Analysis of Seattle PD bicycle-related Field Contacts¶

kc_citations = pd.read_excel(data_dir + '2021-10-18 - compiled King County bike citation records.xlsx').drop(columns=['Unnamed: 0'])
spd_citations = kc_citations[kc_citations['Law Enforcement Agency'] == 'Seattle Police Department']
spd_citations.head(3)

# load compiled statistics by city
cities = pd.read_excel(data_dir + '2021-10-18 - King County helmet citation statistics.xlsx',index_col='Jurisdiction')
cities.loc['Seattle']

# load SPD Field Contact report summaries
spd_fc_summaries = pd.read_excel(data_dir + '2021-12-03 - Seattle PD Field Contact Report summaries - Installments 2-3.xlsx',index_col='Report Number')
spd_fc_summaries.head(3)

# load SPD Field Contact report entries
spd_fc_data = pd.read_excel(data_dir + '2021-06-16 - Seattle PD Records Management System (RMS) Field Contacts (2019-05-07 to 2021-03-26) - all bike-related.xlsx',
                            index_col='Report Number')

# redact identifying information; display
spd_fc_data.drop(columns=['Report Subtype','FCC Subject 1','FCC Subject 1 DOB','FCC Subject 1 Home Address Street Address',
                          'FCC Subject 1 Home Address Lat ','FCC Subject 1 Home Address Long','License Plate / Registration #','VIN #'],
                 inplace=True)
display(spd_fc_data.head(3))

# merge subject types into narrative summary entries
subject_types = spd_fc_data.loc[spd_fc_summaries.index]['FCC Subject 1 Involvement Type']
subject_types = subject_types[~subject_types.index.duplicated()]
spd_fc_summaries['Subject Types'] = subject_types

# merge dates into narrative summary entries
fc_dates = spd_fc_data.loc[spd_fc_summaries.index]['Date']
fc_dates = fc_dates[~fc_dates.index.duplicated()]
spd_fc_summaries['Date'] = fc_dates

print('\nNumber of reports reviewed:',len(spd_fc_summaries))

print('\nBreakdown of types of Field Contacts:')
display(spd_fc_summaries['FCC Type (redundant)'].value_counts())

print('\nBreakdown of types of Field Contacts (%):')
display(100 * spd_fc_summaries['FCC Type (redundant)'].value_counts() / len(spd_fc_summaries))

print('\nFraction of Field Contacts involving a homeless subject, either noted by officer or inferred from clear narrative elements: {0:.1f}%'\
      .format(100 * sum(spd_fc_summaries['Homelessness'].str.contains('Yes')) / len(spd_fc_summaries)))

print('\nFraction of Field Contacts in which officer(s) were on mounted bike patrol: {0:.1f}%'\
      .format(100 * sum(spd_fc_summaries['Officer on Bike Patrol'].str.contains('Yes')) / len(spd_fc_summaries)))

print('\nFraction of Field Contacts originating from a bike helmet violation: {0:.1f}%'\
      .format(100 * sum(spd_fc_summaries['Type of Bike Violation'].str.contains('Helmet violation')) / len(spd_fc_summaries)))

print('\nFraction of Field Contacts in which a warrant check was conducted: {0:.1f}%'\
      .format(100 * sum(spd_fc_summaries['Warrant Check'].str.contains('Yes')) / len(spd_fc_summaries)))

print('\nResults of warrant checks (yes indicates an open warrant was identified):')
display(spd_fc_summaries['Warrant Check Result'].value_counts())

print('\nResults of warrant checks (yes indicates an open warrant was identified) (%):')
display(100 * spd_fc_summaries['Warrant Check Result'].value_counts() / sum(spd_fc_summaries['Warrant Check'].str.contains('Yes')))

print('\nOutcomes of Field Contacts:')
display(spd_fc_summaries['Outcome'].value_counts())

print('\nOutcomes of Field Contacts (%):')
display(100 * spd_fc_summaries['Outcome'].value_counts() / len(spd_fc_summaries))

print('\nSuspicion articulated for Terry Stops:')
print(spd_fc_summaries[spd_fc_summaries['FCC Type (redundant)'] == 'Terry Stop']['Reason for Suspicion/Other Circumstances'].values)

print('\nFraction of Terry Stops in which a bike violation (e.g., helmet violation) was the sole rationale originally articulated for the stop; \n' + \
      'i.e., cases in which suspicion developed after the stop was initiated: {0:.0f}% ({1} of {2})'
      .format(100 * 2 / spd_fc_summaries['FCC Type (redundant)'].value_counts()['Terry Stop'],
              2,spd_fc_summaries['FCC Type (redundant)'].value_counts()['Terry Stop']))

print('\nNOTE: no suspicion was articulated in narrative reports for the remainder of Field Contacts that were not Terry Stops')

# subset Field Contacts into (1) Terry Stops, and (2) 'Bike/infraction warning-no cite'
spd_fc_terry_stops = spd_fc_data[spd_fc_data['FCC Type'] == 'Terry Stop']
spd_fc_bike_warning = spd_fc_data[spd_fc_data['FCC Type'] == 'Bike/infraction warning-no cite']
spd_fc_combined = spd_fc_data[logical_or(spd_fc_data['FCC Type'] == 'Terry Stop',
                                         spd_fc_data['FCC Type'] == 'Bike/infraction warning-no cite')]

# summarize
data_avail_in_months = (datetime(2021,3,26)-datetime(2019,5,7)) / timedelta(days=30.5)
print('\nTime period of Field Contact data availability: {0:.1f} months, from {1} to {2}'\
      .format(data_avail_in_months, datetime(2019,5,7).date(), datetime(2021,3,26).date()))

print('\nSearch keywords used for public records request (plus "Biker" and "Bicycler", which returned no Field Contacts during this period):')
display(spd_fc_data['Keyword'].value_counts())

print('\nFrom here on, we are specifically analyzing two largest types of Field Contacts for which subjects were reliably bicyclists\n  in the narrative reports reviewed: \n' + \
      '{0} Terry Stops and {1} "Bike/infraction warning-no cite" entries (ignoring less common categories - \n'\
      .format(*spd_fc_data['FCC Type'].value_counts()[['Terry Stop','Bike/infraction warning-no cite']].values) + \
      '  campsite clearances, other infractions, tresspass warnings, park violations, pedestrian violations/warnings, citizen tips, etc.)')

susp_frac = spd_fc_bike_warning['FCC Subject 1 Involvement Type'].value_counts()[['suspect','Suspicious']].sum() \
            / len(spd_fc_bike_warning)
print('\nFraction of bike infraction warning entries involving suspicion of subject unrelated to infraction (i.e., pretextual stops):\n  {0:.1f}%'
      .format(100 * susp_frac))

print('\nAnnual rate of police contacts based on these records:\n' + \
      '- Terry Stops only: {0:.0f} per year (some may not be stops of bicyclists, but most likely are)\n'
      .format(12 * len(spd_fc_terry_stops) / data_avail_in_months) + \
      '- Bike infraction warnings only: {0:.0f} per year\n'
      .format(12 * len(spd_fc_bike_warning) / data_avail_in_months) + \
      '- Both combined: {0:.0f} per year\n'
      .format(12 * len(spd_fc_combined) / data_avail_in_months) + \
      '- ** Estimated Terry Stops that develop solely from a bike infraction, based on 2-in-7 statistic from above: {0:.0f} per year\n'
      .format((2/7) * 12 * len(spd_fc_terry_stops) / data_avail_in_months) + \
      '- ** Bike infraction warnings involving suspicion (pretextual stops): {0:.0f} per year\n'
      .format(susp_frac * 12 * len(spd_fc_bike_warning) / data_avail_in_months) + \
      '\n      ** = suspicion-related stops enabled by existing bicycle infractions')

annual_citation_rate = len(spd_citations[logical_and(spd_citations['Violation Date'].dt.year >= 2015,
                                                     spd_citations['Violation Date'].dt.year <= 2019)]) / 5
print('\nCompare sum of two final annual rates to annual rate of bike-related citations (to bicyclists) based on Seattle Municipal Court records:\n' + \
      '- {0:.0f} per year'.format(annual_citation_rate))

print('\nFrequency of arrests resulting from Terry stops of bicyclists: {0:.1f}%'\
      .format(100 * sum(spd_fc_terry_stops['FCC Disposition'].str.contains('Arrest',case=False)) \
              / len(spd_fc_terry_stops)))
      

print('\n\n-- DEMOGRAPHICS --')

print('\n\nFraction of Field Contacts involving a homeless subject, as noted by officer in a check box RMS field:\n' + \
      '- Terry Stops only: {0:.1f}%\n- Bike infraction warnings only: {1:.1f}%\n- Both combined: {2:.1f}%\n'
      .format(100 * sum(spd_fc_terry_stops['Event Statistics'].str.contains('Homelessness')) / len(spd_fc_terry_stops),
              100 * sum(spd_fc_bike_warning['Event Statistics'].str.contains('Homelessness')) / len(spd_fc_bike_warning),
              100 * sum(spd_fc_combined['Event Statistics'].str.contains('Homelessness')) / len(spd_fc_combined)) + \
      '\n      NOTE: the details in the narrative reports reviewed above indicate these percentages are likely an undercount')

susp_mask = logical_or(spd_fc_bike_warning['FCC Subject 1 Involvement Type'] == 'suspect',
                       spd_fc_bike_warning['FCC Subject 1 Involvement Type'] == 'Suspicious')
spd_fc_bike_warning_susp = spd_fc_bike_warning[susp_mask]
spd_fc_bike_warning_non_susp = spd_fc_bike_warning[~susp_mask]

def ethnicity_disp(input_data):
    ethnicity_counts = input_data[input_data['FCC Subject 1 Race'] == 'White']['FCC Subject 1 Ethnicity'].value_counts().drop('Unknown')
    print()
    eth_summary = 100 * ethnicity_counts / ethnicity_counts.sum()
    display(eth_summary)
    return eth_summary

print('\n\nAll recorded bike infraction stops resulting in warnings (n = {0}):'\
      .format(spd_fc_bike_warning['FCC Subject 1 Race'].value_counts().sum()))
demos_spd_fc_bike_warning = 100 * spd_fc_bike_warning['FCC Subject 1 Race'].value_counts(normalize=True)
display(demos_spd_fc_bike_warning)
eth_spd_fc_bike_warning = ethnicity_disp(spd_fc_bike_warning)

print('\n\nPretextual stops (bike infraction stops resulting in warnings in which suspicion was a factor in the stop) (n = {0}):'\
      .format(spd_fc_bike_warning_susp['FCC Subject 1 Race'].value_counts().sum()))
demos_spd_fc_bike_warning_susp = 100 * spd_fc_bike_warning_susp['FCC Subject 1 Race'].value_counts(normalize=True)
display(demos_spd_fc_bike_warning_susp)
eth_spd_fc_bike_warning_susp = ethnicity_disp(spd_fc_bike_warning_susp)

print('\n\nTerry stops of bicyclists (n = {0}):'\
      .format(spd_fc_terry_stops['FCC Subject 1 Race'].value_counts().sum()))
demos_spd_fc_terry_stops = 100 * spd_fc_terry_stops['FCC Subject 1 Race'].value_counts(normalize=True)
display(demos_spd_fc_terry_stops)
eth_spd_fc_terry_stops = ethnicity_disp(spd_fc_terry_stops)

plt.figure(figsize=(10,5.5),facecolor='w')

labels = ['White','Hispanic/Latino','Asian/Pacific Islander','Black','Other/Unknown']
colors = ['cornflowerblue','cornflowerblue','indianred','olivedrab','0.7']
bump_label = [0,0,0,-0.4,0]
bump_label_v = [0,0,0,0,0]
label_star = [0,0,0,0,0]
y_baseline = 4.2
widths = array([demos_spd_fc_bike_warning['White'] * eth_spd_fc_bike_warning['Not Hispanic Or Latino']/100,
                demos_spd_fc_bike_warning['White'] * eth_spd_fc_bike_warning['Hispanic Or Latino']/100,
                demos_spd_fc_bike_warning['Asian'],
                demos_spd_fc_bike_warning['Black or African American'],
                demos_spd_fc_bike_warning['Unknown']])
h1 = plt.barh(y_baseline,widths,height=0.5,left=array([0,*cumsum(widths)[:-1]]),color=colors,edgecolor='w',linewidth=2.0)
h1[1].set_hatch('/////')   # for Hispanic/Latino category
for w_idx, w in enumerate(widths):
    plt.text(array([0,*cumsum(widths)[:-1]])[w_idx] + w/2 + bump_label[w_idx], y_baseline - 0.4 + bump_label_v[w_idx],
             '{0:.1f}%{1}'.format(w,'$^*$'*label_star[w_idx]),
             horizontalalignment='center',fontstyle='italic',color='0.3',fontsize=9)
plt.text(2,y_baseline+0.3,r'$\bf{Recorded~bicycle~infraction~stops~resulting~in~warnings}$ (Seattle PD, 2019-2021)')
plt.text(86,y_baseline+0.34,'(n = {0})'.format(spd_fc_bike_warning['FCC Subject 1 Race'].value_counts().sum()),
         style='italic',color='0.3',verticalalignment='center',fontsize=9)

labels = ['White','Hispanic/Latino','Black','Other/Unknown']
colors = ['cornflowerblue','cornflowerblue','olivedrab','0.7']
bump_label = [0,0,-0.4,0]
bump_label_v = [0,0,0,0]
label_star = [0,0,0,0]
y_baseline = 3.0
widths = array([demos_spd_fc_bike_warning_susp['White'] * eth_spd_fc_bike_warning_susp['Not Hispanic Or Latino']/100,
                demos_spd_fc_bike_warning_susp['White'] * eth_spd_fc_bike_warning_susp['Hispanic Or Latino']/100,
                demos_spd_fc_bike_warning_susp['Black or African American'],
                demos_spd_fc_bike_warning_susp['Unknown']])
h2 = plt.barh(y_baseline,widths,height=0.5,left=array([0,*cumsum(widths)[:-1]]),color=colors,edgecolor='w',linewidth=2.0)
h2[1].set_hatch('/////')   # for Hispanic/Latino category
for w_idx, w in enumerate(widths):
    plt.text(array([0,*cumsum(widths)[:-1]])[w_idx] + w/2 + bump_label[w_idx], y_baseline - 0.4 + bump_label_v[w_idx],
             '{0:.1f}%{1}'.format(w,'$^*$'*label_star[w_idx]),
             horizontalalignment='center',fontstyle='italic',color='0.3',fontsize=9)
plt.text(2,y_baseline+0.3,r'$\bf{Recorded~pretextual~stops~of~cyclists}$$^\ddag$ (Seattle PD, 2019-2021)')
plt.text(68,y_baseline+0.34,'(n = {0})'.format(spd_fc_bike_warning_susp['FCC Subject 1 Race'].value_counts().sum()),
         style='italic',color='0.3',verticalalignment='center',fontsize=9)
plt.text(100,y_baseline-0.67,'$^\ddag$These represent bicycle infraction stops resulting in warnings in which ' + \
                             'some unrelated suspicion was recorded as a factor in the stop',
         fontstyle='italic',horizontalalignment='right',color='0.5',fontsize=7)

labels = ['White','Hispanic/Latino','Asian/Pacific Islander','Black',
          'Native American/Alaska Native','Other/Unknown']
colors = ['cornflowerblue','cornflowerblue','indianred','olivedrab','gold','0.7']
bump_label = [0,0,0,-0.4,0.0,1.2]
bump_label_v = [0,0,0,0,0,0]   # if needed, drop by -0.17
label_star = [0,0,0,0,0,0]
y_baseline = 1.6
widths = array([demos_spd_fc_terry_stops['White'] * eth_spd_fc_terry_stops['Not Hispanic Or Latino']/100,
                demos_spd_fc_terry_stops['White'] * eth_spd_fc_terry_stops['Hispanic Or Latino']/100,
                demos_spd_fc_terry_stops['Asian'] + demos_spd_fc_terry_stops['Native Hawaiian or Other Pacific Islander'],
                demos_spd_fc_terry_stops['Black or African American'],
                demos_spd_fc_terry_stops['American Indian or Alaska Native'],
                demos_spd_fc_terry_stops['Unknown']])
h3 = plt.barh(y_baseline,widths,height=0.5,left=array([0,*cumsum(widths)[:-1]]),color=colors,edgecolor='w',linewidth=2.0)
h3[1].set_hatch('/////')   # for Hispanic/Latino category
for w_idx, w in enumerate(widths):
    plt.text(array([0,*cumsum(widths)[:-1]])[w_idx] + w/2 + bump_label[w_idx], y_baseline - 0.4 + bump_label_v[w_idx],
             '{0:.1f}%{1}'.format(w,'$^*$'*label_star[w_idx]),
             horizontalalignment='center',fontstyle='italic',color='0.3',fontsize=9)
plt.text(2,y_baseline+0.3,r'$\bf{Inferred~Terry~stops~of~cyclists}$$^\S$ (Seattle PD, 2019-2021)')
plt.text(62,y_baseline+0.34,'(n = {0})'.format(spd_fc_terry_stops['FCC Subject 1 Race'].value_counts().sum()),
         style='italic',color='0.3',verticalalignment='center',fontsize=9)
plt.text(100,y_baseline-0.80,'$^\S$These were identified as containing one of the keywords "bicycle," "bike," "bicyclist," or "cyclist" ' + \
                             'in the associated Field Contact report;\na small fraction (< ~15%) may represent stops by ' + \
                             'officers on bicycle patrol or stops involving a bicycle, rather than stops of cyclists',
         fontstyle='italic',horizontalalignment='right',color='0.5',fontsize=7)

plt.ylim([0,4.7])

plt.legend(h3,labels,ncol=3,frameon=False,loc='lower left',prop={'size':9.5});
plt.xticks([]); plt.yticks([])
plt.gca().spines['top'].set_visible(False); plt.gca().spines['bottom'].set_visible(False)
plt.gca().spines['left'].set_visible(False); plt.gca().spines['right'].set_visible(False)

plt.savefig(current_results_dir + 'spd_field_contact_racial_demographics.pdf')

# housing status demographics from review of 40 narrative Field Contact reports

fc_report_housing = 100 * spd_fc_summaries['Homelessness'].value_counts(normalize=True,dropna=False)
presume_homeless_ratio = (fc_report_housing['Yes'] + fc_report_housing['Yes (presumed)']) / fc_report_housing['Yes']
fc_data_homeless_recorded = 100 * sum(spd_fc_combined['Event Statistics'].str.contains('Homelessness')) / len(spd_fc_combined)
fc_data_homeless_inferred = fc_data_homeless_recorded * presume_homeless_ratio

def autopct(percent): return ('%.0f%%' % percent)

plt.figure(figsize=(6,4),facecolor='w')
wedges, labels, autopct = plt.pie([(100-fc_data_homeless_inferred),fc_data_homeless_inferred],
                                  explode=tile(0.01,2),startangle=90,
                                  labels=['Not\nindicated','Homeless'],
                                  colors=['0.4','0.7'],
                                  labeldistance=1.10,autopct=autopct,pctdistance=0.75)
autopct[0].set_color('w')
for lab in labels: lab.set_fontsize(11)
for pct in autopct: pct.set_fontsize(10)
plt.title('Housing status in bicyclist Field Contact reports\n(2019-2021)');
# RE: text below... see updated version in Keynote presentation for OIG, 12/14/21
# plt.text(0.5,0.05,"Note: the 'homelessness' box was selected by officers in {0:.0f}% of $n$ = {1} reports; however,\nthe frequency of homelessness mentioned in a sample of Field Contact narratives ($n$ = {2})\nsuggests a scaling factor of ~{3:.1f}x to account for underreporting.".format(fc_data_homeless_recorded,len(spd_fc_combined),len(spd_fc_summaries),presume_homeless_ratio),
#          fontsize=7,fontstyle='italic',horizontalalignment='center',transform=plt.gcf().transFigure);
plt.tight_layout()
plt.savefig(current_results_dir + 'spd_field_contact_homelessness.pdf')

# frequency distribution of all Seattle bicycle citations
spd_citations['Law Description'].value_counts(normalize=True)