across the Districts ………………………………………………………………. A-6

Change in Original Offense Sub-Type by Districts ……………………………………………………………… A-6

Change in Recidivism Rates by Districts ………………………………………………………………………….. A-7

Wrap-Up and Recommendations …………………………………………………………………………………… A-7

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Introduction

Growing up in a small rural Iowa town, my parents taught me to obey the rules and

respect those who enforce them. And since our town was small, it was not uncommon to be on a

first-name basis with our local police or county sheriff. Because of these close relationships, I

developed a sense of passion and interest in all types of law enforcement activities. This passion

grew throughout my formative years when I was able to job shadow several different cops. My

brother has been in and out of the Iowa correctional system for the last 15 years due to parole or

probation violations. This experience has personally affected my parents more than me, who

consistently questioned their parenting abilities and where they went wrong in raising my

brother, especially given that I am the opposite of him. While I am at a point in my life where I

don’t believe he will change his ways, I have remained interested in understanding why he, along

with other felons, commit crimes and consistently return to prison.

The primary audience for this report is me. First, the analysis was completed as part of a

class project to demonstrate acquired skills throughout the semester. Second, by conducting this

analysis, I was able to gain a greater understanding of the tendencies of felons, the types of

offenses committed, which judicial districts have the higher preponderance of criminals, which

crimes have higher recidivism rates, and what steps the state is taking or should take to lower the

recidivism rates. Three other audiences may have an interest in these analytics. The first is my

parents, who, like me, want to understand why convicted felons act the way they do and why

certain crimes, such as my brothers, have a higher recidivism rate. Second, public officials with

the state of Iowa. Public officials may take this data and conduct additional analysis to

understand the prison system’s problems and implement policy reform or improve rehabilitation

programs. A final audience could be anyone who may have an interest in crime statistics or

recidivism rates. They should be able to take this data and the associated visualizations and draft

their conclusions, some of which may be different than what I have formed throughout this

report.

The data set used was published by the Iowa Department of Corrections and retrieved

from the Iowa Data Portal. The data set contained several different factors that included: Year of

Arrest, Year of Recidivism, Age at Release, Judicial District, Release Type, Race/Ethnicity, Sex,

Original Offense Classification, Return to Prison Status, New Offense Classification, and Target

Population. I manually cleaned the original data set through Excel due to formatting issues,

spelling mistakes, and duplicate values. Additionally, I manually created a second data set in

Excel with the following columns: Judicial District, City, Latitude, Longitude, County, and FIPS

[Federal Information Processing Standard, which uniquely identifies U.S. counties]. I created the

additional sheet to build a map in Tableau to establish the judicial districts in Iowa, giving a

more holistic view of the problem. Because I had two different datasheets, I had to join the two

in Tableau to make the data work for my project.

My sole analytics approach was Tableau because of the advanced visualizations it offers.

With Tableau, I can briefly provide several different viewpoints through unique visualizations to

tell the story. My project consists of twenty-five individual sheets, fifteen dashboards, and one

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comprehensive storyboard. Throughout my project, I have color-coded the data to ensure the

visualizations stand out from one another, included tooltips, highlight features, filtering options,

and advanced animations, to allow the individual to interact with the data and form relevant and

informed conclusions.

The rest of this report focuses on the analysis I conducted and the methods I used to draw

the conclusions I’ve made. Because my data set ranges from 2010 to 2015, I primarily used

descriptive analytics, explicitly analyzing the number of felons by age, sex, race/ethnicity,

offense type, average days return to prison and the change in percentage of offenders across the

districts using these same metrics. I used predictive and prescriptive analytics to draw additional

conclusions using different trend lines and interactive maps, highlighting what could happen in

Iowa if the trends continue and what should be done by the public officials to reverse the trends.

I will conclude this report by providing my recommendations, grounded in additional research,

to best tackle this problem and reduce Iowa’s growing recidivism rates.

Convicted Felons Across Iowa

This first visualization begins laying the groundwork for my entire project by

descriptively analyzing the total number of convicted felons across the state from 2010 to 2015.

Specifically, the charts break out individual demographics such as sex, age, and race/ethnicity.

By isolating specific demographics, I can precisely identify which groups are more likely to

commit felony related crimes, allowing me to begin to identify potential trends and apply the

results in follow-on visualizations and charts.

Because this visualization includes different demographics from across the state, I found

it essential to incorporate the 2010 U.S. Census results in this report. According to the 2010 U.S.

Census, Iowa had a total population of 3,046,355 million people, split equally between males and

females (CensusViewer, 2011-2012). My analysis results in the first chart [Top Left] indicating

that males account for 87.16% of the total prison population. The second chart, presented in this

visualization [Top Right], analyzes the differences in age groups at the time of release. From this

chart, we can see that felons aged 25-34 account for 9,554 out of 26,020 offenders, or 36.72% of

the population. The next closest age group is 35-44, who account for an additional 6,224, or

23.92% of the population. Because the U.S. Census uses more broad categories for age, it is not

easy to compare the census data results to this chart’s results. The final chart presented in this

visualization looks at the differences in Race/Ethnicity of convicted felons [Bottom]. According

to the 2010 Census, White Non-Hispanics accounted for 91.31% of the total population, with

Black Non-Hispanics accounting for the next highest at 2.93% (CensusViewer, 2011-2012). My

analysis shows that 67.61% of the prison population was White Non-Hispanic and Black Non-

Hispanics account for 23.52%. Based on the population data from the U.S. Census and these

results, it appears that Black Non-Hispanics are disproportionally represented in the criminal

statistics and have a higher frequency of arrests.

What is intuitive about this visualization is the highlighting and filtering of different

demographics to drill down into the data set, allowing the individual to gain clarity across

specific dimensions. Furthermore, I have added a tooltip to the visualization to associate a

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physical number to the percentage located in the box. Many of these same features have been

applied to successive dashboards to allow the individual to use specific constraints for detailed

results.

Recidivism Rates

Continuing my descriptive analytics, this visualization examines the total number of

felons returning to prison, or recidivism rate, by sex, age, and race/ethnicity. The Association of

State Correctional Administrators (ASCA) defines recidivism “as the tendency of a convicted

criminal to relapse, resulting in re-arrest, reconviction or return to prison with or without a new

sentence during a three-year period following the prisoner’s release (National Institute of Justice,

2020). According to a 2015 report from the U.S. Sentencing Commission, recidivism rates across

the nation have steadily declined since 2005, averaging 33.7% over the last ten years (Federal

Bureau of Prisons, 2016). Furthermore, while the national average for recidivism is on the

decline, Iowa’s rate has been steadily rising since a low in 2014, when recidivism rates were

29.8% (Iowa Department of Corrections, 2016). The results of this visualization will help me

form relevant conclusions on the recidivism rates for different demographics, which will

continue to allow me to identify trends and apply these results to other visualizations within the

project.

As previously mentioned, this visualization focuses on the recidivism rates of convicted

felons using different demographics. The individual articulating the chart can select either “no”

or “yes” on return to prison. By selecting “no,” the individual will receive detailed results of

felons who did not meet the recidivism threshold by sex, age, and race/ethnicity. By highlighting

“yes,” we see that males are eight times more likely to re-offend than females in the first chart.

Furthermore, we can use the drop-down filter for gender and form more relevant conclusions

about the population regarding age and race/ethnicity. This filtering indicates that White Non-

Hispanic males aged 25 to 34 have a recidivism rate of 69%. White Non-Hispanic females have

a higher recidivism rate of 75.03%. The next highest recidivism rate is Black Non-Hispanics.

Where male felons return to prison 24.17% of the time and females return to prison 18.42%.

These results are starkly different than what is being observed and reported at the national level.

In which, Black/African American males aged 35-39 were 86.9% more likely to re-offend,

followed by Hispanic males at 64.6% (Gramlich, 2020).

Target Population by Offense Sub-Type (i.e. Participant in a Rehabilitation Program)

Continuing to use descriptive analytics, this visualization identifies whether a felon was

part of the state’s target population. The term target population refers to rehabilitation programs

offered by the state to reduce the recidivism rate. Like other rehabilitation programs across the

U.S., Iowa’s programs include substance abuse counseling, specialized sessions for sexually

violent criminals, educational counseling and classes, life skills training, and learning new trades

to integrate back into society (Iowa Department of Corrections, 2016). The purpose of this

visualization allows me to form an educated opinion on what rehabilitation programs are

working and which ones are not as effective. Additionally, I can use the “not targeted”

population results to recommend whether a rehabilitation program needs additional resources or

complete reform.

The descriptive analytics in this visualization has three different filter options, age at

release, offense sub-type, and race/ethnicity, which allows me to form more appropriate

conclusions about the population. I choose the broader category of offense sub-type because it

gives more detailed results of a specific criminal offense. This will allow me to infer which

rehabilitation program an offender may have undergone before his/her release. It is essential to

highlight that Iowa does not require every felon to be a part of a rehabilitation program.

Criminals may voluntarily request assistance or can be mandated through sentencing (Iowa

Department of Corrections, 2016).

Focusing primarily on the targeted population, my first conclusion that I have arrived at

is for males aged 25-34. The results indicate that rehabilitation programs for males operating

under the influence (OWI), committing assault, vandalism, and murder/manslaughter related

offenses have the most impact. Whereas for females in the same age bracket, rehabilitation

programs for assault, theft, and drug-related (distribution) offenses have the most significant

impact. Conversely, the data suggests that rehabilitation programs for males committing

burglary, forgery/fraud, sex crimes, and trafficking are not working as intended. And for females,

rehabilitation programs for burglary, drug possession, trafficking, and vandalism are insufficient.

I made my conclusions based on a side by side comparison of the two bar charts and the two

measures’ variance. After running through the different filter options, it appears that