Update Note

The majority of code and analysis in this post was originally written back in mid-2018 (in this notebook). I’ve consolidated some cells, automated data retrieval, and added cell labels to make things render nicely, but otherwise I’ve left the work as-is. Compared to my current work products, this old code is very messy and unpolished, but like Eric Ma (creator of the networkx graph data analysis package), I believe in showing newer data analysts and scientists I mentor that no one in this field started off with mastery of git, pandas, bash, etc, and that everyone who lasts loves to keep learning and improving.

After I integrate these old posts into this blog, I’ll write an EDA post that starts from scratch using up-to-date data.

1 Criminal Justice and Corrections Data EDA, Part 2

1.1 Table of Contents

Exploration in Criminal Justice and Corrections Data
Datasets
Total Imprisonment Rates
3a. Visualization Pitfall
3b. Population Dataset
3c. Visualization Breakthrough, Observations, and interactive maps
State Imprisonment Distributions by Gender
4a. Static Scatter Plots with Distributions
4b. Interactive Scatter Plots
Summary

2 Datasets

As in part 1, I’m exploring the most recent data from the Bureau of Justice Statistics’ Prisoner Series datasets.

3 Imprisonment Counts and Rates by State (Table p16t02)

In the previous post, Exploratory Data Analysis Part 1, we saw that the vast majority of people in prisons are in state prisons, but we didn’t discover anything about imprisonment rates in individual states. One of my favorite tools for examining geographical data is the choropleth map (which you may recognize from any recent election night). In this post, I use the Bokeh python package to generate some state-level choropleths to show rates of imprisonment and how they vary from state to state.

Scroll to the bottom if you want to jump straight to the maps.

Imports, styling, path definitions, and util funcs

import os
from urllib.request import urlretrieve

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from IPython.core.display import display, HTML
%matplotlib inline

# Notebook Styling
pd.options.display.float_format = lambda x: "%.5f" % x
pd.options.display.max_columns = None
plt.rcParams['figure.figsize'] = 10,10

DATA_DIR_PATH = os.path.join("..", "010_crime_and_prisons_p1", 'data')
PRISON_DATA_DIR = os.path.join(DATA_DIR_PATH, 'prison')
POP_DATA_DIR = os.path.join(DATA_DIR_PATH, "population")
os.makedirs(POP_DATA_DIR, exist_ok=True)

# Importing modules from a visualization package.
from bokeh.sampledata.us_states import data as states
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import HoverTool, ColumnDataSource
from bokeh.models import LinearColorMapper, ColorBar, BasicTicker

try:
    del states["HI"]
    del states["AK"]
except KeyError:
    pass

# This dictionary maps State names to State Codes
def get_state_code():
    state_code = {
        'United States': 'US',
        'Alabama': 'AL',
        'Alaska': 'AK',
        'American Samoa': 'AS',
        'Arizona': 'AZ',
        'Arkansas': 'AR',
        'California': 'CA',
        'Colorado': 'CO',
        'Connecticut': 'CT',
        'Delaware': 'DE',
        'District of Columbia': 'DC',
        'Florida': 'FL',
        'Georgia': 'GA',
        'Guam': 'GU',
        'Hawaii': 'HI',
        'Idaho': 'ID',
        'Illinois': 'IL',
        'Indiana': 'IN',
        'Iowa': 'IA',
        'Kansas': 'KS',
        'Kentucky': 'KY',
        'Louisiana': 'LA',
        'Maine': 'ME',
        'Maryland': 'MD',
        'Massachusetts': 'MA',
        'Michigan': 'MI',
        'Minnesota': 'MN',
        'Mississippi': 'MS',
        'Missouri': 'MO',
        'Montana': 'MT',
        'National': 'NA',
        'Nebraska': 'NE',
        'Nevada': 'NV',
        'New Hampshire': 'NH',
        'New Jersey': 'NJ',
        'New Mexico': 'NM',
        'New York': 'NY',
        'North Carolina': 'NC',
        'North Dakota': 'ND',
        'Northern Mariana Islands': 'MP',
        'Ohio': 'OH',
        'Oklahoma': 'OK',
        'Oregon': 'OR',
        'Pennsylvania': 'PA',
        'Puerto Rico': 'PR',
        'Rhode Island': 'RI',
        'South Carolina': 'SC',
        'South Dakota': 'SD',
        'Tennessee': 'TN',
        'Texas': 'TX',
        'Utah': 'UT',
        'Vermont': 'VT',
        'Virgin Islands': 'VI',
        'Virginia': 'VA',
        'Washington': 'WA',
        'West Virginia': 'WV',
        'Wisconsin': 'WI',
        'Wyoming': 'WY'
    }
    return state_code

Loading and preprocessing the t02 dataset, Counts by State and Gender

# Breakdown by State and Gender (Table p16t02)
CSV_PATH = os.path.join(PRISON_DATA_DIR, 'p16t02.csv')
state_sex_df = pd.read_csv(CSV_PATH, encoding='latin1',
                           header=[11,12], na_values=':',
                           thousands=r',')
print(f"Initial data format")
display(state_sex_df.head())
state_sex_df.dropna(axis=0, thresh=5, inplace=True)
state_sex_df.dropna(axis=1, thresh=5, inplace=True)
# state_sex_df.dropna(axis=0, inplace=True)
state_sex_df.drop(['Percent change, 20152016', 'Unnamed: 12_level_0',
                   'Unnamed: 14_level_0'],
                  axis=1, inplace=True)
state_sex_df.drop([0,1,2], axis=0, inplace=True)

# Fixing the headers for the dataframe
tmp1 = ['Area']
tmp1.extend(3*[state_sex_df.columns.values[1][0]])
tmp1.extend(3*[state_sex_df.columns.values[4][0]])
tmp2 = [el[1] for el in state_sex_df.columns.values]
tmp2[0] = ''
state_sex_df.columns = pd.MultiIndex.from_arrays([tmp1,tmp2], names=['', ''])
state_sex_df.reset_index(drop=True, inplace=True)

# To clean up the '/_' footnote indicators
fix = lambda x: x.split('/')[0]
state_sex_df['Area'] = state_sex_df['Area'].apply(fix)

# This creates a new column in the DataFrame with the state code for each state
state_code = get_state_code()
map_state_codes = lambda x: state_code[x]
state_sex_df['Code'] = state_sex_df['Area'].apply(map_state_codes)
state_sex_df.drop(['Area'], axis=1, inplace=True)  # drops the row with aggregated values
# This sets the state code as the index
state_sex_df.set_index('Code', inplace=True)

# This fixes the names of the columns (to facilitate merging later)
state_sex_df.columns.set_names([None,'SEX'], inplace=True)
state_sex_df.index.name = 'Code'

print(f"\n\nData format after preprocessing")
display(state_sex_df.head())

Initial data format


Data format after preprocessing

	Unnamed: 0_level_0	Unnamed: 1_level_0	2015	Unnamed: 3_level_0	Unnamed: 4_level_0	Unnamed: 5_level_0	2016	Unnamed: 7_level_0	Unnamed: 8_level_0	Unnamed: 9_level_0	Percent change, 2015–2016	Unnamed: 11_level_0	Unnamed: 12_level_0	Unnamed: 13_level_0	Unnamed: 14_level_0	Unnamed: 15_level_0
	Jurisdiction	Unnamed: 1_level_1	Total	Male	Female	Unnamed: 5_level_1	Total	Male	Female	Unnamed: 9_level_1	Total	Unnamed: 11_level_1	Male	Unnamed: 13_level_1	Female	Unnamed: 15_level_1
0	NaN	U.S. total/a	1526603.00000	1415112.00000	111491.00000	nan	1505397.00000	1393975.00000	111422.00000	nan	-1.40000	%	-1.50000	%	-0.10000	%
1	Federal/b	NaN	196455.00000	183502.00000	12953.00000	nan	189192.00000	176495.00000	12697.00000	nan	-3.70000	%	-3.80000	%	-2.00000	%
2	State/a	NaN	1330148.00000	1231610.00000	98538.00000	nan	1316205.00000	1217480.00000	98725.00000	nan	-1.00000	%	-1.10000	%	0.20000	%
3	NaN	Alabama	30810.00000	28220.00000	2590.00000	nan	28883.00000	26506.00000	2377.00000	nan	-6.30000	NaN	-6.10000	NaN	-8.20000	NaN
4	NaN	Alaska/c	5338.00000	4761.00000	577.00000	nan	4434.00000	4024.00000	410.00000	nan	-16.90000	NaN	-15.50000	NaN	-28.90000	NaN

/home/matt/miniconda3/envs/prisons_post_env/lib/python3.6/site-packages/pandas/core/generic.py:3812: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  new_axis = axis.drop(labels, errors=errors)

	2015			2016
SEX	Total	Male	Female	Total	Male	Female
Code
AL	30810.00000	28220.00000	2590.00000	28883.00000	26506.00000	2377.00000
AK	5338.00000	4761.00000	577.00000	4434.00000	4024.00000	410.00000
AZ	42719.00000	38738.00000	3981.00000	42320.00000	38323.00000	3997.00000
AR	17707.00000	16305.00000	1402.00000	17537.00000	16161.00000	1376.00000
CA	129593.00000	123808.00000	5785.00000	130390.00000	124487.00000	5903.00000

In the above cell, I’ve cleaned up the DataFrame so that the column header now consists of: * column labels: (‘2015’, ‘2016’, ‘Total’, ‘Male’, ‘Female’) * column label names: (’‘, ’SEX’) * index name: (’Code)

Setting variables to help style plots and locate tooltips

# The color levels to use in the map
COLORS = ['#feedde', '#fdd0a2', '#fdae6b', '#fd8d3c', '#f16913', '#d94801', '#8c2d04']
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]

One of the great things about coding in Jupyter Notebooks is that I can prototype things as I go, and then encapsulate that prototype into a function. I built the prototype for my visualization in the cell below, and after I tune the visualization, I can make it into a function and easily generate more visualizations.

Prototyping Bokeh plotting code

state_names = []
prisoner_counts = []
yr = '2015'
sex = 'Male'
df = state_sex_df
min_val = df.loc[:,yr][sex].min()
max_val = df.loc[:,yr][sex].max()
bins = len(COLORS)
TOOLS = "pan,wheel_zoom,reset,hover,save"
for state_code in states:
    try:
        num_imprisoned = df.loc[state_code,yr][sex]
        state_names.append(state_code)
        prisoner_counts.append(num_imprisoned)
    except KeyError:
        state_names.append(' ')
        prisoner_counts.append(0)

color_mapper = LinearColorMapper(palette=COLORS,
                                 low=min(prisoner_counts),
                                 high=max(prisoner_counts))

source = ColumnDataSource(data=dict(
    x=state_xs,
    y=state_ys,
    name=state_names,
    imp_count = prisoner_counts
))

p = figure(title="(Weak Visualization) Number of Imprisoned Males by State (2015)",
           toolbar_location="left",
           plot_width=800, plot_height=450, tools=TOOLS)
p.patches('x', 'y', source=source, 
          fill_color={'field': 'imp_count', 'transform': color_mapper}, fill_alpha=1.0,
          line_color="#884444", line_width=2, line_alpha=0.3)
color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),
                     label_standoff=12, border_line_color=None, location=(0,0))
p.add_layout(color_bar, 'right')
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
        ("State", "@name"),
        ("No. Imprisoned in this State", "@imp_count")
    ]
output_notebook()
show(p)

Loading BokehJS ...

3.1 Visualization Pitfall!

From this map, we see that the most populated states have the most prisoners (you can see the actual number of people in prison by hovering the mouse over a state). That’s approximately what we would expect, but it doesn’t really help us answer questions about the differences in imprisonment rates between States (with different policies on criminal justice). This information is useful for investigating some questions (e.g. Where could prison reform impact the most lives?), but it’s not as useful if we’re investigating the relationship between policies, crime rates, and imprisonment rates. If we want to actually compare imprisonment rates between states, we need to normalize (or scale) the prisoner counts to the population of the state. This requires getting a different data set.

3.1.1 Population Dataset

The US Census tracks and publishes population data, including a data set that tracks estimates of annual state populations (by gender) from 2010 to 2016. I’ll use this data to normalize the raw prisoner counts by state.

(NEW CODE) automating data collection

pop_file_path = os.path.join(POP_DATA_DIR, "sc-est2016-agesex-civ.csv")
url = "https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/state/asrh/sc-est2016-agesex-civ.csv"
if not os.path.isfile(pop_file_path):
    urlretrieve(url=url, filename=pop_file_path)

Loading and preprocessing the Census State and Gender pop. estimates

state_pop_sex_df = pd.read_csv(pop_file_path, encoding='latin1', header=0)
print('\nRaw Population Data Set Format')
display(state_pop_sex_df.head())
state_pop_sex_df = state_pop_sex_df[state_pop_sex_df['SEX'].isin([1,2])]
state_code = get_state_code()
map_state_codes = lambda x: state_code[x]
fix_sex = lambda x: 'Male' if x == 1 else 'Female'
state_pop_sex_df['SEX'] = state_pop_sex_df['SEX'].apply(fix_sex)
state_pop_sex_df.drop(['SUMLEV','REGION','DIVISION','STATE','AGE', 
                       'ESTBASE2010_CIV', 'POPEST2010_CIV', 'POPEST2011_CIV',
                       'POPEST2012_CIV', 'POPEST2013_CIV', 'POPEST2014_CIV'], 
                      axis=1, inplace=True)
state_pop_sex_df.rename({'POPEST2015_CIV':'2015_pop',
                         'POPEST2016_CIV':'2016_pop'},
                        axis=1, inplace=True)
state_pop_sex_df['Code'] = state_pop_sex_df['NAME'].apply(map_state_codes)
state_pop_sex_df = state_pop_sex_df.groupby(['SEX', 'Code']).sum()
state_pop_sex_df = state_pop_sex_df.unstack(0)
print('\n\nPopulation Data Set Format After Reformatting')
state_pop_sex_df.head()


Raw Population Data Set Format


Population Data Set Format After Reformatting

	SUMLEV	NAME	AGE	ESTBASE2010_CIV	POPEST2010_CIV	POPEST2011_CIV	POPEST2012_CIV	POPEST2013_CIV	POPEST2014_CIV	POPEST2015_CIV	POPEST2016_CIV
0	10	United States	0	3944160	3951400	3963239	3926677	3931346	3955374	3975414	3970145
1	10	United States	1	3978090	3957847	3966617	3978101	3943114	3950083	3974980	3995008
2	10	United States	2	4096939	4090856	3971363	3980016	3992752	3959663	3967361	3992154
3	10	United States	3	4119051	4111929	4102483	3982920	3992660	4006960	3974468	3982074
4	10	United States	4	4063186	4077557	4122286	4112795	3994261	4005464	4020276	3987656

	2015_pop		2016_pop
SEX	Female	Male	Female	Male
Code
AK	697258	734122	701214	739176
AL	4998694	4681748	5011344	4687956
AR	3028428	2916226	3039182	2926812
AZ	6854628	6744392	6970496	6853282
CA	39205974	38470174	39466330	38716576

Now that I’ve reformatted the population data to match the format of the state prisoner counts, I want to merge these two DataFrames together so that I can calculate the normalized rates of imprisonment.

Joining state pop. estimates with state inmate counts

state_sex_join_df = state_sex_df.join(state_pop_sex_df)
display(state_sex_join_df.head())

	2015			2016			2015_pop		2016_pop
SEX	Total	Male	Female	Total	Male	Female	Female	Male	Female	Male
Code
AL	30810.00000	28220.00000	2590.00000	28883.00000	26506.00000	2377.00000	4998694	4681748	5011344	4687956
AK	5338.00000	4761.00000	577.00000	4434.00000	4024.00000	410.00000	697258	734122	701214	739176
AZ	42719.00000	38738.00000	3981.00000	42320.00000	38323.00000	3997.00000	6854628	6744392	6970496	6853282
AR	17707.00000	16305.00000	1402.00000	17537.00000	16161.00000	1376.00000	3028428	2916226	3039182	2926812
CA	129593.00000	123808.00000	5785.00000	130390.00000	124487.00000	5903.00000	39205974	38470174	39466330	38716576

Now that I’ve merged the state prisoner counts with population data, I have to divide male prisoner counts by state population (of that gender) and store that information for use later.

Engineering per-capita inmate count features

# This Cell normalizes prisoner counts to be per 100k
#   state population for that year.
yrs = ['2015','2016']
sexes = ['Male','Female']

for yr in yrs:
    for sex in sexes:
        state_sex_join_df.loc[:,(yr, sex + '_rate')] = \
            state_sex_join_df.loc[:,(yr, sex)]\
            .divide(state_sex_join_df.loc[:,(yr + '_pop', sex)]/100000)
display(state_sex_join_df.head(3))

	2015			2016			2015_pop		2016_pop		2015		2016
SEX	Total	Male	Female	Total	Male	Female	Female	Male	Female	Male	Male_rate	Female_rate	Male_rate	Female_rate
Code
AL	30810.00000	28220.00000	2590.00000	28883.00000	26506.00000	2377.00000	4998694	4681748	5011344	4687956	602.76632	51.81353	565.40633	47.43239
AK	5338.00000	4761.00000	577.00000	4434.00000	4024.00000	410.00000	697258	734122	701214	739176	648.52981	82.75273	544.38997	58.47002
AZ	42719.00000	38738.00000	3981.00000	42320.00000	38323.00000	3997.00000	6854628	6744392	6970496	6853282	574.37349	58.07755	559.19193	57.34169

Encapsulating Bokeh Choropleth Plotting code in a function

# I'm going to generate more choropleths, so I'll encapsulate the code
#   I prototyped above into this function.
def make_choropleth(df, level1, level2, title, colors=COLORS):
    """Generates a choropleth with tooltips

    Args:
        df:        The DataFrame containing the relevant data. df should be
                 formatted such that it has a 2 level column-header
        level1:    The top column-header name (eg '2015' in examples above)
        level2:    The next-lower column header name (eg 'Male)
        title:     A title for the Choropleth
        colors:    An ordered list of color codes to use in mapping data.
    """
    state_names = []
    data = []
    min_val = df.loc[:,level1][level2].min()
    max_val = df.loc[:,level1][level2].max()
    bins = len(colors)
    TOOLS = "pan,wheel_zoom,reset,hover,save"
    for state_code in states:
        try:
            num_imprisoned = df.loc[state_code,level1][level2]
            state_names.append(state_code)
            data.append(num_imprisoned)
        except KeyError:
            state_names.append(' ')
            data.append(0)

    # Loading up Bokeh's special data structure
    source = ColumnDataSource(data=dict(
        x=state_xs,
        y=state_ys,
        name=state_names,
        imp_count = data
    ))

    # Maps the data value to a color
    color_mapper = LinearColorMapper(palette=colors,
                                     low=min(data),
                                     high=max(data))

    # Instantiating the figure
    p = figure(
        title=title, toolbar_location="left",
        plot_width=800, plot_height=450, tools=TOOLS
    )

    # Plotting the state-polygons on the figure
    p.patches('x', 'y', source=source,
              fill_color={'field': 'imp_count',
                          'transform': color_mapper},
              fill_alpha=1.0,
              line_color="#884444",
              line_width=2,
              line_alpha=0.3)

    # Setting the legend for colors
    color_bar = ColorBar(color_mapper=color_mapper,
                         ticker=BasicTicker(),
                         label_standoff=12,
                         border_line_color=None,
                         location=(0,0))

    p.add_layout(color_bar, 'right')
    hover = p.select_one(HoverTool)
    hover.point_policy = "follow_mouse"
    hover.tooltips = [
        ("State", "@name"),
        ("Imprisonment rate (per 100k state pop)", "@imp_count")
    ]
    output_notebook()
    show(p)

3.2 Visualization Breakthrough!

Now that we’ve normalized the number of imprisoned people to the state populations, we can compare rates between states. From looking at these choropleths, it’s clear that there are massive differences between states. ### Observations * For both men and women over the two included years, Massachusetts has the lowest imprisonment rates (below 145 men per 100k state male pop. and below 10 women per 100k state female pop). * For men, Louisiana has the highest imprisonment rate (at least 740 men per 100k state male pop.). * Delaware stands out as having a much higher imprisonment rate that its neighbors. Delaware imprisons at least 660 men per 100k state male pop., which is 2 to 3 times more than any neighboring state. * Oklahoma has the highest imprisonment rate for women at over 75 women per 100k state female pop. * In general, southern states have higher male imprisonment rates than the north. * Except for Delaware, New England and the Midwest have lower than average female imprisonment rates. * Bokeh’s built-in state geometries are convenient, but they’re not great (just look at Michigan and the Great Lakes).

Calling our Choropleth-plotting function

title = "Number of Imprisoned Females per 100k Female State Population (2015)"
make_choropleth(state_sex_join_df, '2015', 'Female_rate', title)

Loading BokehJS ...

Calling our Choropleth-plotting function

title = "Number of Imprisoned Females per 100k Female State Population (2016)"
make_choropleth(state_sex_join_df, '2016', 'Female_rate', title)

Loading BokehJS ...

Calling our Choropleth-plotting function

title = "Number of Imprisoned Males per 100k State Male Population (2015)"
make_choropleth(state_sex_join_df, '2015', 'Male_rate', title)

Loading BokehJS ...

Calling our Choropleth-plotting function

title = "Number of Imprisoned Males per 100k State Male Population (2016)"
make_choropleth(state_sex_join_df, '2016', 'Male_rate', title)

Loading BokehJS ...

3.3 State Imprisonment Distributions by Gender

From the choropleths above (and from the prior notebook), we see that women are imprisoned at a far lower rate than men. But how does the ratio of female-to-male imprisonment rates vary by state?

We could get a sense of that by checking values between different states in same-year-different-gender choropleths above, but that’s slow and difficult, which means it’s a bad way to visualize this data. Scatter plots are much better at visualizing ratios for many different cases, and histograms are much better at quickly expressing the distribution of many observations of a single data feature (eg male imprisonment rates across states).

Encapsulating Seaborn plot-generating code in a function

def plot_male_v_female_by_state_sea(yr, df):
    with sns.axes_style("darkgrid"):
        tmp_df = df.loc[:,(yr, ['Male_rate','Female_rate'])][yr]
        g = sns.JointGrid(x="Male_rate", y="Female_rate", data=tmp_df, size=8) 
        g.plot_joint(sns.regplot, order=2) 
        g.plot_marginals(sns.distplot)
        plt.subplots_adjust(top=0.93)
        title = 'Female vs Male State Imprisonment Rates in {}'.format(yr)
        g.fig.suptitle(title, fontsize=16)
        g.set_axis_labels('Male Imprisonment Rate [per 100k State male pop.]',
                          'Female Imprisonment Rate [per 100k State female pop.]', 
                          fontsize=13)

3.3.1 Static Scatter Plots with Distributions

The plots below (Seaborn’s JointGrid layout) combine both of these plots into a single visualization.

For both 2015 and 2016, we see that the bulk of imprisonment rates for women fall between 15 and 60 per 100k of respective state female population with a median rate of about 32 (per 100k state female pop.) and the bulk of imprisonment rates for men fall between 100 and 500 per 100k of respective state male pop. with a median rate around 350 (per 100k state male pop.).

We also see that there’s a fairly linear trend between the ratios of female-to-male imprisonment rates, meaning states that have a high male imprisonment rate also have a high female imprisonment rate, which could indicate that states’ criminal justice systems are consistent in how they treat women relative to how they treat men, or it could indicate actual differences in the rates at which men and women commit crime in different states. We would have to gather and explore more detailed data to explore these questions and possibly identify causal relationships.

Calling our plotting function to plot data for 2016 and 2016

plot_male_v_female_by_state_sea('2015', state_sex_join_df)
plot_male_v_female_by_state_sea('2016', state_sex_join_df)

/home/matt/miniconda3/envs/prisons_post_env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "

Encapsulating Bokeh plot-generating code in a function

def plot_male_v_female_by_state(yr, df):
    mf_df = df.loc[:,(yr, ['Male_rate','Female_rate'])][yr]
    cdf = ColumnDataSource(mf_df)
    p = figure(plot_height=500, plot_width=500, tools="", toolbar_location=None,
               title="Female Imprisonment Rates vs Male Imprisonment Rates in {}"
               .format(yr))
    p.background_fill_color="#f5f5f5"
    p.grid.grid_line_color="white"
    p.xaxis.axis_label = 'Male Imprisonment Rate [per 100k State male pop.]'
    p.yaxis.axis_label = 'Female Imprisonment Rate [per 100k State female pop.]'
    p.scatter(x='Male_rate',
              y='Female_rate',
              source=cdf,
              fill_alpha=0.6,
              size=12,
              line_color=None)

    p.add_tools(HoverTool(
        tooltips=[
            ('State', '@Code'),
            ('Male Rate', '@Male_rate'),
            ('Female Rate', '@Female_rate'),
        ],
    ))

    output_notebook()
    show(p)

3.3.2 Interactive Scatter Plots

The plots above also show a regression curve that appears to deflect downward near the high end of the male imprisonment rates (mildly for 2015, and more significantly in 2016). From inspection, we see the state with the highest male imprisonment rate has a fairly average female imprisonment rate. From the choropleths above, we could (correctly) guess that this is Louisiana, but this highlights the fact that we have to cross-reference other data to identify the states, but that’s inconvenient if you’re just reading through this notebook on NBViewer. So I’ll replicate these plots with a library that allows me to reveal more information when you hover over a data point.

Now we can confirm the outlier data point is Louisiana. You can also see that, in 2015, Alaska (AK) had the highest female imprisonment rate, which was not revealed in the choropleth maps above because I removed Alaska and Hawaii to make the maps more compact. Comparing between years, we can see that Alaska’s imprisonment rates for both men and women dropped significantly while the ratio remained fairly average.

Calling our plotting function to plot data for 2016 and 2016

plot_male_v_female_by_state('2015', state_sex_join_df)
plot_male_v_female_by_state('2016', state_sex_join_df)

Loading BokehJS ...

3.3.3 Summary

Good visualizations allow us to quickly see interesting trends and outliers that would otherwise be hidden in data. For example, while it is somewhat useful to know where the bulk of prisoners are located (Texas, California, and Florida), it’s more useful and interesting to see this data after we’ve controlled for state population. Interestingly, we see that Louisiana has a significantly higher imprisonment rate than any other state. Knowing this, we can target further research and questions about this. Per some brief independent research, I’ve found that Louisiana is one of two states that allow people to be convicted of felonies when only 10 of 12 jurors agree on guilt. This lower bar for conviction may be one of several causal factors driving Louisiana’s imprisonment rate, but it will take more data and research to strengthen that hypothesis.