Date: July 14, 2022
+ +import logging
+
+
+def configure_root_logger():
+ console_logger = logging.StreamHandler()
+ console_logger.setLevel(logging.DEBUG)
+ console_logger.setFormatter(
+ logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+ )
+
+ logger = logging.getLogger()
+ logger.handlers = [] # reset handlers in case this cell is re-run
+ logger.setLevel(logging.DEBUG)
+ logger.addHandler(console_logger)
+ return logger
+import os
+
+from sqlalchemy import create_engine
+
+from dash import Dash, html, dcc
+import plotly.express as px
+import pandas as pd
+import plotly
+plotly.offline.init_notebook_mode()
+logger = configure_root_logger()
+pg_engine = create_engine("postgresql://postgres@127.0.0.1:54344/traffic_stops_nc")
+pg_conn = pg_engine.connect()
+df = pd.read_sql(
+ f"""
+ SELECT
+ id
+ , name
+ FROM nc_agency
+ WHERE name ~ 'Mecklenburg County'
+ """,
+ pg_conn,
+)
+agency_id = df.iloc[0]['id']
+df
+| + | id | +name | +
|---|---|---|
| 0 | +168 | +Mecklenburg County Sheriff's Office | +
df = pd.read_sql(
+ f"""
+ SELECT
+ MIN(date)::date AS first_reported_stop
+ , MAX(date)::date AS last_reported_stop
+ , COUNT(*) AS total_stops
+ FROM nc_stop
+ WHERE nc_stop.agency_id = {agency_id}
+ """,
+ pg_conn,
+)
+df.T
+| + | 0 | +
|---|---|
| first_reported_stop | +2002-01-01 | +
| last_reported_stop | +2022-07-13 | +
| total_stops | +41952 | +
The Mecklenburg County Sheriff's Office made about 40k stops in the past ~20 years.
+ +mecklenburg_driver_stops = f"""
+SELECT
+ "nc_stop"."stop_id"
+ , "nc_stop"."date"
+ , DATE_TRUNC('year', date AT TIME ZONE 'America/New_York') AS "year"
+ , (CASE WHEN nc_person.ethnicity = 'H' THEN 'Hispanic'
+ WHEN nc_person.ethnicity = 'N' AND nc_person.race = 'A' THEN 'Asian'
+ WHEN nc_person.ethnicity = 'N' AND nc_person.race = 'B' THEN 'Black'
+ WHEN nc_person.ethnicity = 'N' AND nc_person.race = 'I' THEN 'Native American'
+ WHEN nc_person.ethnicity = 'N' AND nc_person.race = 'U' THEN 'Other'
+ WHEN nc_person.ethnicity = 'N' AND nc_person.race = 'W' THEN 'White'
+ END) as driver_race
+ , "nc_stop"."officer_id"
+ , (CASE WHEN nc_stop.purpose = 1 THEN 'Speed Limit Violation' -- "Safety stop"
+ WHEN nc_stop.purpose = 2 THEN 'Stop Light/Sign Violation' -- "Safety stop"
+ WHEN nc_stop.purpose = 3 THEN 'Driving While Impaired' -- "Safety stop"
+ WHEN nc_stop.purpose = 4 THEN 'Safe Movement Violation' -- "Safety stop"
+ WHEN nc_stop.purpose = 8 THEN 'Investigation' -- "Investigatory stop"
+ WHEN nc_stop.purpose = 7 THEN 'Seat Belt Violation' -- "Investigatory stop"
+ WHEN nc_stop.purpose = 10 THEN 'Checkpoint' -- "Investigatory stop"
+ WHEN nc_stop.purpose = 5 THEN 'Vehicle Equipment Violation' -- "Economic stop"
+ WHEN nc_stop.purpose = 6 THEN 'Vehicle Regulatory Violation' -- "Economic stop"
+ WHEN nc_stop.purpose = 9 THEN 'Other Motor Vehicle Violation' -- "Economic stop"
+ END) as stop_purpose
+ , (CASE WHEN nc_stop.purpose IN (1, 2, 3, 4) THEN 'Safety Violation'
+ WHEN nc_stop.purpose IN (7, 8, 10) THEN 'Investigatory'
+ WHEN nc_stop.purpose IN (5, 6, 9) THEN 'Regulatory and Equipment'
+ ELSE 'Other'
+ END) as stop_purpose_group
+ , (nc_search.search_id IS NOT NULL) AS driver_searched
+ , "nc_search"."search_id"
+ , (CASE WHEN nc_search.type = 1 THEN 'Consent'
+ WHEN nc_search.type = 2 THEN 'Search Warrant'
+ WHEN nc_search.type = 3 THEN 'Probable Cause'
+ WHEN nc_search.type = 4 THEN 'Search Incident to Arrest'
+ WHEN nc_search.type = 5 THEN 'Protective Frisk'
+ ELSE ''
+ END) as search_type
+ , (CASE
+ WHEN nc_contraband.contraband_id IS NULL THEN false
+ ELSE true
+ END) AS contraband_found
+ , nc_contraband.contraband_id
+ , (CASE WHEN nc_contraband.ounces > 0 OR nc_contraband.pounds > 0 OR nc_contraband.dosages > 0 OR nc_contraband.grams > 0 OR nc_contraband.kilos > 0 THEN true
+ ELSE false
+ END) AS drugs_found
+ , (CASE WHEN nc_contraband.pints > 0 OR nc_contraband.gallons > 0 THEN true
+ ELSE false
+ END) AS alcohol_found
+ , (CASE WHEN nc_contraband.weapons > 0 THEN true
+ ELSE false
+ END) AS weapons_found
+ , (CASE WHEN nc_contraband.money > 0 THEN true
+ ELSE false
+ END) AS money_found
+ , (CASE WHEN nc_contraband.dollar_amount > 0 THEN true
+ ELSE false
+ END) AS other_found
+ , nc_contraband.ounces AS contraband_ounces
+ , nc_contraband.pounds AS contraband_pounds
+ , nc_contraband.dosages AS contraband_dosages
+ , nc_contraband.grams AS contraband_grams
+ , nc_contraband.kilos AS contraband_kilos
+ , nc_contraband.pints AS contraband_pints
+ , nc_contraband.gallons AS contraband_gallons
+ , nc_contraband.weapons AS contraband_weapons
+ , nc_contraband.money AS contraband_money
+ , nc_contraband.dollar_amount AS contraband_dollar_amount
+FROM "nc_stop"
+INNER JOIN "nc_person"
+ ON ("nc_stop"."stop_id" = "nc_person"."stop_id" AND "nc_person"."type" = 'D')
+LEFT OUTER JOIN "nc_search"
+ ON ("nc_stop"."stop_id" = "nc_search"."stop_id")
+LEFT OUTER JOIN "nc_contraband"
+ ON ("nc_stop"."stop_id" = "nc_contraband"."stop_id")
+WHERE
+ nc_stop.agency_id = {agency_id}
+ORDER BY nc_stop.date ASC
+"""
+df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ driver_searched
+ , COUNT(*) AS stop_count
+ FROM stops
+ GROUP BY 1
+ ORDER BY 1
+ """,
+ pg_conn,
+)
+df
+| + | driver_searched | +stop_count | +
|---|---|---|
| 0 | +False | +41597 | +
| 1 | +True | +355 | +
Drivers were searched in 355 of the ~42k total stops.
+ +df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ contraband_found
+ , COUNT(*) AS stop_count
+ FROM stops
+ GROUP BY 1
+ ORDER BY 1
+ """,
+ pg_conn,
+)
+df
+| + | contraband_found | +stop_count | +
|---|---|---|
| 0 | +False | +41845 | +
| 1 | +True | +107 | +
Contraband was found in 107 of the ~42k total stops.
+ +df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ drugs_found
+ , alcohol_found
+ , weapons_found
+ , money_found
+ , other_found
+ , COUNT(*)
+ FROM stops
+ WHERE contraband_found = true
+ GROUP BY 1, 2, 3, 4, 5
+ ORDER BY 1, 2, 3, 4, 5
+ """,
+ pg_conn,
+)
+pd.concat([df, pd.DataFrame([{"count": df["count"].sum()}])], ignore_index=True)
+| + | drugs_found | +alcohol_found | +weapons_found | +money_found | +other_found | +count | +
|---|---|---|---|---|---|---|
| 0 | +False | +False | +False | +False | +False | +26 | +
| 1 | +False | +False | +False | +False | +True | +5 | +
| 2 | +False | +False | +False | +True | +False | +2 | +
| 3 | +False | +False | +True | +False | +False | +11 | +
| 4 | +False | +False | +True | +True | +False | +1 | +
| 5 | +False | +True | +False | +False | +False | +14 | +
| 6 | +True | +False | +False | +False | +False | +20 | +
| 7 | +True | +False | +False | +True | +False | +1 | +
| 8 | +True | +False | +True | +False | +False | +1 | +
| 9 | +True | +False | +True | +True | +False | +1 | +
| 10 | +True | +True | +False | +False | +False | +20 | +
| 11 | +True | +True | +True | +False | +False | +2 | +
| 12 | +True | +True | +True | +True | +False | +3 | +
| 13 | +NaN | +NaN | +NaN | +NaN | +NaN | +107 | +
Multiple contraband seizures may occur during the same stop (e.g. both drugs and alcohol found).
+ +df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ stop_purpose_group
+ , driver_race
+ , COUNT(*) FILTER (WHERE drugs_found = true) AS drugs_found
+ , COUNT(*) FILTER (WHERE alcohol_found = true) AS alcohol_found
+ , COUNT(*) FILTER (WHERE weapons_found = true) AS weapons_found
+ , COUNT(*) FILTER (WHERE money_found = true) AS money_found
+ , COUNT(*) FILTER (WHERE other_found = true) AS other_found
+ , COUNT(*) FILTER (WHERE driver_searched = true) AS drivers_searched
+ , COUNT(*) AS total_stops
+ FROM stops
+ GROUP BY 1, 2
+ ORDER BY 1, 2
+ """,
+ pg_conn,
+)
+pd.concat([df, df[["drugs_found", "alcohol_found", "weapons_found", "money_found", "other_found", "drivers_searched", "total_stops"]].sum().to_frame().T], ignore_index=True)
+| + | stop_purpose_group | +driver_race | +drugs_found | +alcohol_found | +weapons_found | +money_found | +other_found | +drivers_searched | +total_stops | +
|---|---|---|---|---|---|---|---|---|---|
| 0 | +Investigatory | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +8 | +
| 1 | +Investigatory | +Black | +6 | +3 | +1 | +1 | +1 | +32 | +525 | +
| 2 | +Investigatory | +Hispanic | +0 | +0 | +0 | +0 | +0 | +2 | +143 | +
| 3 | +Investigatory | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +6 | +
| 4 | +Investigatory | +Other | +0 | +0 | +0 | +0 | +0 | +2 | +27 | +
| 5 | +Investigatory | +White | +4 | +2 | +0 | +0 | +0 | +12 | +327 | +
| 6 | +Regulatory and Equipment | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +96 | +
| 7 | +Regulatory and Equipment | +Black | +3 | +1 | +4 | +4 | +1 | +52 | +3890 | +
| 8 | +Regulatory and Equipment | +Hispanic | +1 | +0 | +0 | +0 | +0 | +15 | +732 | +
| 9 | +Regulatory and Equipment | +Native American | +0 | +0 | +0 | +0 | +0 | +2 | +57 | +
| 10 | +Regulatory and Equipment | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +150 | +
| 11 | +Regulatory and Equipment | +White | +3 | +1 | +1 | +1 | +2 | +19 | +2215 | +
| 12 | +Safety Violation | +Asian | +0 | +0 | +0 | +0 | +0 | +1 | +753 | +
| 13 | +Safety Violation | +Black | +20 | +18 | +8 | +1 | +1 | +110 | +15528 | +
| 14 | +Safety Violation | +Hispanic | +4 | +5 | +3 | +1 | +0 | +41 | +4179 | +
| 15 | +Safety Violation | +Native American | +0 | +0 | +0 | +0 | +0 | +1 | +200 | +
| 16 | +Safety Violation | +Other | +0 | +0 | +0 | +0 | +0 | +4 | +996 | +
| 17 | +Safety Violation | +White | +7 | +9 | +2 | +0 | +0 | +62 | +12120 | +
| 18 | +NaN | +NaN | +48 | +39 | +19 | +8 | +5 | +355 | +41952 | +
Breakdown of contraband seizures by race and stop purpose grouping.
+ +df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ year
+ , stop_purpose_group
+ , driver_race
+ , COUNT(*) FILTER (WHERE drugs_found = true) AS drugs_found
+ , COUNT(*) FILTER (WHERE alcohol_found = true) AS alcohol_found
+ , COUNT(*) FILTER (WHERE weapons_found = true) AS weapons_found
+ , COUNT(*) FILTER (WHERE money_found = true) AS money_found
+ , COUNT(*) FILTER (WHERE other_found = true) AS other_found
+ , COUNT(*) FILTER (WHERE driver_searched = true) AS drivers_searched
+ , COUNT(*) AS total_stops
+ FROM stops
+ WHERE year >= '2020-01-01'
+ GROUP BY 1, 2, 3
+ ORDER BY 1, 2, 3
+ """,
+ pg_conn,
+)
+pd.concat([df, df[["drugs_found", "alcohol_found", "weapons_found", "money_found", "other_found", "drivers_searched", "total_stops"]].sum().to_frame().T], ignore_index=True)
+| + | year | +stop_purpose_group | +driver_race | +drugs_found | +alcohol_found | +weapons_found | +money_found | +other_found | +drivers_searched | +total_stops | +
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +2020-01-01 | +Investigatory | +Black | +1 | +1 | +0 | +0 | +0 | +2 | +48 | +
| 1 | +2020-01-01 | +Investigatory | +Hispanic | +0 | +0 | +0 | +0 | +0 | +0 | +10 | +
| 2 | +2020-01-01 | +Investigatory | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +1 | +
| 3 | +2020-01-01 | +Investigatory | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +2 | +
| 4 | +2020-01-01 | +Investigatory | +White | +0 | +0 | +0 | +0 | +0 | +0 | +24 | +
| 5 | +2020-01-01 | +Regulatory and Equipment | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +3 | +
| 6 | +2020-01-01 | +Regulatory and Equipment | +Black | +0 | +0 | +0 | +0 | +0 | +0 | +340 | +
| 7 | +2020-01-01 | +Regulatory and Equipment | +Hispanic | +0 | +0 | +0 | +0 | +0 | +0 | +50 | +
| 8 | +2020-01-01 | +Regulatory and Equipment | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +4 | +
| 9 | +2020-01-01 | +Regulatory and Equipment | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +6 | +
| 10 | +2020-01-01 | +Regulatory and Equipment | +White | +0 | +0 | +0 | +0 | +0 | +0 | +146 | +
| 11 | +2020-01-01 | +Safety Violation | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +102 | +
| 12 | +2020-01-01 | +Safety Violation | +Black | +2 | +1 | +1 | +0 | +0 | +4 | +2633 | +
| 13 | +2020-01-01 | +Safety Violation | +Hispanic | +0 | +0 | +0 | +0 | +0 | +2 | +692 | +
| 14 | +2020-01-01 | +Safety Violation | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +37 | +
| 15 | +2020-01-01 | +Safety Violation | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +147 | +
| 16 | +2020-01-01 | +Safety Violation | +White | +3 | +1 | +0 | +0 | +0 | +3 | +1995 | +
| 17 | +2021-01-01 | +Investigatory | +Black | +0 | +0 | +0 | +0 | +0 | +1 | +82 | +
| 18 | +2021-01-01 | +Investigatory | +Hispanic | +0 | +0 | +0 | +0 | +0 | +0 | +38 | +
| 19 | +2021-01-01 | +Investigatory | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +6 | +
| 20 | +2021-01-01 | +Investigatory | +White | +1 | +1 | +0 | +0 | +0 | +1 | +32 | +
| 21 | +2021-01-01 | +Regulatory and Equipment | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +6 | +
| 22 | +2021-01-01 | +Regulatory and Equipment | +Black | +0 | +0 | +0 | +0 | +0 | +0 | +440 | +
| 23 | +2021-01-01 | +Regulatory and Equipment | +Hispanic | +1 | +0 | +0 | +0 | +0 | +1 | +120 | +
| 24 | +2021-01-01 | +Regulatory and Equipment | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +4 | +
| 25 | +2021-01-01 | +Regulatory and Equipment | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +22 | +
| 26 | +2021-01-01 | +Regulatory and Equipment | +White | +0 | +0 | +0 | +0 | +0 | +0 | +247 | +
| 27 | +2021-01-01 | +Safety Violation | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +144 | +
| 28 | +2021-01-01 | +Safety Violation | +Black | +5 | +2 | +1 | +0 | +0 | +10 | +3457 | +
| 29 | +2021-01-01 | +Safety Violation | +Hispanic | +0 | +0 | +1 | +0 | +0 | +2 | +1089 | +
| 30 | +2021-01-01 | +Safety Violation | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +33 | +
| 31 | +2021-01-01 | +Safety Violation | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +236 | +
| 32 | +2021-01-01 | +Safety Violation | +White | +1 | +0 | +1 | +0 | +0 | +2 | +2328 | +
| 33 | +2022-01-01 | +Investigatory | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +2 | +
| 34 | +2022-01-01 | +Investigatory | +Black | +0 | +0 | +0 | +0 | +0 | +1 | +38 | +
| 35 | +2022-01-01 | +Investigatory | +Hispanic | +0 | +0 | +0 | +0 | +0 | +0 | +8 | +
| 36 | +2022-01-01 | +Investigatory | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +2 | +
| 37 | +2022-01-01 | +Investigatory | +White | +0 | +0 | +0 | +0 | +0 | +0 | +7 | +
| 38 | +2022-01-01 | +Regulatory and Equipment | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +5 | +
| 39 | +2022-01-01 | +Regulatory and Equipment | +Black | +0 | +0 | +0 | +0 | +0 | +1 | +220 | +
| 40 | +2022-01-01 | +Regulatory and Equipment | +Hispanic | +0 | +0 | +0 | +0 | +0 | +0 | +38 | +
| 41 | +2022-01-01 | +Regulatory and Equipment | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +2 | +
| 42 | +2022-01-01 | +Regulatory and Equipment | +Other | +0 | +0 | +0 | +0 | +0 | +0 | +13 | +
| 43 | +2022-01-01 | +Regulatory and Equipment | +White | +0 | +0 | +0 | +0 | +0 | +0 | +124 | +
| 44 | +2022-01-01 | +Safety Violation | +Asian | +0 | +0 | +0 | +0 | +0 | +0 | +59 | +
| 45 | +2022-01-01 | +Safety Violation | +Black | +2 | +0 | +1 | +1 | +0 | +6 | +1053 | +
| 46 | +2022-01-01 | +Safety Violation | +Hispanic | +1 | +1 | +1 | +1 | +0 | +1 | +303 | +
| 47 | +2022-01-01 | +Safety Violation | +Native American | +0 | +0 | +0 | +0 | +0 | +0 | +5 | +
| 48 | +2022-01-01 | +Safety Violation | +Other | +0 | +0 | +0 | +0 | +0 | +1 | +74 | +
| 49 | +2022-01-01 | +Safety Violation | +White | +0 | +0 | +0 | +0 | +0 | +0 | +636 | +
| 50 | +NaT | +NaN | +NaN | +17 | +7 | +6 | +2 | +0 | +38 | +17113 | +
Breakdown of contraband seizures by race and stop purpose grouping since 2020.
+ +df = pd.read_sql(
+ f"""
+ WITH stops AS ({mecklenburg_driver_stops})
+ SELECT
+ *
+ FROM stops
+ """,
+ pg_conn,
+)
+df.to_csv("mecklenburg-sheriff-2022-07.csv", index=False)
+