FROM tmp.moving_violations a INNER JOIN source_data.mv_geocodes ON source_data.mv_geocodes.location = a.location
WHERE a.latitude IS NULL and a.longitude IS NULL
"""
engine.execute(step_2_query)
print("lat and long values updated")
# create geography field with populated lat and long values, and create index
step_3_query = """
UPDATE tmp.moving_violations_add_geocode
SET geography = ST_SetSRID(ST_MakePoint(longitude_2, latitude_2), 4326)::geography
WHERE geography IS NULL AND longitude_2 is not null AND latitude_2 is not null
;
CREATE INDEX IF NOT EXISTS mv_geom_idx ON tmp.moving_violations_add_geocode USING GIST (geography);
"""
engine.execute(step_3_query)
print("geography field created")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine, target_schema='tmp', target_table='moving_violations_nbh_ward', from_schema='tmp', from_table='moving_violations_add_geocode', partition_by_field='objectid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine, target_schema='tmp', target_table='moving_violations_schools', from_schema=next_tables[0], from_table=next_tables[1])
print("schools query complete")
next_tables = add_roadway_info(engine=engine, target_schema='tmp', target_table='moving_violations_roadway_info', from_schema=next_tables[0], from_table=next_tables[1], partition_by_field='objectid', within_distance= 10)
print("roadway info query complete")
next_tables = add_intersection_info(engine=engine, target_schema='tmp', target_table='moving_violations_intersection_info', from_schema=next_tables[0], from_table=next_tables[1], partition_by_field='objectid', within_distance= 20)
print("intersection info query complete")
row_count = create_final_table(engine=engine, target_schema = 'analysis_data', target_table='moving_violations', from_schema=next_tables[0], from_table=next_tables[1])
print("final query complete with row count ",row_count)
env = "DEV"
engine = create_postgres_engine(destination="AWS_PostGIS",
target_db=dbname,
env=env)
db_credentials = get_connection_strings("AWS_PostGIS")
engine.execute(
'update source_data.census_blocks set geography = ST_MakeValid(geography::geometry)::geography'
)
print('geography updated')
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='census_blocks_nbh_ward',
from_schema='source_data',
from_table='census_blocks',
partition_by_field='objectid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='census_blocks_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='census_blocks',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
def generate_pulsepoint_analysis_table(AWS_Credentials: dict, **kwargs):
# if no environment is specified default to dev
env = kwargs.get('env', None)
if env == None:
env = 'DEV'
env = env.upper()
# set up RDS and S3 connections, engines, cursors
region = AWS_Credentials['region']
engine = create_postgres_engine(destination="AWS_PostGIS", env=env)
# flag that some records might be duplicate calls for the same incident
dupe_check_query = """
DROP TABLE IF EXISTS tmp.pulsepoint_dupe_check;
CREATE TABLE tmp.pulsepoint_dupe_check
AS (
SELECT DISTINCT a.* ,
case
when b.incident_id is null then 1
when a.num_units_responding = 0 and b.num_units_responding >0 then 0
when b.unit_status_transport > a.unit_status_transport then 0
when b.num_units_responding > a.num_units_responding then 0
when b.call_received_datetime < a.call_received_datetime then 0
else 1 end as KEEP_RECORD_FLAG
FROM source_data.pulsepoint a
LEFT JOIN source_data.pulsepoint b on a.incident_id <> b.incident_id
and date_part('day', a.call_received_datetime - b.call_received_datetime) = 0
and date_part('hour', a.call_received_datetime - b.call_received_datetime) = 0
and date_part('month', a.call_received_datetime - b.call_received_datetime) = 0
and abs(date_part('minute', a.call_received_datetime - b.call_received_datetime)) <=20
and ST_DWithin(a.geography, b.geography, 100)
and a.Agency_ID = b.Agency_ID
and (a.num_units_responding = 0 or a.unit_ids && b.unit_ids)
) ;
CREATE INDEX IF NOT EXISTS pulsepoint_dupe_check_geom_idx ON tmp.pulsepoint_dupe_check USING GIST (geography);
"""
# then join to the crashes table
crashes_join_query = """
DROP TABLE IF EXISTS tmp.pulsepoint_crash_join;
CREATE TABLE tmp.pulsepoint_crash_join
AS (SELECT *
FROM (
SELECT DISTINCT a.*
,concat(a.agency_id, a.incident_id) as Agency_Incident_ID
,b.objectid as Crash_Objectid
,b.geography as Crash_Geo
,b.total_injuries as Crash_Total_Injuries
,b.total_major_injuries as Crash_Total_Major_Injuries
,b.total_minor_injuries as Crash_Total_Minor_Injuries
,(b.bicycle_fatalities + b.pedestrian_fatalities + b.vehicle_fatalities) as Crash_Total_Fatalities
,b.bicycle_injuries as Crash_Bike_Injuries
,b.vehicle_injuries as Crash_Car_Injuries
,b.pedestrian_injuries as Crash_Ped_Injuries
,case when b.total_injuries is null or b.total_injuries < a.unit_status_transport then 1 else 0 end as injuries_mismatch
,ST_Distance(a.geography, b.geography) as Distance_To_Crash
,(b.reportdate at time zone 'America/New_York') - (a.CALL_RECEIVED_DATETIME at time zone 'America/New_York') as Time_Between_Crash_And_Report
,b.intersectionid as Crash_Intersection_ID
,b.block_objectid as Crash_Block_Objectid
,Row_Number() over (partition by a.incident_id, a.agency_id order by ST_Distance(a.geography, b.geography)) as Crash_Distance_Rank
,Row_Number() over (partition by a.incident_id, a.agency_id order by (b.reportdate at time zone 'America/New_York') - (a.CALL_RECEIVED_DATETIME at time zone 'America/New_York')) as Crash_Time_Rank
FROM tmp.pulsepoint_dupe_check a
LEFT JOIN analysis_data.dc_crashes_w_details b on ST_DWITHIN(a.geography, b.geography, 200)
AND cast(b.fromdate as date) =cast((call_received_datetime at time zone 'America/New_York') as date)
AND (a.CALL_RECEIVED_DATETIME at time zone 'America/New_York') < (b.reportdate at time zone 'America/New_York')
WHERE a.KEEP_RECORD_FLAG = 1
) tmp WHERE Crash_Distance_Rank = 1 and (incident_type in ('TC', 'TCE', 'TCS') or (agency_id = '16000' and incident_type in ('TC', 'TCS', 'TCE', 'RES')))
) ;
CREATE INDEX IF NOT EXISTS pulsepoint_crash_join_geom_idx ON tmp.pulsepoint_crash_join USING GIST (geography);
alter table tmp.pulsepoint_crash_join drop column KEEP_RECORD_FLAG;
alter table tmp.pulsepoint_crash_join drop column Crash_Distance_Rank;
"""
# First execute the table-specific queries
engine.execute(dupe_check_query)
print("dupe check query complete")
engine.execute(crashes_join_query)
print("join to crashes query complete")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='pulsepoint_nbh_ward',
from_schema='tmp',
from_table='pulsepoint_crash_join',
partition_by_field='Agency_Incident_ID')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='pulsepoint_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
next_tables = add_walkscore_info(engine=engine,
target_schema='tmp',
target_table='pulsepoint_walkscore',
from_schema=next_tables[0],
from_table=next_tables[1])
print("walkscore query complete")
next_tables = add_roadway_info(engine=engine,
target_schema='tmp',
target_table='pulsepoint_roadway_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='Agency_Incident_ID',
within_distance=100)
print("roadway info query complete")
next_tables = add_intersection_info(
engine=engine,
target_schema='tmp',
target_table='pulsepoint_intersection_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='Agency_Incident_ID',
within_distance=60)
print("intersection info query complete")
next_tables = is_national_park(engine=engine,
target_schema='tmp',
target_table='pulsepoint_national_park',
from_schema=next_tables[0],
from_table=next_tables[1])
print("national parks info query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='pulsepoint',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
from_table=next_tables[1])
print("walkscore query complete")
next_tables = add_roadway_info(engine=engine,
target_schema='tmp',
target_table='pulsepoint_roadway_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='Agency_Incident_ID',
within_distance=100)
print("roadway info query complete")
next_tables = add_intersection_info(
engine=engine,
target_schema='tmp',
target_table='pulsepoint_intersection_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='Agency_Incident_ID',
within_distance=60)
print("intersection info query complete")
next_tables = is_national_park(engine=engine,
target_schema='tmp',
target_table='pulsepoint_national_park',
from_schema=next_tables[0],
from_table=next_tables[1])
print("national parks info query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='pulsepoint',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
def generate_crashes_table(AWS_Credentials: dict, **kwargs):
# if no environment is specified default to dev
env = kwargs.get('env', None)
if env == None:
env = 'DEV'
env = env.upper()
# set up RDS and S3 connections, engines, cursors
region = AWS_Credentials['region']
engine = create_postgres_engine(destination="AWS_PostGIS", env=env)
# The queries that are specific to the crash data and are not run anywhere else
add_columns_query = """
DROP TABLE IF EXISTS tmp.crash_details;
CREATE TABLE tmp.crash_details
AS (
SELECT *
,CASE WHEN PERSONTYPE = 'Driver' AND AGE >=65 THEN 1 ELSE 0 END AS DRIVERS_OVER_65
,CASE WHEN PERSONTYPE = 'Driver' AND AGE <=25 THEN 1 ELSE 0 END AS DRIVERS_UNDER_25
,CASE WHEN PERSONTYPE = 'Pedestrian' AND AGE >=65 THEN 1 ELSE 0 END AS PEDS_OVER_65
,CASE WHEN PERSONTYPE = 'Pedestrian' AND AGE <=12 THEN 1 ELSE 0 END AS PEDS_UNDER_12
,CASE WHEN PERSONTYPE = 'Bicyclist' AND AGE >=65 THEN 1 ELSE 0 END AS BIKERS_OVER_65
,CASE WHEN PERSONTYPE = 'Bicyclist' AND AGE <=18 THEN 1 ELSE 0 END AS BIKERS_UNDER_18
,CASE WHEN PERSONTYPE = 'Driver' AND LICENSEPLATESTATE <> 'DC' AND LICENSEPLATESTATE <> ' None' THEN 1 ELSE 0 END AS OOS_VEHICLES
,CASE WHEN PERSONTYPE = 'Driver' AND INVEHICLETYPE = 'Passenger Car/automobile' THEN 1 ELSE 0 END AS NUM_CARS
,CASE WHEN PERSONTYPE = 'Driver' AND INVEHICLETYPE in ('Suv (sport Utility Vehicle)', 'Pickup Truck') THEN 1 ELSE 0 END AS NUM_SUVS_OR_TRUCKS
,CASE WHEN PERSONTYPE = 'Pedestrian' AND FATAL='Y' THEN 1 ELSE 0 END AS PED_FATALITIES
,CASE WHEN PERSONTYPE = 'Bicyclist' AND FATAL='Y'THEN 1 ELSE 0 END AS BICYCLE_FATALITIES
,CASE WHEN PERSONTYPE in ('Driver','Passenger') AND FATAL='Y' THEN 1 ELSE 0 END AS VEHICLE_FATALITIES
,CASE WHEN PERSONTYPE = 'Pedestrian' AND (MAJORINJURY='Y' OR MINORINJURY ='Y')THEN 1 ELSE 0 END AS PED_INJURIES
,CASE WHEN PERSONTYPE = 'Bicyclist' AND (MAJORINJURY='Y' OR MINORINJURY ='Y') THEN 1 ELSE 0 END AS BICYCLE_INJURIES
,CASE WHEN PERSONTYPE in ('Driver','Passenger') AND (MAJORINJURY='Y' OR MINORINJURY ='Y') THEN 1 ELSE 0 END AS VEHICLE_INJURIES
,CASE WHEN PERSONTYPE = 'Driver' AND TICKETISSUED ='Y' THEN 1 ELSE 0 END AS DRIVER_TICKETS
,CASE WHEN PERSONTYPE = 'Driver' AND SPEEDING ='Y' THEN 1 ELSE 0 END AS DRIVERS_SPEEDING
,CASE WHEN PERSONTYPE = 'Driver' AND IMPAIRED ='Y' THEN 1 ELSE 0 END AS DRIVERS_IMPAIRED
,CASE WHEN PERSONTYPE = 'Bicyclist' AND TICKETISSUED ='Y' THEN 1 ELSE 0 END AS BICYCLE_TICKETS
,CASE WHEN PERSONTYPE = 'Pedestrian' AND TICKETISSUED ='Y' THEN 1 ELSE 0 END AS PED_TICKETS
,CASE WHEN (MAJORINJURY='Y' OR MINORINJURY ='Y') THEN 1 ELSE 0 END AS TOTAL_INJURIES
,CASE WHEN MAJORINJURY='Y' THEN 1 ELSE 0 END AS TOTAL_MAJOR_INJURIES
,CASE WHEN MINORINJURY ='Y' THEN 1 ELSE 0 END AS TOTAL_MINOR_INJURIES
,CASE WHEN PERSONTYPE = 'Driver' THEN 1 ELSE 0 END AS TOTAL_VEHICLES
,CASE WHEN PERSONTYPE = 'Pedestrian' THEN 1 ELSE 0 END AS TOTAL_PEDESTRIANS
,CASE WHEN PERSONTYPE = 'Bicyclist' THEN 1 ELSE 0 END AS TOTAL_BICYCLISTS
FROM source_data.crash_details
)
"""
group_by_query = """
DROP TABLE IF EXISTS tmp.crash_details_agg;
CREATE TABLE tmp.crash_details_agg
AS (
SELECT
CRIMEID
,SUM(DRIVERS_OVER_65) AS DRIVERS_OVER_65
,SUM(DRIVERS_UNDER_25) AS DRIVERS_UNDER_25
,SUM(PEDS_OVER_65) AS PEDS_OVER_65
,SUM(PEDS_UNDER_12) AS PEDS_UNDER_12
,SUM(BIKERS_OVER_65) AS BIKERS_OVER_65
,SUM(BIKERS_UNDER_18) AS BIKERS_UNDER_18
,SUM(OOS_VEHICLES) AS OOS_VEHICLES
,SUM(NUM_CARS) AS NUM_CARS
,SUM(NUM_SUVS_OR_TRUCKS) AS NUM_SUVS_OR_TRUCKS
,SUM(PED_INJURIES) AS PEDESTRIAN_INJURIES
,SUM(BICYCLE_INJURIES) AS BICYCLE_INJURIES
,SUM(VEHICLE_INJURIES) AS VEHICLE_INJURIES
,SUM(PED_FATALITIES) AS PEDESTRIAN_FATALITIES
,SUM(BICYCLE_FATALITIES) AS BICYCLE_FATALITIES
,SUM(VEHICLE_FATALITIES) AS VEHICLE_FATALITIES
,SUM(DRIVER_TICKETS) AS DRIVER_TICKETS
,SUM(DRIVERS_SPEEDING) AS DRIVERS_SPEEDING
,SUM(DRIVERS_IMPAIRED) AS DRIVERS_IMPAIRED
,SUM(BICYCLE_TICKETS) AS BICYCLE_TICKETS
,SUM(PED_TICKETS) AS PED_TICKETS
,SUM(TOTAL_INJURIES) AS TOTAL_INJURIES
,SUM(TOTAL_MAJOR_INJURIES) AS TOTAL_MAJOR_INJURIES
,SUM(TOTAL_MINOR_INJURIES) AS TOTAL_MINOR_INJURIES
,SUM(TOTAL_VEHICLES) AS TOTAL_VEHICLES
,SUM(TOTAL_PEDESTRIANS) AS TOTAL_PEDESTRIANS
,SUM(TOTAL_BICYCLISTS) AS TOTAL_BICYCLISTS
,ARRAY_AGG(PERSONTYPE) AS PERSONTYPE_ARRAY
,ARRAY_AGG(INVEHICLETYPE) AS INVEHICLETYPE_ARRAY
,ARRAY_AGG(LICENSEPLATESTATE) AS LICENSEPLATESTATE_ARRAY
FROM tmp.crash_details
GROUP BY CRIMEID
) ;
create index crime_id on tmp.crash_details_agg (crimeid);
"""
join_query = """
DROP TABLE IF EXISTS tmp.crashes_join;
CREATE TABLE tmp.crashes_join
AS (
SELECT
a.OBJECTID
,a.CRIMEID
,a.REPORTDATE
,a.FROMDATE
,a.TODATE
,a.ADDRESS
,a.mpdlatitude
,a.mpdlongitude
,CASE WHEN b.CRIMEID IS NULL OR b.BICYCLE_INJURIES < (a.MAJORINJURIES_BICYCLIST + a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
THEN (a.MAJORINJURIES_BICYCLIST + a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
ELSE b.BICYCLE_INJURIES END AS BICYCLE_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.VEHICLE_INJURIES < (a.MAJORINJURIES_DRIVER+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MAJORINJURIESPASSENGER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER)
THEN (a.MAJORINJURIES_DRIVER+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MAJORINJURIESPASSENGER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER)
ELSE b.VEHICLE_INJURIES END AS VEHICLE_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.PEDESTRIAN_INJURIES < (a.MAJORINJURIES_PEDESTRIAN+ a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN)
THEN (a.MAJORINJURIES_PEDESTRIAN + a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN)
ELSE b.PEDESTRIAN_INJURIES END AS PEDESTRIAN_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.TOTAL_INJURIES < (a.MAJORINJURIES_PEDESTRIAN+ a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN
+a.MAJORINJURIES_DRIVER+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MAJORINJURIESPASSENGER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER
+a.MAJORINJURIES_BICYCLIST + a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
THEN (a.MAJORINJURIES_PEDESTRIAN+ a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN
+a.MAJORINJURIES_DRIVER+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MAJORINJURIESPASSENGER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER
+a.MAJORINJURIES_BICYCLIST + a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
ELSE b.TOTAL_INJURIES end as TOTAL_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.TOTAL_MAJOR_INJURIES < (a.MAJORINJURIES_PEDESTRIAN+
+a.MAJORINJURIES_DRIVER+a.MAJORINJURIESPASSENGER
+a.MAJORINJURIES_BICYCLIST)
THEN (a.MAJORINJURIES_PEDESTRIAN+a.MAJORINJURIES_DRIVER+a.MAJORINJURIESPASSENGER+a.MAJORINJURIES_BICYCLIST)
ELSE b.TOTAL_MAJOR_INJURIES end as TOTAL_MAJOR_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.TOTAL_MINOR_INJURIES < (a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN
+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER
+a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
THEN (a.MINORINJURIES_PEDESTRIAN + a.UNKNOWNINJURIES_PEDESTRIAN
+a.MINORINJURIES_DRIVER+a.UNKNOWNINJURIES_DRIVER+a.MINORINJURIESPASSENGER+a.UNKNOWNINJURIESPASSENGER
+a.MINORINJURIES_BICYCLIST + a.UNKNOWNINJURIES_BICYCLIST)
ELSE b.TOTAL_MINOR_INJURIES end as TOTAL_MINOR_INJURIES
,CASE WHEN b.CRIMEID IS NULL OR b.BICYCLE_FATALITIES < a.FATAL_BICYCLIST
THEN a.FATAL_BICYCLIST
ELSE b.BICYCLE_FATALITIES END AS BICYCLE_FATALITIES
,CASE WHEN b.CRIMEID IS NULL OR b.PEDESTRIAN_FATALITIES < a.FATAL_PEDESTRIAN
THEN a.FATAL_PEDESTRIAN
ELSE b.PEDESTRIAN_FATALITIES END AS PEDESTRIAN_FATALITIES
,CASE WHEN b.CRIMEID IS NULL OR b.VEHICLE_FATALITIES < (a.FATAL_DRIVER+a.FATALPASSENGER)
THEN (a.FATAL_DRIVER+a.FATALPASSENGER)
ELSE b.VEHICLE_FATALITIES END AS VEHICLE_FATALITIES
,CASE WHEN b.CRIMEID IS NULL or b.DRIVERS_IMPAIRED < a.DRIVERSIMPAIRED THEN a.DRIVERSIMPAIRED ELSE b.DRIVERS_IMPAIRED END AS DRIVERS_IMPAIRED
,CASE WHEN b.CRIMEID IS NULL or b.DRIVERS_SPEEDING < a.SPEEDING_INVOLVED THEN a.SPEEDING_INVOLVED ELSE b.DRIVERS_SPEEDING END AS DRIVERS_SPEEDING
,CASE WHEN b.CRIMEID IS NULL or b.TOTAL_VEHICLES < a.TOTAL_VEHICLES THEN a.TOTAL_VEHICLES ELSE b.TOTAL_VEHICLES END AS TOTAL_VEHICLES
,CASE WHEN b.CRIMEID IS NULL or b.TOTAL_BICYCLISTS < a.TOTAL_BICYCLES THEN a.TOTAL_BICYCLES ELSE b.TOTAL_BICYCLISTS END AS TOTAL_BICYCLISTS
,CASE WHEN b.CRIMEID IS NULL or b.TOTAL_PEDESTRIANS < a.TOTAL_PEDESTRIANS THEN a.TOTAL_PEDESTRIANS ELSE b.TOTAL_PEDESTRIANS END AS TOTAL_PEDESTRIANS
,b.DRIVERS_OVER_65
,b.DRIVERS_UNDER_25
,b.PEDS_OVER_65
,b.PEDS_UNDER_12
,b.BIKERS_OVER_65
,b.BIKERS_UNDER_18
,b.OOS_VEHICLES
,b.NUM_CARS
,b.NUM_SUVS_OR_TRUCKS
,b.DRIVER_TICKETS
,b.BICYCLE_TICKETS
,b.PED_TICKETS
,b.PERSONTYPE_ARRAY
,b.INVEHICLETYPE_ARRAY
,b.LICENSEPLATESTATE_ARRAY
,a.INTAPPROACHDIRECTION
,a.LOCATIONERROR
,a.LASTUPDATEDATE
,a.BLOCKKEY
,a.SUBBLOCKKEY
,ST_Force2D(a.geography::geometry) as geography
FROM source_data.crashes_raw a
LEFT JOIN tmp.crash_details_agg b on a.CRIMEID = b.CRIMEID
WHERE date_part('year', a.fromdate) >=2015
) ;
CREATE INDEX crashes_geom_idx ON tmp.crashes_join USING GIST (geography);
"""
# join in the pulsepoint info
pulsepoint_join_query = """
DROP TABLE IF EXISTS tmp.crash_pulsepoint_join;
CREATE TABLE tmp.crash_pulsepoint_join
AS (SELECT *
FROM (
SELECT DISTINCT a.*
,b.Agency_Incident_ID as pp_agency_incident_id
,b.unit_status_transport as pp_total_injuries
,b.transport_unit_is_amr as pp_total_minor_injuries
,b.transport_unit_is_non_amr as pp_total_major_injuries
,Row_Number() over (partition by a.objectid order by ST_Distance(a.geography, b.geography)) as PP_Call_Distance_Rank
,Row_Number() over (partition by a.objectid order by (a.reportdate at time zone 'America/New_York') - (b.CALL_RECEIVED_DATETIME at time zone 'America/New_York')) as PP_Call_Time_Rank
FROM tmp.crashes_join a
LEFT JOIN analysis_data.pulsepoint b on ST_DWITHIN(a.geography, b.geography, 200)
AND cast(fromdate as date) =cast((call_received_datetime at time zone 'America/New_York') as date)
AND (b.CALL_RECEIVED_DATETIME at time zone 'America/New_York') < (a.reportdate at time zone 'America/New_York')
) tmp WHERE PP_Call_Distance_Rank = 1
) ;
CREATE INDEX IF NOT EXISTS crash_pulsepoint_join_geom_idx ON tmp.crash_pulsepoint_join USING GIST (geography);
alter table tmp.crash_pulsepoint_join drop column PP_Call_Distance_Rank;
"""
# First execute the table-specific queries
engine.execute(add_columns_query)
print("add columns query complete")
engine.execute(group_by_query)
print("group by query complete")
engine.execute(join_query)
print("join query complete")
engine.execute(pulsepoint_join_query)
print("pulsepoint join query complete")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='crashes_nbh_ward',
from_schema='tmp',
from_table='crash_pulsepoint_join',
partition_by_field='objectid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='crashes_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
next_tables = add_walkscore_info(engine=engine,
target_schema='tmp',
target_table='crashes_walkscore',
from_schema=next_tables[0],
from_table=next_tables[1])
print("walkscore query complete")
next_tables = add_roadway_info(engine=engine,
target_schema='tmp',
target_table='crashes_roadway_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=0.001)
print("roadway info query complete")
next_tables = add_intersection_info(
engine=engine,
target_schema='tmp',
target_table='crashes_intersection_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=10)
print("intersection info query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='dc_crashes_w_details',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
next_tables = add_walkscore_info(engine=engine,
target_schema='tmp',
target_table='crashes_walkscore',
from_schema=next_tables[0],
from_table=next_tables[1])
print("walkscore query complete")
next_tables = add_roadway_info(engine=engine,
target_schema='tmp',
target_table='crashes_roadway_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=0.001)
print("roadway info query complete")
next_tables = add_intersection_info(engine=engine,
target_schema='tmp',
target_table='crashes_intersection_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=10)
print("intersection info query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='dc_crashes_w_details',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
LEFT JOIN analysis_data.roadway_blocks b on ST_DWithin(a.geography, b.geography, 0.001)
GROUP BY a.intersectionid, a.intersection_type, a.geography, a.street_names
) ;
CREATE INDEX tmp_intersection_points_road_types_index ON tmp.intersection_points_road_types USING GIST (geography);
"""
# First execute the table-specific queries
engine.execute(step1_query)
print("step1 query complete")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='intersection_points_nbh_ward',
from_schema='tmp',
from_table='intersection_points_road_types',
partition_by_field='intersectionid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='intersection_points_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='intersection_points',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
"""
# First execute the table-specific queries
engine.execute(step1_query)
print("step1 query complete")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='acs_2019_by_tract_nbh_ward',
from_schema='tmp',
from_table='acs_2019_by_tract',
partition_by_field='tract')
print("neighborhood-ward query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='acs_2019_by_tract',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
# specific to this dataset: assign some census tracts that contain large amts of parkland or water to the nearest populated neighborhood
engine.execute(
'update analysis_data.acs_2019_by_tract set nbh_cluster_names = \'Woodridge, Fort Lincoln, Gateway\', name = \'Cluster 24\' where census_tract = \'011100\';'
)
engine.execute(
'update analysis_data.acs_2019_by_tract set nbh_cluster_names = \'Takoma, Brightwood, Manor Park\', name = \'Cluster 17\' where census_tract = \'001803\';'
)
engine.execute(
'update analysis_data.acs_2019_by_tract set nbh_cluster_names = \'North Cleveland Park, Forest Hills, Van Ness\', name = \'Cluster 12\' where census_tract = \'001301\';'
)
,ST_SetSRID(ST_MakePoint(cast(xcoord as numeric), cast(ycoord as numeric)), 4326)::geography as geography
FROM source_data.metro_stations_daily_ridership
WHERE station is not null
)
"""
# First execute the table-specific queries
engine.execute(step1_query)
print("step1 query complete")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(
engine=engine,
target_schema='tmp',
target_table='metro_stations_ridership_nbh_ward',
from_schema='tmp',
from_table='metro_stations_ridership',
partition_by_field='objectid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='metro_stations_ridership_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='metro_stations_ridership',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)
def generate_moving_violations_table(AWS_Credentials: dict, **kwargs):
# if no environment is specified default to dev
env = kwargs.get('env', None)
if env == None:
env = 'DEV'
env = env.upper()
# set up RDS and S3 connections, engines, cursors
region = AWS_Credentials['region']
engine = create_postgres_engine(destination="AWS_PostGIS", env=env)
# First move all source data records to a temp table
step_1_query = """
CREATE TABLE IF NOT EXISTS tmp.moving_violations_need_geo as
SELECT * FROM source_data.moving_violations
WHERE geography IS NULL;
CREATE TABLE IF NOT EXISTS tmp.moving_violations_has_geo as
SELECT * FROM source_data.moving_violations
WHERE geography IS NOT NULL;
CREATE INDEX IF NOT EXISTS mv_location_index ON tmp.moving_violations_need_geo (location);
"""
engine.execute(step_1_query)
print("temp table created")
# geocode the locations
records = [
loc for (loc, ) in engine.execute(
"select distinct location from tmp.moving_violations_need_geo where geography is null limit 2000"
).fetchall()
]
print(len(records), " records passed to geocode function")
geocode_text(engine=engine,
records_to_geocode=records,
administrative_area='District of Columbia',
text_type='Moving Violations location')
# update lat and long values from new data
step_2_query = """
UPDATE tmp.moving_violations_need_geo
SET geography = source_data.geocoded_text.point_geography
FROM source_data.geocoded_text
WHERE source_data.geocoded_text.text = location
;
INSERT INTO tmp.moving_violations_has_geo
SELECT * FROM tmp.moving_violations_need_geo;
CREATE INDEX IF NOT EXISTS mv_geom_idx ON tmp.moving_violations_has_geo USING GIST (geography);
"""
engine.execute(step_2_query)
print("geo values updated")
# Then execute the same location-info queries (roadway, schools, neighborhoods) that apply to all analysis tables and create the final table
next_tables = add_location_info(engine=engine,
target_schema='tmp',
target_table='moving_violations_nbh_ward',
from_schema='tmp',
from_table='moving_violations_has_geo',
partition_by_field='objectid')
print("neighborhood-ward query complete")
next_tables = add_school_info(engine=engine,
target_schema='tmp',
target_table='moving_violations_schools',
from_schema=next_tables[0],
from_table=next_tables[1])
print("schools query complete")
next_tables = add_roadway_info(
engine=engine,
target_schema='tmp',
target_table='moving_violations_roadway_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=50)
print("roadway info query complete")
next_tables = add_intersection_info(
engine=engine,
target_schema='tmp',
target_table='moving_violations_intersection_info',
from_schema=next_tables[0],
from_table=next_tables[1],
partition_by_field='objectid',
within_distance=20)
print("intersection info query complete")
row_count = create_final_table(engine=engine,
target_schema='analysis_data',
target_table='moving_violations',
from_schema=next_tables[0],
from_table=next_tables[1])
print("final query complete with row count ", row_count)