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prox_list2.py
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prox_list2.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 8 13:56:51 2020
@author: pi
"""
#import geopandas as gpd
#from shapely.geometry import Point
from geopy.distance import geodesic
import pandas as pd
from sqlalchemy import create_engine, MetaData,Table, select,and_
engine = create_engine('sqlite:///spatialite_db/gnaf_may_2020.sqlite')
print(engine.table_names())
connection = engine.connect()
#tables
metadata = MetaData()
#ADDRESS_DETAIL -> ADDRESS_DETAIL_PID (in v_address_view)
#ADDRESS_MESH_BLOCK_2016 -> ADDRESS_MESH_BLOCK_2016_PID,ADDRESS_DETAIL_PID,MB_2016_PID
#MB_2016 -> MB_2016_PID,MB_2016_CODE
t_locality = Table('LOCALITY', metadata, autoload=True,
autoload_with=engine)
t_locality_point = Table('LOCALITY_POINT', metadata, autoload=True,
autoload_with=engine)
v_address_view= Table('ADDRESS_VIEW',metadata, autoload=True,
autoload_with=engine)
print('locality_point')
print(t_locality_point.columns.keys())
print('locality')
print(t_locality.columns.keys())
print('address view')
print(v_address_view.columns.keys())
ilocs_stmt = select([t_locality.columns.locality_pid,
t_locality.columns.locality_name,
t_locality.columns.state_pid,
t_locality_point.columns.latitude,
t_locality_point.columns.longitude,
t_locality.columns.locality_class_code])
ilocs_stmt = ilocs_stmt.where(
and_(t_locality.columns.locality_pid == t_locality_point.columns.locality_pid,
t_locality.columns.locality_class_code == 'I'))
ilocs_stmt = ilocs_stmt.order_by(t_locality.columns.locality_pid)
results = connection.execute(ilocs_stmt).fetchall()
df = pd.DataFrame(results)
# Set Column names
df.columns = results[0].keys()
#gdf = gpd.GeoDataFrame(
# df, geometry=gpd.points_from_xy(df.longitude, df.latitude,))
# Print the DataFrame
print(df)
closest=[]
v_stmt = select([v_address_view.columns.Address_Detail_PID,
v_address_view.columns.Latitude, v_address_view.columns.Longitude,
v_address_view.columns.AddressText])
#
for index,row in df.iterrows():
#limit by bounding box
v_stmt_loop = v_stmt.where(
and_(v_address_view.columns.Longitude.between(int(row.longitude)-3,
int(row.longitude)+3),
v_address_view.columns.Latitude.between(int(row.latitude)-3,
int(row.latitude)+3)))
smallest=99999
small_data=[0,0,0, 0,row.locality_pid,
row.locality_name,row.latitude, row.longitude,0]
for result in connection.execute(v_stmt_loop):
distance_km=geodesic((result.Latitude,result.Longitude),
(row.latitude,row.longitude)).km
#if distance_km < 60:
if distance_km < smallest:
smallest=distance_km
small_data=[result.Address_Detail_PID,result.AddressText.str.strip(),
result.Latitude,result.Longitude, row.locality_pid,
row.locality_name,row.latitude, row.longitude,
distance_km]
print(index,small_data)
closest.append(small_data)
connection.close()
print(closest)
df_closest=pd.DataFrame(closest,columns=['Address_Detail_PID','AddressText',
'address_latitude','address_longitude',
'locality_pid','locality_name',
'locality_latitude',
'locality_longitude','distance_km'])
df_closest.to_csv('closest3.csv')
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
# finished sql processing from GNAF Tables
#df_closest = pd.read_csv('closest3.csv')
agil_names_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/6947c036-6383-44f3-a867-0fa0c2a48d6b/download/agil_names20190208.csv"
agil_locations_url="https://data.gov.au/data/dataset/34b1c164-fbe8-44a0-84fd-467dba645aa7/resource/625e0a41-6a30-4c11-9a20-ac64ba5a1d1f/download/agil_locations20190208.csv"
agil_names = pd.read_csv(agil_names_url)
agil_locations = pd.read_csv(agil_locations_url)
agil_joined = pd.merge(agil_locations,agil_names,on='LCODE')
geom=[Point(xy) for xy in zip(agil_joined.LONGITUDE, agil_joined.LATITUDE)]
agil_joined_gdf=gpd.GeoDataFrame(agil_joined,geometry=geom,crs="EPSG:4283")
rafile = gpd.read_file('shape/RA_2016_AUST.shp')
agil_remote_gdf = gpd.sjoin(agil_joined_gdf, rafile[['RA_NAME16','geometry']][rafile.geometry!=None], how='left', op='within')
p_agil = agil_remote_gdf.loc[agil_remote_gdf['NFLAG']=='P',['LCODE','NAME','STATE','LONGITUDE','LATITUDE','RA_NAME16']]
a_agil = agil_remote_gdf[agil_remote_gdf.NFLAG=='A']
# stages 1 and 2 - convert to inputs to geospatial and base process
ref_agil= pd.merge(df_closest, p_agil, left_on='locality_name', right_on='NAME',
how='left')
ref_agil['locality_longlat_RA16'] = ref_agil.RA_NAME16.str.replace('.\(.*\)','')
process_dataset_1 = ref_agil[['LCODE','locality_pid','locality_name','locality_latitude',
'locality_longitude','locality_longlat_RA16','distance_km',
'Address_Detail_PID','AddressText','address_latitude',
'address_longitude']]
geom=[Point(xy) for xy in zip(process_dataset_1.address_longitude,
process_dataset_1.address_latitude)]
process_dataset_1_gdf = gpd.GeoDataFrame(process_dataset_1,geometry=geom,
crs="EPSG:4283")
process_dataset_2_gdf = gpd.sjoin(process_dataset_1_gdf,
rafile[['RA_NAME16','geometry']][rafile.geometry!=None],
how='left', op='within')
process_dataset_2_gdf['AddressText'] = process_dataset_2_gdf.AddressText.str.strip()
process_dataset_2_gdf['address_longlat_RA16'] = process_dataset_2_gdf.RA_NAME16.str.replace('.\(.*\)','')
process_dataset_2_gdf['RA16_Diff_Flag'] = process_dataset_2_gdf['address_longlat_RA16'] != process_dataset_2_gdf['locality_longlat_RA16']
process_dataset_2_gdf['RA16_Diff_Flag'] = process_dataset_2_gdf.RA16_Diff_Flag.apply(lambda x:int(x))
#stage 3 - drop working columns and output primary list
process_dataset_3 = process_dataset_2_gdf.drop(['geometry','index_right','RA_NAME16'], axis = 1)
process_dataset_3.to_csv('csv/new_closest_wout_alts.csv')
agil_joined_ref = agil_joined[['LCODE','NCODE','NAME','NFLAG']]
#stage 4 and 5 - replace with alternate name references and output complete list
process_dataset_4 = pd.merge(process_dataset_3, agil_joined_ref, on='LCODE',
how='left')
process_dataset_4['locality_name'] = process_dataset_4.NAME
process_dataset_5 = process_dataset_4.drop(['NCODE','NFLAG','NAME'],axis=1)
process_dataset_5.to_csv('csv/new_closest_w_alts.csv')