'''
uHoo analysis work

Tasks: 
1. Get data from database of air quality data. 
2. Get data from uhoo devices using auth
3. Get data from one device from the earliest timestamp. 
4. Create calendar heatmap for the one device for No2. 
5. Create heatmap for outdoor air quality for lat long of the 
6. Geocode devices and plot on map 
7. 
'''

'\nuHoo analysis work\n\nTasks: \n1. Get data from database of air quality data. \n2. Get data from uhoo devices using auth\n3. Get data from one device from the earliest timestamp. \n4. Create calendar heatmap for the one device for No2. \n5. Create heatmap for outdoor air quality for lat long of the \n6. Geocode devices and plot on map \n7. \n'

# Import libraries
import requests
import hashlib
import pandas as pd
import geopandas as gpd
import psycopg2, psycopg2.extras
import ipyleaflet
from ipyleaflet import Map, Marker, Popup
import shapely
from shapely.geometry import Point, Polygon
import json
import matplotlib.pyplot as plt
import seaborn as sns
from highcharts import Highchart
import datetime
import os


%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = (10.0, 10.0)

# GIS database connection
global gisdb
def postgres_test():
    global gisdb
    try:
        cur = gisdb.cursor()
        cur.execute('SELECT 1')
        cur.close()
        return gisdb
    except:
        gisdb = psycopg2.connect(host='',database="gis", user='p', p='postgres', cursor_factory=psycopg2.extras.RealDictCursor)
        return gisdb
        
def showalltables():
    cur = postgres_test().cursor()
    cur.execute('SELECT * FROM pg_catalog.pg_tables')
    data = cur.fetchall()
    #data = showalltables()
    tables = []
    for i in data:
        if i['schemaname'] == 'public':
            tables.append(i['tablename'])
            #print(i['tablename'])
    cur.close()
    return tables

def querydb(q):
    cur = postgres_test().cursor()
    cur.execute(q)
    data = cur.fetchall()
    cur.close()
    return data

 # Use rollback_db if you get any transaction related error
def rollback_db():
    curs = postgres_test().cursor()
    curs.execute("ROLLBACK")
    cur.close()
    gisdb.commit()

showalltables()

['gis_osm_pois_free_1',
 'spatial_ref_sys',
 'gis_osm_pofw_free_1',
 'gis_osm_pois_a_free_1',
 'indgebfunderingsproblematiek',
 'kadastralekaartv3_annotatie',
 'kadastralekaartv3_bebouwing',
 'archeologische_monumenten',
 'bag_ligplaats',
 'bag_openbareruimte',
 'bag_pand',
 'bag_verblijfsobject',
 'bag_verblijfsobject_poly',
 'bag_woonplaats',
 'beschermdenatuurmonumenten',
 'bestuurlijkegrenzen:gemeenten',
 'bestuurlijkegrenzen:landsgrens',
 'bestuurlijkegrenzen_provincies',
 'bodem50',
 'brobhr',
 'brocpt',
 'brogmw',
 'buurt_2017',
 'buurt_data_2018',
 'cbs_70262ned_bodemgebruik_per_gemeente',
 'cbs_84286ned_kerncijfers_wijken_en_buurten_2018',
 'cbs_84324ned_bodemgebruik_wijk_en_buurtcijfers_2015',
 'cbs_tableinfo',
 'cp_cadastralparcel',
 'energieklasseutiliteit',
 'energieklassewoningen',
 'gem_2017',
 'gis_osm_pofw_a_free_1',
 'kadastralekaartv3_bebouwing_poly',
 'kadastralekaartv3_kadastralegrens',
 'kadastralekaartv3_kadastralegrens_poly',
 'kadastralekaartv3_perceel',
 'memorix_rce_api_image_table',
 'n2000_20180827',
 'nationaleparken',
 'natura2000',
 'pbwetbodembescherming',
 'plu_besluitgebied_a_gml_gz',
 'plu_besluitgebied_p_gml_gz',
 'plu_besluitgebied_x_gml_gz',
 'plu_besluitsubvlak_a_gml_gz',
 'plu_besluitsubvlak_p_gml_gz',
 'plu_besluitsubvlak_x_gml_gz',
 'plu_besluitvlak_a_gml_gz',
 'plu_besluitvlak_p_gml_gz',
 'plu_besluitvlak_x_gml_gz',
 'plu_bestemmingsplangebied_gml_gz',
 'plu_bouwaanduiding_gml_gz',
 'plu_bouwvlak_gml_gz',
 'plu_dubbelbestemming_gml_gz',
 'plu_enkelbestemming_gml_gz',
 'plu_figuur_gml_gz',
 'plu_functieaanduiding_gml_gz',
 'plu_gebiedsaanduiding_gml_gz',
 'plu_lettertekenaanduiding_gml_gz',
 'plu_maatvoering_gml_gz',
 'plu_plangebied_gml_gz',
 'plu_plangebied_pcp_gml_gz',
 'plu_provinciaalcomplex_gml_gz',
 'plu_provinciaalgebied_gml_gz',
 'plu_provinciaalplangebied_gml_gz',
 'plu_structuurvisiecomplex_g_gml_gz',
 'plu_structuurvisiecomplex_p_gml_gz',
 'plu_structuurvisiecomplex_r_gml_gz',
 'plu_structuurvisiegebied_g_gml_gz',
 'plu_structuurvisiegebied_p_gml_gz',
 'plu_structuurvisiegebied_r_gml_gz',
 'plu_structuurvisieplangebied_g_gml_gz',
 'plu_structuurvisieplangebied_p_gml_gz',
 'plu_structuurvisieplangebied_r_gml_gz',
 'plu_structuurvisieverklaring_p_gml_gz',
 'postcode_2017',
 'ps_protectedsites_points',
 'ps_protectedsites_polygon',
 'soortendiversiteit',
 'rijksmonumenten',
 'soortendiversiteit_rl',
 'spoorwegen_station',
 'raster_draagkracht_totale_zetting',
 'stads_en_dorpsgezichten',
 'top10nl_inricht_el_station',
 'top10nl_renovatio_functioneelgebied_line',
 'top10nl_renovatio_functioneelgebied_point',
 'top10nl_renovatio_functioneelgebied_poly',
 'top10nl_renovatio_gebouw_line',
 'top10nl_renovatio_gebouw_point',
 'top10nl_renovatio_gebouw_poly',
 'top10nl_renovatio_geografischgebied_line',
 'top10nl_renovatio_geografischgebied_point',
 'top10nl_renovatio_geografischgebied_poly',
 'top10nl_renovatio_hoogte_line',
 'top10nl_renovatio_hoogte_point',
 'top10nl_renovatio_hoogte_poly',
 'top10nl_renovatio_inrichtingselement_line',
 'top10nl_renovatio_inrichtingselement_point',
 'top10nl_renovatio_inrichtingselement_poly',
 'top10nl_renovatio_plaats_line',
 'top10nl_renovatio_plaats_point',
 'top10nl_renovatio_plaats_poly',
 'top10nl_renovatio_registratiefgebied_line',
 'top10nl_renovatio_registratiefgebied_point',
 'top10nl_renovatio_registratiefgebied_poly',
 'top10nl_renovatio_spoorbaandeel_line',
 'top10nl_renovatio_spoorbaandeel_point',
 'top10nl_renovatio_spoorbaandeel_poly',
 'top10nl_renovatio_waterdeel_line',
 'top10nl_renovatio_waterdeel_point',
 'top10nl_renovatio_waterdeel_poly',
 'top10nl_renovatio_wegdeel_line',
 'top10nl_renovatio_wegdeel_point',
 'top10nl_renovatio_wegdeel_poly',
 'transport_bushaltes',
 'unesco_werelderfgoed',
 'wetlands2014',
 'wijk_2017',
 'raster_bodemdalingskaart_10de_percentiel_mm_per_jaar',
 'raster_bodemdalingskaart_mediaan_min_10de_percentiel_mm_per_jaa',
 'raster_bodemdalingskaart_mediaan_mm_per_jaar',
 'gem_vraagprijs_per_m2',
 'realeastate_woningmarkt_gemeenten',
 'bestuurlijkegrenzen_gemeentegrenzen',
 'adres',
 'bestuurlijkegrenzen_provinciegrenzen',
 'bestuurlijkegrenzen_landsgrens',
 'rijstroken',
 'rijstroken_vertices_pgr',
 'rijstroken_line',
 'postcode_pc6_2018',
 'woonplaats_actueel',
 'zwembaden_point',
 'luchtmeetnet_openapi_stations']

# Generate Authentication credentials
def getauth():
    login = ""
    password = ""
    
    salt1 = ""
    salt2 = ""
    concat = (salt1 + password + salt2).encode("utf-8")
    hashed_password  = hashlib.sha256(concat).hexdigest()
    
    params = {
        "username": login,
        "password": hashed_password        
    }
    
    return params

# Get Device List
params = getauth()
r = requests.post("https://api.uhooinc.com/v1/getdevicelist", data = params)
df = pd.DataFrame(r.json())
df

#Get Latest Data for a device
def get_latest_data(serialno):
    params = getauth()
    params['serialNumber'] = serialno    
    r = requests.post("https://api.uhooinc.com/v1/getlatestdata", data = params)
    return r.json()

# Test it
get_latest_data(df.head(1)['serialNumber'].values[0])

{'Temperature': '13.4',
 'Relative Humidity': '73.4',
 'PM2.5': '6.7',
 'TVOC': '74.0',
 'CO2': '504.0',
 'CO': '0.0',
 'Air Pressure': '1005.5',
 'Ozone': '6.9',
 'NO2': '0.1',
 'Timestamp': 1552649062,
 'DateTime': '2019-03-15 12:24'}

# Get Latest Data for multiple devices
df1 = df.head(100)
df2 = df1.apply(lambda x : get_latest_data(x['serialNumber']), result_type = 'expand', axis=1)
df3 = df1.join(df2)
#df3['datetime'] = pd.to_datetime(df3['Timestamp']*10**9)
df3['datetime'] = pd.to_datetime(df3['DateTime'], errors='ignore')
df3

# Get Hourly Data
params = getauth()
params['serialNumber'] = df.serialNumber.values[0]
params['prevDateTime'] = '2019-01-31 08:00:00'
r = requests.post("https://api.uhooinc.com/v1/gethourlydata", data = params)
_df = pd.DataFrame(r.json())
#_df['datetime'] = pd.to_datetime(_df['Timestamp']*10**9)
_df['datetime'] = pd.to_datetime(_df['DateTime'], errors='ignore')
print('Data for Device with Serial No:', params['serialNumber'])
_df

Data for Device with Serial No: 49ff76067580485655301867

# Get Daily Data
params = getauth()
params['serialNumber'] = df.serialNumber.values[0]
params['prevDateTime'] = '2018-10-1 08:00:00'
r = requests.post('https://api.uhooinc.com/v1/getdailydata', data = params)
_df = pd.DataFrame(r.json())
#_df['datetime'] = pd.to_datetime(_df['Timestamp']*10**9)
_df['datetime'] = pd.to_datetime(_df['DateTime'], errors='ignore')
print('Data for Device with Serial No:', params['serialNumber'])
_df

Data for Device with Serial No: 49ff76067580485655301867

df_measurements = df3.loc[:, 'Air Pressure':'Temperature']
df_m = df_measurements.drop(columns='DateTime')
df_measurements_int = df_m.apply(pd.to_numeric)

corrmat = df_measurements_int.corr()
f, ax = plt.subplots(figsize=(12,9))
sns.heatmap(corrmat, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 11})

<matplotlib.axes._subplots.AxesSubplot at 0x2381e38de10>

# Prepare for Plotting
plotdf = _df.copy()
plotdf.index = plotdf['datetime']
plotdf = plotdf[['Air Pressure', 'CO', 'CO2', 'NO2', 'Ozone', 'PM2.5', 'Relative Humidity', 'TVOC', 'Temperature']]
plotdf = plotdf.apply(pd.to_numeric)
plotdf

# Daily Data
plotdf.resample('D', level=0).mean()

# Code for Plotting Callender Map 
# Source Url: https://www.pythonprogramming.in/how-to-create-heatmap-calendar-using-numpy-and-matplotlib.html

import datetime as dt
import matplotlib.pyplot as plt
import numpy as np

def calendar_array(dates, data):
    i, j = zip(*[d.isocalendar()[1:] for d in dates])
    i = np.array(i) - min(i)
    j = np.array(j) - 1
    ni = max(i) + 1
 
    calendar = np.nan * np.zeros((ni, 7))
    calendar[i, j] = data
    return i, j, calendar
 
 
def calendar_heatmap(ax, df, variable):
    dates = df.index.to_series().dt.to_pydatetime()
    data = df[variable].values
    i, j, calendar = calendar_array(dates, data)
    im = ax.imshow(calendar, interpolation='none', cmap='summer')
    label_days(ax, dates, i, j, calendar)
    label_months(ax, dates, i, j, calendar)
    ax.figure.colorbar(im)
 
 
def label_days(ax, dates, i, j, calendar):
    ni, nj = calendar.shape
    day_of_month = np.nan * np.zeros((ni, 7))
    day_of_month[i, j] = [d.day for d in dates]
 
    for (i, j), day in np.ndenumerate(day_of_month):
        if np.isfinite(day):
            ax.text(j, i, int(day), ha='center', va='center')
 
    ax.set(xticks=np.arange(7),
           xticklabels=['M', 'T', 'W', 'R', 'F', 'S', 'S'])
    ax.xaxis.tick_top()
 
 
def label_months(ax, dates, i, j, calendar):
    month_labels = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul',
                             'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    months = np.array([d.month for d in dates])
    uniq_months = sorted(set(months))
    yticks = [i[months == m].mean() for m in uniq_months]
    labels = [month_labels[m - 1] for m in uniq_months]
    ax.set(yticks=yticks)
    ax.set_yticklabels(labels, rotation=90)

# Plot the heatmap
fig, ax = plt.subplots(figsize=(6, 10))
calendar_heatmap(ax, plotdf, 'NO2')
plt.show()

# Test Heat Map for a month
params = getauth()
params['serialNumber'] = df.serialNumber.values[0]
print('Data for Device with Serial No:', params['serialNumber'])
    
# Get Daily Data for a month
df_list = []
startdate = '01-10-2018' # 'dd-mm-yyyy' 
time = 135
i = 0
startdate_ = datetime.datetime.strptime(startdate, "%d-%m-%Y")
print("Getting Data From", startdate_.strftime("%Y-%m-%d"), " to ", (startdate_ + datetime.timedelta(days=time)).strftime("%Y-%m-%d") )
print("Progress", i, "/", time)
while i < time:
    a = startdate_ + datetime.timedelta(days=i)
    params['prevDateTime'] = a.strftime("%Y-%m-%d %H:%M:%S")
    r = requests.post('https://api.uhooinc.com/v1/getdailydata', data = params)
    if r.ok:
        _df = pd.DataFrame(r.json())
        df_list.append(_df)
    i+=1
    if ((i % 5) == 0):
        print("Progress", i, "/", time)
# All Done
tsdf = pd.concat(df_list)
#tsdf['datetime'] = pd.to_datetime(tsdf['Timestamp']*10**9)
tsdf['datetime'] = pd.to_datetime(tsdf['DateTime'], errors='ignore')
tsdf

Data for Device with Serial No: 49ff76067580485655301867
Getting Data From 2018-10-01  to  2019-02-13
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# Prepare for Plotting
print("Plotting Data From", startdate_.strftime("%Y-%m-%d"), " to ", (startdate_ + datetime.timedelta(days=time)).strftime("%Y-%m-%d"), "for Device with Serial No:", params['serialNumber']  )
plotdf = tsdf.copy()
plotdf.index = plotdf['datetime']
plotdf = plotdf[['Air Pressure', 'CO', 'CO2', 'NO2', 'Ozone', 'PM2.5', 'Relative Humidity', 'TVOC', 'Temperature']]
plotdf = plotdf.apply(pd.to_numeric)
# Daily Data
plotdf.resample('D', level=0).mean()
# Plot the heatmap
fig, ax = plt.subplots(figsize=(40, 40))
calendar_heatmap(ax, plotdf, 'NO2')
plt.show()

Plotting Data From 2018-10-01  to  2019-02-13 for Device with Serial No: 49ff76067580485655301867

# Clean Data to get pc6
df3['pc6'] = df3['deviceName'].str.split('/').str[0].str.strip().str.replace(' ', '')
df3['pc4'] = pd.to_numeric(df3['deviceName'].str.split(' ').str[0].str.strip() , errors='ceorce')
df3 = df3[~df3['pc4'].isnull()]

# Get pincode data
pincodelist = "', '".join(df3['pc6'].unique().astype(str))
query = "select postcode as pc6, wkb_geometry from postcode_pc6_2018 where postcode in ('"+ pincodelist +"') LIMIT 100"
con = postgres_test()
gdf = gpd.GeoDataFrame.from_postgis(query, con, geom_col='wkb_geometry' )
gdf.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x23820cbfda0>

gdf

# Geocode Devices
gdf['x'] = gdf.wkb_geometry.centroid.x
gdf['y'] = gdf.wkb_geometry.centroid.y
_gdf = gdf[['x', 'y', 'pc6']]
geo_df = df3.merge(_gdf, on='pc6')
crs = {'init': 'epsg:28992'}
geometry = [Point(xy) for xy in zip(geo_df.x, geo_df.y)]
geo_df = gpd.GeoDataFrame(geo_df, crs=crs, geometry=geometry)
geo_df = geo_df.to_crs({'init': 'epsg:4326'})
geo_df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x23820cd8e48>

# Convert layer to geojson
geo_df['Timestamp'] = geo_df['Timestamp'].astype(str)
geo_df['datetime'] = geo_df['datetime'].astype(str)

data = geo_df.to_json()
data = json.loads(data)
data

{'type': 'FeatureCollection',
 'features': [{'id': '0',
   'type': 'Feature',
   'properties': {'deviceName': '2951 BE / Project Ablasserdam',
    'macAddress': '606405acf1e5',
    'serialNumber': '49ff76067580485655301867',
    'Air Pressure': '1005.5',
    'CO': '0.0',
    'CO2': '504.0',
    'DateTime': '2019-03-15 12:24',
    'NO2': '0.1',
    'Ozone': '6.9',
    'PM2.5': '6.7',
    'Relative Humidity': '73.4',
    'TVOC': '74.0',
    'Temperature': '13.4',
    'Timestamp': '1552649062',
    'datetime': '2019-03-15 12:24:00',
    'pc6': '2951BE',
    'pc4': 2951.0,
    'x': 104986.14116720512,
    'y': 431261.36722261243},
   'geometry': {'type': 'Point',
    'coordinates': [4.66103620904929, 51.86765719595473]}},
  {'id': '1',
   'type': 'Feature',
   'properties': {'deviceName': '2951 BE / Project Ablasserdam',
    'macAddress': 'a81b6a9ae855',
    'serialNumber': '48ff72067580505235331667',
    'Air Pressure': '1004.0',
    'CO': '0.0',
    'CO2': '442.0',
    'DateTime': '2019-03-15 12:24',
    'NO2': '0.7',
    'Ozone': '9.0',
    'PM2.5': '57.4',
    'Relative Humidity': '50.7',
    'TVOC': '72.0',
    'Temperature': '17.8',
    'Timestamp': '1552649088',
    'datetime': '2019-03-15 12:24:00',
    'pc6': '2951BE',
    'pc4': 2951.0,
    'x': 104986.14116720512,
    'y': 431261.36722261243},
   'geometry': {'type': 'Point',
    'coordinates': [4.66103620904929, 51.86765719595473]}},
  {'id': '2',
   'type': 'Feature',
   'properties': {'deviceName': '2951 PM / Project Alblasserdam',
    'macAddress': '606405acd6e2',
    'serialNumber': '48ff72067580505252431467',
    'Air Pressure': '1003.5',
    'CO': '0.0',
    'CO2': '456.0',
    'DateTime': '2019-03-15 12:24',
    'NO2': '0.4',
    'Ozone': '9.6',
    'PM2.5': '6.5',
    'Relative Humidity': '52.9',
    'TVOC': '58.0',
    'Temperature': '18.7',
    'Timestamp': '1552649086',
    'datetime': '2019-03-15 12:24:00',
    'pc6': '2951PM',
    'pc4': 2951.0,
    'x': 105656.16709962736,
    'y': 431517.2654491878},
   'geometry': {'type': 'Point',
    'coordinates': [4.6707271772420995, 51.8700168821404]}},
  {'id': '3',
   'type': 'Feature',
   'properties': {'deviceName': '2953 AM / Project Alblasserdam',
    'macAddress': 'a81b6a9ad4ff',
    'serialNumber': '48ff6c067580505240571367',
    'Air Pressure': '1003.2',
    'CO': '0.0',
    'CO2': '773.0',
    'DateTime': '2019-03-15 12:24',
    'NO2': '1.0',
    'Ozone': '7.8',
    'PM2.5': '6.7',
    'Relative Humidity': '51.8',
    'TVOC': '130.0',
    'Temperature': '19.9',
    'Timestamp': '1552649062',
    'datetime': '2019-03-15 12:24:00',
    'pc6': '2953AM',
    'pc4': 2953.0,
    'x': 104442.31728432377,
    'y': 431414.4776872626},
   'geometry': {'type': 'Point',
    'coordinates': [4.653118439552964, 51.86898422920255]}},
  {'id': '4',
   'type': 'Feature',
   'properties': {'deviceName': '2951 HB / Project Alblasserdam',
    'macAddress': '606405acf51f',
    'serialNumber': '49ff6d067580485648301367',
    'Air Pressure': '1024.4',
    'CO': '0.0',
    'CO2': '1510.0',
    'DateTime': '2018-12-04 15:34',
    'NO2': '19.4',
    'Ozone': '8.7',
    'PM2.5': '10.8',
    'Relative Humidity': '58.3',
    'TVOC': '10.0',
    'Temperature': '20.3',
    'Timestamp': '1543934068',
    'datetime': '2018-12-04 15:34:00',
    'pc6': '2951HB',
    'pc4': 2951.0,
    'x': 105268.5902409369,
    'y': 431539.5627000045},
   'geometry': {'type': 'Point',
    'coordinates': [4.665096748820099, 51.87018282349947]}},
  {'id': '5',
   'type': 'Feature',
   'properties': {'deviceName': '2953 WB  / Project Alblasserdam',
    'macAddress': '606405acf6d4',
    'serialNumber': '30ff70064d48303544172543',
    'Air Pressure': '1003.7',
    'CO': '0.0',
    'CO2': '859.0',
    'DateTime': '2019-03-15 14:24',
    'NO2': '0.5',
    'Ozone': '4.9',
    'PM2.5': '30.9',
    'Relative Humidity': '52.4',
    'TVOC': '74.0',
    'Temperature': '19.8',
    'Timestamp': '1552649069',
    'datetime': '2019-03-15 14:24:00',
    'pc6': '2953WB',
    'pc4': 2953.0,
    'x': 104120.20380335854,
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    'Timestamp': '1552648224',
    'datetime': '2019-03-15 12:10:00',
    'pc6': '2951ER',
    'pc4': 2951.0,
    'x': 104788.64866542941,
    'y': 430894.55895891413},
   'geometry': {'type': 'Point',
    'coordinates': [4.658222367740549, 51.86434266254493]}},
  {'id': '56',
   'type': 'Feature',
   'properties': {'deviceName': '2954 BL / Project Alblasserdam',
    'macAddress': 'a81b6a9ae0be',
    'serialNumber': '48ff6c067580505251251867',
    'Air Pressure': '1003.9',
    'CO': '0.0',
    'CO2': '441.0',
    'DateTime': '2019-03-15 12:25',
    'NO2': '0.3',
    'Ozone': '9.3',
    'PM2.5': '8.2',
    'Relative Humidity': '47.8',
    'TVOC': '65.0',
    'Temperature': '19.3',
    'Timestamp': '1552649151',
    'datetime': '2019-03-15 12:25:00',
    'pc6': '2954BL',
    'pc4': 2954.0,
    'x': 104716.80345857094,
    'y': 431720.45885117375},
   'geometry': {'type': 'Point',
    'coordinates': [4.657058984969572, 51.87175914068789]}},
  {'id': '57',
   'type': 'Feature',
   'properties': {'deviceName': '2954 BE / Project Alblasserdam',
    'macAddress': '606405acb200',
    'serialNumber': '49ff70067580485650282067',
    'Air Pressure': '1004.2',
    'CO': '0.0',
    'CO2': '634.0',
    'DateTime': '2019-03-15 12:26',
    'NO2': '0.6',
    'Ozone': '5.0',
    'PM2.5': '16.8',
    'Relative Humidity': '48.3',
    'TVOC': '1156.0',
    'Temperature': '21.2',
    'Timestamp': '1552649174',
    'datetime': '2019-03-15 12:26:00',
    'pc6': '2954BE',
    'pc4': 2954.0,
    'x': 104490.61488863114,
    'y': 431739.9653917526},
   'geometry': {'type': 'Point',
    'coordinates': [4.6537719846594126, 51.871913999189175]}},
  {'id': '58',
   'type': 'Feature',
   'properties': {'deviceName': '2954 BE / Project Alblasserdam',
    'macAddress': 'a81b6a9ad930',
    'serialNumber': '48ff76067580505246451867',
    'Air Pressure': '1003.7',
    'CO': '0.0',
    'CO2': '536.0',
    'DateTime': '2019-03-15 12:23',
    'NO2': '15.6',
    'Ozone': '8.1',
    'PM2.5': '3.8',
    'Relative Humidity': '52.7',
    'TVOC': '1.0',
    'Temperature': '17.8',
    'Timestamp': '1552649010',
    'datetime': '2019-03-15 12:23:00',
    'pc6': '2954BE',
    'pc4': 2954.0,
    'x': 104490.61488863114,
    'y': 431739.9653917526},
   'geometry': {'type': 'Point',
    'coordinates': [4.6537719846594126, 51.871913999189175]}},
  {'id': '59',
   'type': 'Feature',
   'properties': {'deviceName': '2953 CN / Project Alblasserdam',
    'macAddress': '606405acb265',
    'serialNumber': '49ff6a067580485629221967',
    'Air Pressure': '1003.3',
    'CO': '0.0',
    'CO2': '1508.0',
    'DateTime': '2019-03-15 12:26',
    'NO2': '0.3',
    'Ozone': '8.2',
    'PM2.5': '33.2',
    'Relative Humidity': '56.4',
    'TVOC': '35.0',
    'Temperature': '19.5',
    'Timestamp': '1552649186',
    'datetime': '2019-03-15 12:26:00',
    'pc6': '2953CN',
    'pc4': 2953.0,
    'x': 103499.42966465857,
    'y': 431564.74737042096},
   'geometry': {'type': 'Point',
    'coordinates': [4.639406628142007, 51.870248450137154]}},
  {'id': '60',
   'type': 'Feature',
   'properties': {'deviceName': '2954 PC / Project Alblasserdam',
    'macAddress': '606405acf1a2',
    'serialNumber': '49ff72067580485628441767',
    'Air Pressure': '1004.2',
    'CO': '0.0',
    'CO2': '902.0',
    'DateTime': '2019-03-15 12:26',
    'NO2': '0.2',
    'Ozone': '7.2',
    'PM2.5': '33.5',
    'Relative Humidity': '47.0',
    'TVOC': '43.0',
    'Temperature': '20.0',
    'Timestamp': '1552649166',
    'datetime': '2019-03-15 12:26:00',
    'pc6': '2954PC',
    'pc4': 2954.0,
    'x': 104817.58294736255,
    'y': 431691.00094602647},
   'geometry': {'type': 'Point',
    'coordinates': [4.658526539645879, 51.87150346811622]}},
  {'id': '61',
   'type': 'Feature',
   'properties': {'deviceName': '2951 PN / Project Alblasserdam',
    'macAddress': 'a81b6a9ae5ea',
    'serialNumber': '48ff74067580505259392067',
    'Air Pressure': '1003.2',
    'CO': '0.0',
    'CO2': '442.0',
    'DateTime': '2019-03-15 12:26',
    'NO2': '10.8',
    'Ozone': '9.9',
    'PM2.5': '19.1',
    'Relative Humidity': '59.4',
    'TVOC': '3.0',
    'Temperature': '18.0',
    'Timestamp': '1552649187',
    'datetime': '2019-03-15 12:26:00',
    'pc6': '2951PN',
    'pc4': 2951.0,
    'x': 105663.41796120623,
    'y': 431455.0096880625},
   'geometry': {'type': 'Point',
    'coordinates': [4.670841357723621, 51.86945798569486]}},
  {'id': '62',
   'type': 'Feature',
   'properties': {'deviceName': '2969 BT / Project Alblasserdam',
    'macAddress': 'a81b6a9ae2ab',
    'serialNumber': '48ff75067580505219521767',
    'Air Pressure': '1019.5',
    'CO': '0.0',
    'CO2': '1349.0',
    'DateTime': '2018-12-18 12:17',
    'NO2': '0.7',
    'Ozone': '7.5',
    'PM2.5': '4.2',
    'Relative Humidity': '56.7',
    'TVOC': '41.0',
    'Temperature': '17.6',
    'Timestamp': '1545131875',
    'datetime': '2018-12-18 12:17:00',
    'pc6': '2969BT',
    'pc4': 2969.0,
    'x': 108248.81806906153,
    'y': 430049.60101183545},
   'geometry': {'type': 'Point',
    'coordinates': [4.7085689032818685, 51.857049384338055]}},
  {'id': '63',
   'type': 'Feature',
   'properties': {'deviceName': '2954 PB / Project Alblasserdam',
    'macAddress': '606405acf1bd',
    'serialNumber': '49ff76067580485637441767',
    'Air Pressure': '1003.9',
    'CO': '0.0',
    'CO2': '483.0',
    'DateTime': '2019-03-15 12:25',
    'NO2': '3.4',
    'Ozone': '10.3',
    'PM2.5': '3.7',
    'Relative Humidity': '52.8',
    'TVOC': '8.0',
    'Temperature': '19.2',
    'Timestamp': '1552649139',
    'datetime': '2019-03-15 12:25:00',
    'pc6': '2954PB',
    'pc4': 2954.0,
    'x': 104851.89883777537,
    'y': 431659.5552295856},
   'geometry': {'type': 'Point',
    'coordinates': [4.6590293591295335, 51.871223932761865]}},
  {'id': '64',
   'type': 'Feature',
   'properties': {'deviceName': '2953 GC / Project Alblasserdam',
    'macAddress': 'a81b6a9ae2f5',
    'serialNumber': '48ff6e067580505229081467',
    'Air Pressure': '1003.7',
    'CO': '0.0',
    'CO2': '764.0',
    'DateTime': '2019-03-15 12:25',
    'NO2': '0.4',
    'Ozone': '7.7',
    'PM2.5': '25.1',
    'Relative Humidity': '48.5',
    'TVOC': '57.0',
    'Temperature': '20.3',
    'Timestamp': '1552649159',
    'datetime': '2019-03-15 12:25:00',
    'pc6': '2953GC',
    'pc4': 2953.0,
    'x': 103394.00290096687,
    'y': 432111.10494611855},
   'geometry': {'type': 'Point',
    'coordinates': [4.637794178796906, 51.875149183247665]}},
  {'id': '65',
   'type': 'Feature',
   'properties': {'deviceName': '2951 EC / Project Alblasserdam',
    'macAddress': 'a81b6a9ae585',
    'serialNumber': '48ff6f067580505217131767',
    'Air Pressure': '1004.2',
    'CO': '0.0',
    'CO2': '522.0',
    'DateTime': '2019-03-15 12:25',
    'NO2': '0.5',
    'Ozone': '5.2',
    'PM2.5': '3.3',
    'Relative Humidity': '52.7',
    'TVOC': '64.0',
    'Temperature': '20.4',
    'Timestamp': '1552649142',
    'datetime': '2019-03-15 12:25:00',
    'pc6': '2951EC',
    'pc4': 2951.0,
    'x': 104384.9529356474,
    'y': 430878.7443331277},
   'geometry': {'type': 'Point',
    'coordinates': [4.652364197677537, 51.864164003148694]}},
  {'id': '66',
   'type': 'Feature',
   'properties': {'deviceName': '2951 WB  / Project Alblasserdam',
    'macAddress': '606405acb261',
    'serialNumber': '48ff73067580505240411367',
    'Air Pressure': '1003.8',
    'CO': '0.0',
    'CO2': '912.0',
    'DateTime': '2019-03-15 12:26',
    'NO2': '0.6',
    'Ozone': '8.9',
    'PM2.5': '28.8',
    'Relative Humidity': '47.6',
    'TVOC': '57.0',
    'Temperature': '21.0',
    'Timestamp': '1552649174',
    'datetime': '2019-03-15 12:26:00',
    'pc6': '2951WB',
    'pc4': 2951.0,
    'x': 105364.59710984299,
    'y': 430946.6375300503},
   'geometry': {'type': 'Point',
    'coordinates': [4.666575993547738, 51.86486233042584]}}]}

# Create a Map
maap = ipyleaflet.Map(
    center=(51.8703, 4.6702),
    zoom=12
) 

layer = ipyleaflet.GeoJSON(data=data)

def hover_handler(properties=None, event=None, id=None, type=None, coordinates=tuple):
    print(properties)
    
layer.on_click(hover_handler)

maap.add_layer(layer)
maap

# Get Station data
query = "SELECT * FROM luchtmeetnet_openapi_stations LIMIT 150"
con = postgres_test()
luchst_df = gpd.GeoDataFrame.from_postgis(query, con, geom_col='geom' )
luchst_df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1f76134cba8>

#Logic for get nearest station for all the points
def get_nearest_station_id(geom, df2, geometry_col):
    df2['distance'] = df2[geometry_col].distance(geom)
    return df2.sort_values(by=['distance'])[['number', 'distance']].values[0]

# Get Nearest Station
geo_df_ = geo_df.to_crs(epsg=28992)
luchst_df_ = luchst_df.to_crs(epsg=28992)
geo_df_[['luchst_number', 'luchst_distance']] = geo_df_.apply(lambda x: get_nearest_station_id(x['geometry'], luchst_df_, 'geom'), axis=1, result_type= 'expand')
geo_df = geo_df.join(geo_df_[['luchst_number', 'luchst_distance']])
geo_df

# Logic for Get data for station
def get_station_data(station_number):
    # Get All dates to get data for
    dates = []
    start = plotdf.index.to_series().min()
    #datetime.datetime.strptime(date1, '%Y-%m-%d')
    end = plotdf.index.to_series().max()
    #datetime.datetime.strptime(date2, '%Y-%m-%d')
    step = datetime.timedelta(days=7)
    while start <= end:
        #print (start.date())
        r = {
            'start': start,
            'end': start+step
        }
        dates.append(r)
        start+=step
    
    print(f'Getting data for {len(dates)} combinations of date')
    
    api_url = 'https://www.luchtmeetnet.nl/open_api/measurements'
    global api_params 
    api_params = {
        'start': '',
        'end': '',
        'station_number': '',
        'order_by': 'timestamp_measured',
        'order_by_direction': 'desc',
        'status': '',
    }
    
    #datastore
    df_list = []
    for r in dates:
        api_params['start'] = r['start']
        api_params['end'] = r['end']
        api_params['station_number'] = station_number
        #print(api_params)
        r = requests.get(api_url, params=api_params)
        if r.ok:
            try:
                response_json = r.json()
                data = response_json['data']
                last_page = response_json['pagination']['last_page']
                #print(last_page)
                df = pd.DataFrame(data)
                df_list.append(df)
            except Exception as e:
                print('Error:', e)
    # Merge data
    df = pd.concat(df_list)
    return df

# Get unique station no to get data for
luchst_df_ = luchst_df[luchst_df['number'].isin(geo_df['luchst_number'].unique())]
luchst_df_

# Data store
luchst_data = []
stations = list(luchst_df_['number'])
for st in stations:
    df = get_station_data(st)
    luchst_data.append(df)
if len(luchst_data) > 0: 
    luchst_data_df_ = pd.concat(luchst_data)
    
luchst_data_df_

Getting data for 20 combinations of date

# Make Binary Variables
luchst_data_df = luchst_data_df_.pivot_table(index=['station_number','timestamp_measured'], columns='formula').reset_index()

# Flatten Columns
luchst_data_df.columns = luchst_data_df.columns.to_series().str.join('_')
luchst_data_df

# PLot



stations = luchst_data_df['station_number_'].unique()

for st in stations:
    print("Plotting station data for station:", st)
    luchst_plotdf = luchst_data_df[luchst_data_df['station_number_'] == st]
    # Make Datetime index
    luchst_plotdf.index = pd.to_datetime(luchst_plotdf['timestamp_measured_'], errors='ignore')
    luchst_plotdf = luchst_plotdf[luchst_plotdf.columns[2:]]

    luchst_plotdf = luchst_plotdf.apply(pd.to_numeric)
    # Daily Data
    luchst_plotdf.resample('D', level=0).mean()
    # Plot the heatmap
    fig, ax = plt.subplots(figsize=(40, 40))
    calendar_heatmap(ax, luchst_plotdf, 'value_NO2')
    plt.show()

Plotting station data for station: NL01908

c:\users\apsac243\envs\geo\lib\site-packages\ipykernel_launcher.py:20: FutureWarning: The default of the 'keep_tz' keyword will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.

## Does not work

# MAp with popup

import ipywidgets as ipyw

maap = ipyleaflet.Map(
    center=(51.8703, 4.6702),
    zoom=12
) 

label = ipyw.Label(layout=ipyw.Layout(width='100%'))

message1 = ipyw.HTML()
message1.value = "Try clicking the marker!"

layer = ipyleaflet.GeoJSON(data=data)

def hover_handler(properties=None, event=None, id=None, type=None, coordinates=tuple):
    print(properties)
    #print(type)
    #print(coordinates)
    label.value = json.dumps(properties)
    try:
        i=1+'1'
    except:
        pass
    
layer.on_click(hover_handler)

maap.add_layer(layer)


ipyw.VBox([maap, label])

# Setup API for air quality data

# Import libraries
from rasterstats import zonal_stats

query = 'SELECT * FROM bestuurlijkegrenzen_gemeentegrenzen LIMIT 1'
query = 'SELECT code, gemeentenaam, wkb_geometry FROM bestuurlijkegrenzen_gemeentegrenzen LIMIT 20'
con = postgres_test()
gdf = gpd.GeoDataFrame.from_postgis(query, con, geom_col='wkb_geometry' )
gdf.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1c7adb951d0>

a = gdf.wkb_geometry
a[5]

lucht__actueel_pm25 = os.path.join('data', 'lucht__actueel_pm25.tif')
zonal_stats([a[0]], lucht__actueel_pm25)

[{'min': 5.66, 'max': 8.0, 'mean': 6.2496617687829215, 'count': 3607}]

# This is Renovatio API

def airQualityApi(entities=None, level=None, parameters='pm10, pm25, no2, o3, lki', years='actueel', geometry='false'):
    entities = entities.strip().lower()
    level = level.strip().lower()
    parameters = parameters.strip().lower()
    years = years.strip().lower()
    geometry = geometry.strip().lower()
    if level == 'gementee':
        gem_codes = []
        gem_names = []
        if entities == '' or entities == 'all' or entities is None:
            whereQuery = ""
        else:
            entities = entities.split(",")
            entities = [x.strip() for x in entities]
            for enti in entities:
                try:
                    int(enti)
                    gem_codes.append(enti)
                except:
                    gem_names.append(enti)
            whereQuery = "WHERE "
            if len(gem_codes) > 0:
                gem_codes = ["'"+ str(x) +"'" for x in gem_codes]
                whereQuery+= f'code IN ({",".join(gem_codes)})'
            if len(gem_names) > 0:
                gem_names = ["'"+x+"'" for x in gem_names]
                whereQuery+= f' OR gemeentenaam::citext IN ({",".join(gem_names)})'
        print(whereQuery)        
        query = f'''SELECT code, gemeentenaam, ST_Simplify(wkb_geometry, 50)as wkb_geometry
                    FROM bestuurlijkegrenzen_gemeentegrenzen 
                    {whereQuery}
                    LIMIT 20
                '''
        print(query)
        con = postgres_test()
        gdf = gpd.GeoDataFrame.from_postgis(query, con, geom_col='wkb_geometry' )
        gdf['airquality'] = gdf['wkb_geometry'].apply(lambda x: getAirQuality(x, parameters, years))
        if geometry == 'false':
            gdf.drop(['wkb_geometry'], axis=1, inplace=True)
            
        #air_quality = getAirQuality(gdf.wkb_geometry, parameters)
        
        #gdf['data'] = pd.Series(air_quality)
        return gdf

def getAirQuality(geometry, parameters, years):
    
    # create object to store returning data
    airQuality = {}
    airQuality['Errors'] = []
    
    # check supplied parameters otherwise raise error
    valid_parameters = ['lki', 'no2', 'o3', 'pm10', 'pm25']
    parameters = parameters.split(",")
    parameters = [x.strip() for x in parameters]
    for parameter in parameters:
        if parameter not in valid_parameters:
            parameters.remove(parameter)
            airQuality['Errors'].append( f'Sorry Buddy but {parameter} passed in parameters is an invalid value')
    
    # Check years
    years = years.split(",")
    years = [x.strip() for x in years]
    for year in years:
        if year != 'actueel':
            try:
                year_ = int(year)
                if year_ > datetime.datetime.now().year:
                    raise Exception('Invalid Year')
            except Exception as error:
                years.remove(year)
                airQuality['Errors'].append( f'Sorry buddy but {year} passed in years is an invalid value error: {str(error)}')
    
    # Get Actual Data
    for year in years:
        airQuality[year] = {}
        for parameter in parameters:
            if year == 'actueel':
                lucht_file = os.path.join('data', f'lucht__{year}_{parameter}.tif')
            else:
                lucht_file = os.path.join('data', f'gcn__conc_{parameter}_{year}.tif')
            if os.path.isfile(lucht_file):
                pm25_ZonalStatsData = zonal_stats(geometry, lucht_file)
                airQuality[year][parameter] = pm25_ZonalStatsData[0]                
            else:
                airQuality['Errors'].append(f'Sorry Buddy no data found for parameter {parameter} in year {year}')
    return (airQuality)

gdf = airQualityApi(entities='1621,Achtkarspelen,0356,0654,Wassenaar', level='gementee', geometry='true')
gdf

WHERE code IN ('1621','0356','0654') OR gemeentenaam::citext IN ('achtkarspelen','wassenaar')
SELECT code, gemeentenaam, ST_Simplify(wkb_geometry, 50)as wkb_geometry
                    FROM bestuurlijkegrenzen_gemeentegrenzen 
                    WHERE code IN ('1621','0356','0654') OR gemeentenaam::citext IN ('achtkarspelen','wassenaar')
                    LIMIT 20

gdf.to_json()

'{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", "properties": {"code": "1621", "gemeentenaam": "Lansingerland", "airquality": {"Errors": [], "actueel": {"pm10": {"min": 12.68, "max": 23.71, "mean": 13.590798553144129, "count": 3594}, "pm25": {"min": 5.66, "max": 8.0, "mean": 6.2498469671675005, "count": 3594}, "no2": {"min": 25.69, "max": 117.98, "mean": 36.52251530328326, "count": 3594}, "o3": {"min": 32.8, "max": 40.1, "mean": 37.78208402893711, "count": 3594}, "lki": {"min": 3.87, "max": 5.45, "mean": 4.125239287701725, "count": 3594}}}}, "geometry": {"type": "MultiPolygon", "coordinates": [[[[94441.604, 449656.069], [92566.379, 449663.743], [91956.968, 449135.89], [91867.02, 448962.262], [91167.573, 448610.746], [91313.955, 448038.793], [91250.75, 447881.156], [91453.491, 447548.088], [91021.387, 446902.305], [91132.277, 446694.564], [91259.384, 446659.623], [91244.121, 446388.861], [90626.97, 446206.912], [90835.333, 445844.578], [90441.729, 445308.721], 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lucht__actueel_pm25 = os.path.join('data', 'lucht__actueel_pm25.tif')
zonal_stats(gdf.wkb_geometry, lucht__actueel_pm25)

[{'min': 5.66, 'max': 8.0, 'mean': 6.2498469671675005, 'count': 3594},
 {'min': 6.07, 'max': 7.4, 'mean': 6.806826747720366, 'count': 1645},
 {'min': 2.94, 'max': 4.18, 'mean': 3.268298767658551, 'count': 6654},
 {'min': 4.56, 'max': 6.35, 'mean': 4.97082376841036, 'count': 9845},
 {'min': 5.5, 'max': 7.35, 'mean': 6.100602672484318, 'count': 3667}]

	deviceName	macAddress	serialNumber
0	2951 BE / Project Ablasserdam	606405acf1e5	49ff76067580485655301867
1	2951 PM / Project Alblasserdam	606405acd6e2	48ff72067580505252431467
2	2953 AM / Project Alblasserdam	a81b6a9ad4ff	48ff6c067580505240571367
3	2951 BE / Project Ablasserdam	a81b6a9ae855	48ff72067580505235331667
4	2951 HB / Project Alblasserdam	606405acf51f	49ff6d067580485648301367
5	2953 WB / Project Alblasserdam	606405acf6d4	30ff70064d48303544172543
6	2953 BB / Project Alblasserdam	606405acf1d7	49ff6c067580485635281367
7	Gemeentehuis Alblasserdam	a81b6a9addf1	48ff68067580505216271467
8	2952 AH / Project Alblasserdam	606405ace2b3	49ff6c067580485629261367
9	2953 HC / Project Alblasserdam	606405acf440	49ff68067580485646301367
10	2951 HH / Project Alblasserdam	606405ace741	49ff71067580485651331367
11	2951 JG / Project Alblasserdam	606405acf78e	49ff72067580485655441767
12	2953 XT / Project Alblasserdam	606405acae24	49ff72067580485623351367
13	2953 XD / Project Alblasserdam	606405acf1d1	48ff76067580505240431367
14	2951 VH / Project Alblasserdam	a81b6a9ae594	49ff76067580485649291867
15	2953 BE / Project Alblasserdam	606405acf459	49ff74067580485629291367
16	2952 AK / Project Alblasserdam	606405acf1a7	49ff72067580485612381667
17	2951 AG / Project Alblasserdam	606405ace2c2	49ff72067580485617291367
18	2953 WB / Project Alblasserdam	606405acb241	48ff6e067580505240551367
19	2951 JG / Project Alblasserdam	606405acb237	48ff6e067580505237541367
20	2952 AK / Project Alblasserdam	a81b6a9ad4ed	48ff76067580505238401467
21	2951 XV / Project Alblasserdam	606405acf32b	48ff6e067580505232341967
22	2954 BP / Project Alblasserdam	606405acf502	49ff72067580485658331367
23	2951 XW / Project Alblasserdam	a81b6a9ae0a9	48ff74067580505240471367
24	2951 JG 92 / Project Alblasserdam	606405acf40a	49ff79067580485628351367
25	2951 PK / Project Alblasserdam	606405acb262	49ff72067580485618491667
26	2953 HC / Project Alblasserdam	a81b6a9ae0df	48ff70067580505251401867
27	2951 VB / Project Alblasserdam	606405acf308	49ff74067580485629431667
28	2951 VN / Project Alblasserdam	606405acf34a	49ff73067580485647491667
29	2951 VN / Project Alblasserdam	a81b6a9ad967	48ff72067580505244081967
...	...	...	...
48	2953 HB / Project Alblasserdam	a81b6a9ae0f4	49ff6e067580485632491667
49	2951 JD / Project Alblasserdam	606405acafe0	49ff70067580485654281867
50	2953 HA /Project Alblasserdam	606405acb43e	49ff73067580485638461767
51	2953 XA / Project Alblassertdam	606405acb0c3	49ff72067580485624431667
52	2951 PA / Project Alblasserdam	606405acf337	48ff75067580505248151667
53	2951 JG / Project Alblasserdam	a81b6a9aeb50	49ff70067580485649461767
54	2951 JG 20 / Project Alblasserdam	a81b6a9ae805	48ff74067580505222602067
55	2951 DB / Project Alblasserdam	606405acad38	49ff73067580485625391967
56	2951 GG / Project Alblasserdam	606405ace561	48ff74067580505237491367
57	2951 ER / Project Alblasserdam	606405acad52	49ff6e067580485633281467
58	2954 BL / Project Alblasserdam	a81b6a9ae0be	48ff6c067580505251251867
59	2951HD / Project Alblasserdam	606405acf300	49ff6c067580485647341767
60	2954 BE / Project Alblasserdam	606405acb200	49ff70067580485650282067
61	2953 CN / Project Alblasserdam	606405acb265	49ff6a067580485629221967
62	2954 PC / Project Alblasserdam	606405acf1a2	49ff72067580485628441767
63	2951 PN / Project Alblasserdam	a81b6a9ae5ea	48ff74067580505259392067
64	2954 BE / Project Alblasserdam	a81b6a9ad930	48ff76067580505246451867
65	2969 BT / Project Alblasserdam	a81b6a9ae2ab	48ff75067580505219521767
66	2953HC / Project Alblasserdam	606405acb27c	49ff76067580485632441667
67	2952AB / ProjectAlblasserdam	a81b6a9aeb48	48ff71067580505236321467
68	2954 PB / Project Alblasserdam	606405acf1bd	49ff76067580485637441767
69	2953 GC / Project Alblasserdam	a81b6a9ae2f5	48ff6e067580505229081467
70	2951 HC Ablasserdam	606405acf537	49ff70067580485657291867
71	2951 JG Ablasserdam	606405ace2a8	49ff6a067580485633331367
72	2951 EC / Project Alblasserdam	a81b6a9ae585	48ff6f067580505217131767
73	2953 XB Alblasserdam	a81b6a9aee3a	48ff70067580505228482067
74	2951 WB / Project Alblasserdam	606405acb261	48ff73067580505240411367
75	2954BJ Alblasserdam	606405acf434	49ff74067580485632291867
76	2951 VH / Project Alblasserdam	606405a4462c	53ff73067788545138430567
77	2954AH / ProjectAlblasserdam	a81b6a9aeb34	48ff76067580505219111367

	Air Pressure	CO	CO2	DateTime	NO2	Ozone	PM2.5	Relative Humidity	TVOC	Temperature	Timestamp	datetime
0	997.9	0.0	576.0	2019-01-31 08:00	43.3	10.3	9.4	45.2	3.0	19.8	1548918009	2019-01-31 08:00:00
1	997.9	0.0	581.0	2019-01-31 08:01	43.1	10.3	7.6	45.3	3.0	19.8	1548918068	2019-01-31 08:01:00
2	997.9	0.0	603.0	2019-01-31 08:02	42.8	10.3	18.1	45.4	3.0	19.8	1548918128	2019-01-31 08:02:00
3	997.9	0.0	588.0	2019-01-31 08:03	43.0	10.3	12.4	45.3	0.0	19.8	1548918187	2019-01-31 08:03:00
4	997.9	0.0	568.0	2019-01-31 08:04	42.6	10.2	18.1	45.3	0.0	19.9	1548918247	2019-01-31 08:04:00
5	997.9	0.0	578.0	2019-01-31 08:05	42.6	10.2	7.6	45.3	0.0	19.9	1548918306	2019-01-31 08:05:00
6	998.0	0.0	576.0	2019-01-31 08:06	42.2	10.2	6.2	45.2	1.0	19.9	1548918366	2019-01-31 08:06:00
7	998.0	0.0	571.0	2019-01-31 08:07	42.0	10.2	9.1	45.2	1.0	19.9	1548918426	2019-01-31 08:07:00
8	997.9	0.0	582.0	2019-01-31 08:08	42.0	10.2	7.7	45.0	1.0	19.9	1548918485	2019-01-31 08:08:00
9	997.9	0.0	560.0	2019-01-31 08:09	42.4	10.2	14.9	44.8	2.0	19.9	1548918545	2019-01-31 08:09:00
10	997.9	0.0	532.0	2019-01-31 08:10	42.4	10.2	5.5	45.1	2.0	19.9	1548918604	2019-01-31 08:10:00
11	998.0	0.0	537.0	2019-01-31 08:11	42.4	10.2	6.8	44.9	2.0	20.0	1548918664	2019-01-31 08:11:00
12	997.9	0.0	569.0	2019-01-31 08:12	42.3	10.2	5.8	44.9	2.0	20.0	1548918723	2019-01-31 08:12:00
13	998.0	0.0	564.0	2019-01-31 08:13	40.6	10.1	20.7	45.0	2.0	20.0	1548918783	2019-01-31 08:13:00
14	998.0	0.0	559.0	2019-01-31 08:14	42.3	10.2	26.8	45.0	3.0	20.1	1548918842	2019-01-31 08:14:00
15	997.9	0.0	546.0	2019-01-31 08:15	41.7	10.2	17.2	44.9	2.0	20.1	1548918902	2019-01-31 08:15:00
16	998.0	0.0	548.0	2019-01-31 08:16	41.7	10.2	9.1	45.0	3.0	20.2	1548918961	2019-01-31 08:16:00
17	998.0	0.0	572.0	2019-01-31 08:17	41.0	10.1	6.1	44.9	3.0	20.3	1548919021	2019-01-31 08:17:00
18	998.0	0.0	580.0	2019-01-31 08:18	41.1	10.1	6.2	45.1	3.0	20.3	1548919080	2019-01-31 08:18:00
19	998.0	0.0	583.0	2019-01-31 08:19	40.6	10.1	24.5	45.0	0.0	20.3	1548919140	2019-01-31 08:19:00
20	998.0	0.0	602.0	2019-01-31 08:19	40.6	10.1	5.9	45.0	3.0	20.3	1548919199	2019-01-31 08:19:00
21	998.0	0.0	619.0	2019-01-31 08:20	40.3	10.1	6.3	45.2	3.0	20.3	1548919259	2019-01-31 08:20:00
22	997.9	0.0	655.0	2019-01-31 08:21	40.2	10.1	8.8	45.2	4.0	20.3	1548919318	2019-01-31 08:21:00
23	998.0	0.0	644.0	2019-01-31 08:22	39.9	10.0	20.4	45.0	0.0	20.3	1548919378	2019-01-31 08:22:00
24	998.0	0.0	629.0	2019-01-31 08:23	40.0	10.1	29.1	45.2	0.0	20.3	1548919437	2019-01-31 08:23:00
25	998.0	0.0	661.0	2019-01-31 08:24	39.9	10.1	16.4	45.3	0.0	20.4	1548919497	2019-01-31 08:24:00
26	998.0	0.0	668.0	2019-01-31 08:25	37.0	9.9	8.4	45.3	0.0	20.4	1548919556	2019-01-31 08:25:00
27	998.1	0.0	646.0	2019-01-31 08:26	39.6	10.0	8.4	45.4	0.0	20.3	1548919616	2019-01-31 08:26:00
28	998.1	0.0	637.0	2019-01-31 08:27	40.3	10.1	10.5	45.4	0.0	20.3	1548919675	2019-01-31 08:27:00
29	998.1	0.0	665.0	2019-01-31 08:28	40.1	10.1	11.0	45.3	0.0	20.3	1548919735	2019-01-31 08:28:00
...	...	...	...	...	...	...	...	...	...	...	...	...
31	998.1	0.0	671.0	2019-01-31 08:30	39.9	10.1	6.3	45.4	1.0	20.4	1548919854	2019-01-31 08:30:00
32	998.1	0.0	691.0	2019-01-31 08:31	33.4	9.6	8.6	45.5	1.0	20.4	1548919913	2019-01-31 08:31:00
33	998.0	0.0	692.0	2019-01-31 08:32	39.7	10.1	21.4	45.4	1.0	20.4	1548919973	2019-01-31 08:32:00
34	998.1	0.0	677.0	2019-01-31 08:33	39.2	10.0	19.8	45.5	1.0	20.4	1548920032	2019-01-31 08:33:00
35	998.1	0.0	673.0	2019-01-31 08:34	39.1	10.0	21.6	45.4	1.0	20.4	1548920092	2019-01-31 08:34:00
36	998.1	0.0	681.0	2019-01-31 08:35	38.9	10.0	19.2	45.5	1.0	20.4	1548920151	2019-01-31 08:35:00
37	998.1	0.0	705.0	2019-01-31 08:36	39.1	10.0	6.1	45.5	2.0	20.4	1548920211	2019-01-31 08:36:00
38	998.1	0.0	710.0	2019-01-31 08:37	38.9	10.0	8.0	45.4	2.0	20.5	1548920270	2019-01-31 08:37:00
39	998.1	0.0	682.0	2019-01-31 08:38	36.5	9.8	11.2	45.4	2.0	20.4	1548920330	2019-01-31 08:38:00
40	998.2	0.0	662.0	2019-01-31 08:39	38.8	10.0	7.3	45.5	2.0	20.4	1548920389	2019-01-31 08:39:00
41	998.1	0.0	680.0	2019-01-31 08:40	38.9	10.0	5.8	45.4	2.0	20.5	1548920449	2019-01-31 08:40:00
42	998.1	0.0	696.0	2019-01-31 08:41	38.9	10.0	14.5	45.4	3.0	20.5	1548920508	2019-01-31 08:41:00
43	998.1	0.0	697.0	2019-01-31 08:42	38.9	10.0	15.3	45.4	3.0	20.5	1548920568	2019-01-31 08:42:00
44	998.2	0.0	725.0	2019-01-31 08:43	38.7	10.0	13.9	45.4	3.0	20.5	1548920627	2019-01-31 08:43:00
45	998.2	0.0	742.0	2019-01-31 08:44	38.5	10.0	11.1	45.4	3.0	20.5	1548920687	2019-01-31 08:44:00
46	998.2	0.0	705.0	2019-01-31 08:45	38.4	10.0	7.8	45.4	3.0	20.5	1548920746	2019-01-31 08:45:00
47	998.2	0.0	732.0	2019-01-31 08:46	38.6	10.0	9.4	45.4	4.0	20.6	1548920806	2019-01-31 08:46:00
48	998.2	0.0	733.0	2019-01-31 08:47	38.8	10.0	6.1	45.4	5.0	20.6	1548920865	2019-01-31 08:47:00
49	998.2	0.0	723.0	2019-01-31 08:48	38.4	10.0	6.0	45.4	5.0	20.6	1548920925	2019-01-31 08:48:00
50	998.3	0.0	725.0	2019-01-31 08:49	38.6	10.0	5.8	45.5	5.0	20.5	1548920984	2019-01-31 08:49:00
51	998.2	0.0	731.0	2019-01-31 08:50	38.3	9.9	9.2	45.5	5.0	20.6	1548921044	2019-01-31 08:50:00
52	998.2	0.0	739.0	2019-01-31 08:51	38.6	10.0	23.1	45.4	6.0	20.5	1548921103	2019-01-31 08:51:00
53	998.2	0.0	749.0	2019-01-31 08:52	38.3	9.9	14.9	45.4	6.0	20.6	1548921163	2019-01-31 08:52:00
54	998.3	0.0	733.0	2019-01-31 08:53	38.5	10.0	17.6	45.4	6.0	20.6	1548921222	2019-01-31 08:53:00
55	998.2	0.0	729.0	2019-01-31 08:54	38.4	10.0	10.7	45.4	7.0	20.6	1548921282	2019-01-31 08:54:00
56	998.3	0.0	741.0	2019-01-31 08:55	38.4	10.0	7.1	45.5	7.0	20.6	1548921341	2019-01-31 08:55:00
57	998.2	0.0	729.0	2019-01-31 08:56	38.1	9.9	8.0	45.5	7.0	20.6	1548921401	2019-01-31 08:56:00
58	998.3	0.0	711.0	2019-01-31 08:57	38.2	9.9	8.5	45.6	8.0	20.6	1548921460	2019-01-31 08:57:00
59	998.2	0.0	719.0	2019-01-31 08:58	37.7	9.9	6.3	45.7	8.0	20.6	1548921520	2019-01-31 08:58:00
60	998.3	0.0	737.0	2019-01-31 08:59	37.8	9.9	7.3	45.7	8.0	20.6	1548921579	2019-01-31 08:59:00

	Air Pressure	CO2	DateTime	NO2	Ozone	PM2.5	Relative Humidity	TVOC	Temperature	Timestamp	datetime
0	1017.9	690.3	2018-10-01 00:00	0.5	4.5	10.0	53.4	155.2	21.0	1538348401	2018-10-01 00:00:00
1	1017.9	616.6	2018-10-01 01:00	0.5	4.6	9.7	53.5	128.8	20.8	1538352001	2018-10-01 01:00:00
2	1018.1	596.8	2018-10-01 02:00	0.4	4.7	9.0	53.6	114.6	20.6	1538355601	2018-10-01 02:00:00
3	1018.4	583.9	2018-10-01 03:00	0.5	4.7	8.8	53.7	106.1	20.4	1538359201	2018-10-01 03:00:00
4	1018.2	579.0	2018-10-01 04:00	0.5	4.8	8.4	53.9	106.5	20.2	1538362801	2018-10-01 04:00:00
5	1018.5	583.8	2018-10-01 05:00	0.5	4.8	8.3	54.1	103.1	20.1	1538366401	2018-10-01 05:00:00
6	1019.0	787.6	2018-10-01 06:00	0.4	4.5	11.5	55.6	165.4	20.5	1538370001	2018-10-01 06:00:00
7	1019.8	766.4	2018-10-01 07:00	0.5	4.5	10.9	56.3	172.5	20.5	1538373601	2018-10-01 07:00:00
8	1020.7	838.5	2018-10-01 08:00	0.4	4.6	19.2	55.9	145.9	20.5	1538377201	2018-10-01 08:00:00
9	1021.9	843.3	2018-10-01 09:00	0.5	4.7	32.5	55.5	138.8	20.5	1538380801	2018-10-01 09:00:00
10	1022.4	922.2	2018-10-01 10:00	0.5	4.8	27.0	55.9	167.2	20.6	1538384401	2018-10-01 10:00:00
11	1022.5	872.3	2018-10-01 11:00	0.5	4.3	90.6	52.5	274.2	21.3	1538388001	2018-10-01 11:00:00
12	1023.0	743.6	2018-10-01 12:00	0.5	3.0	149.6	50.1	892.0	21.4	1538391601	2018-10-01 12:00:00
13	1023.2	680.3	2018-10-01 13:00	0.6	3.7	50.1	51.4	450.0	21.1	1538395201	2018-10-01 13:00:00
14	1023.3	681.8	2018-10-01 14:00	0.5	4.0	21.4	50.5	344.1	21.3	1538398801	2018-10-01 14:00:00
15	1023.5	739.2	2018-10-01 15:00	0.5	4.3	14.4	51.5	182.9	20.9	1538402401	2018-10-01 15:00:00
16	1023.8	749.0	2018-10-01 16:00	0.5	4.4	19.7	51.1	99.6	20.9	1538406001	2018-10-01 16:00:00
17	1024.3	777.9	2018-10-01 17:00	0.5	4.5	17.4	50.7	70.2	21.2	1538409601	2018-10-01 17:00:00
18	1024.8	780.4	2018-10-01 18:00	0.5	4.4	16.6	50.7	86.5	21.4	1538413201	2018-10-01 18:00:00
19	1025.2	747.0	2018-10-01 19:00	0.5	4.5	15.6	52.5	91.5	21.0	1538416801	2018-10-01 19:00:00
20	1025.1	714.4	2018-10-01 20:00	0.5	4.6	12.0	52.7	54.8	20.6	1538420401	2018-10-01 20:00:00
21	1024.8	678.4	2018-10-01 21:00	0.5	4.7	11.9	52.4	40.9	20.4	1538424001	2018-10-01 21:00:00
22	1024.5	637.9	2018-10-01 22:00	0.5	4.7	9.9	52.3	35.6	20.4	1538427601	2018-10-01 22:00:00
23	1024.0	619.3	2018-10-01 23:00	0.4	4.7	9.3	52.2	37.6	20.2	1538431201	2018-10-01 23:00:00

	Air Pressure	CO	CO2	NO2	Ozone	PM2.5	Relative Humidity	TVOC	Temperature
datetime
2018-10-01 00:00:00	1017.9	0.0	690.3	0.5	4.5	10.0	53.4	155.2	21.0
2018-10-01 01:00:00	1017.9	0.0	616.6	0.5	4.6	9.7	53.5	128.8	20.8
2018-10-01 02:00:00	1018.1	0.0	596.8	0.4	4.7	9.0	53.6	114.6	20.6
2018-10-01 03:00:00	1018.4	0.0	583.9	0.5	4.7	8.8	53.7	106.1	20.4
2018-10-01 04:00:00	1018.2	0.0	579.0	0.5	4.8	8.4	53.9	106.5	20.2
2018-10-01 05:00:00	1018.5	0.0	583.8	0.5	4.8	8.3	54.1	103.1	20.1
2018-10-01 06:00:00	1019.0	0.0	787.6	0.4	4.5	11.5	55.6	165.4	20.5
2018-10-01 07:00:00	1019.8	0.0	766.4	0.5	4.5	10.9	56.3	172.5	20.5
2018-10-01 08:00:00	1020.7	0.0	838.5	0.4	4.6	19.2	55.9	145.9	20.5
2018-10-01 09:00:00	1021.9	0.0	843.3	0.5	4.7	32.5	55.5	138.8	20.5
2018-10-01 10:00:00	1022.4	0.0	922.2	0.5	4.8	27.0	55.9	167.2	20.6
2018-10-01 11:00:00	1022.5	0.0	872.3	0.5	4.3	90.6	52.5	274.2	21.3
2018-10-01 12:00:00	1023.0	0.0	743.6	0.5	3.0	149.6	50.1	892.0	21.4
2018-10-01 13:00:00	1023.2	0.0	680.3	0.6	3.7	50.1	51.4	450.0	21.1
2018-10-01 14:00:00	1023.3	0.0	681.8	0.5	4.0	21.4	50.5	344.1	21.3
2018-10-01 15:00:00	1023.5	0.0	739.2	0.5	4.3	14.4	51.5	182.9	20.9
2018-10-01 16:00:00	1023.8	0.0	749.0	0.5	4.4	19.7	51.1	99.6	20.9
2018-10-01 17:00:00	1024.3	0.0	777.9	0.5	4.5	17.4	50.7	70.2	21.2
2018-10-01 18:00:00	1024.8	0.0	780.4	0.5	4.4	16.6	50.7	86.5	21.4
2018-10-01 19:00:00	1025.2	0.0	747.0	0.5	4.5	15.6	52.5	91.5	21.0
2018-10-01 20:00:00	1025.1	0.0	714.4	0.5	4.6	12.0	52.7	54.8	20.6
2018-10-01 21:00:00	1024.8	0.0	678.4	0.5	4.7	11.9	52.4	40.9	20.4
2018-10-01 22:00:00	1024.5	0.0	637.9	0.5	4.7	9.9	52.3	35.6	20.4
2018-10-01 23:00:00	1024.0	0.0	619.3	0.4	4.7	9.3	52.2	37.6	20.2

	Air Pressure	CO	CO2	NO2	Ozone	PM2.5	Relative Humidity	TVOC	Temperature
datetime
2018-10-01	1021.866667	0.0	717.9125	0.4875	4.458333	24.741667	53.0	173.5	20.741667

	pc6	wkb_geometry
0	2951ER	(POLYGON ((104791.5001766441 430901.5047104367...
1	2951AG	(POLYGON ((105351.697376682 430865.7110104564,...
2	2951AT	(POLYGON ((105207.2637766702 430733.3865105017...
3	2951AW	(POLYGON ((105313.4081766779 430716.3427105072...
4	2951BE	(POLYGON ((105038.7358766636 431323.6454103026...
5	2951CN	(POLYGON ((104680.3259766386 430968.2379104128...
6	2951DB	(POLYGON ((105069.6622766652 431503.465210241,...
7	2951EC	(POLYGON ((104380.8704766151 430954.5252104093...
8	2951GG	(POLYGON ((105068.3635766616 430570.596210549,...
9	2951GJ	(POLYGON ((105554.9494766929 430683.3787105273...
10	2951HB	(POLYGON ((105358.3605766857 431609.6387102101...
11	2951HH	(POLYGON ((105122.1352766703 431584.3420102132...
12	2951JD	(POLYGON ((104429.9827766182 430742.0111104829...
13	2951JG	(POLYGON ((104369.537176614 430617.7807105244,...
14	2951PA	(POLYGON ((105851.6881767209 431542.5957102404...
15	2951PK	(POLYGON ((105667.4069767085 431403.8277102865...
16	2951PM	(POLYGON ((105674.5635767084 431568.3117102302...
17	2951PN	(POLYGON ((105721.0628767101 431469.9266102646...
18	2951VB	(POLYGON ((105131.2501766682 431069.4677103875...
19	2951VE	(POLYGON ((104959.1134766547 430867.4660104557...
20	2951VH	(POLYGON ((105203.6094766716 431062.7947103878...
21	2951VN	(POLYGON ((105156.5310766728 431302.8693103092...
22	2951WB	(POLYGON ((105395.4781766854 430968.3647104228...
23	2951XV	(POLYGON ((105510.401176693 430869.864710459, ...
24	2951XW	(POLYGON ((105480.3101766909 431146.7148103634...
25	2952AH	(POLYGON ((105066.3496766598 430373.3048106213...
26	2952AK	(POLYGON ((104954.6209766518 430434.4408105956...
27	2953AM	(POLYGON ((104468.7093766241 431411.6148102594...
28	2953BB	(POLYGON ((104441.6701766271 431646.9695101805...
29	2953BE	(POLYGON ((104395.326976622 431627.7506101847,...
30	2953CJ	(POLYGON ((104280.828776612 431183.2868103341,...
31	2953CN	(POLYGON ((103466.9180765592 431626.8143101659...
32	2953CT	(POLYGON ((103776.9671765816 431762.2367101334...
33	2953EZ	(POLYGON ((103924.904976594 431915.2589100823,...
34	2953GC	(POLYGON ((103533.0861765667 432130.2617100005...
35	2953HA	(POLYGON ((104533.4168766304 431197.0559103308...
36	2953HB	(POLYGON ((104484.5353766253 431217.0693103203...
37	2953HC	(POLYGON ((104214.9731766062 430920.1797104231...
38	2953WB	(POLYGON ((104204.5492766095 431603.7041101894...
39	2953WL	(POLYGON ((104041.7212765982 431775.9168101324...
40	2953XA	(POLYGON ((103537.759476566 431767.2454101265,...
41	2953XD	(POLYGON ((103429.1724765578 431785.9104101136...
42	2953XJ	(POLYGON ((103466.3963765625 431893.1223100763...
43	2953XT	(POLYGON ((103220.084676549 432514.422709867, ...
44	2954AA	(POLYGON ((105248.4640766799 431675.0329101846...
45	2954BA	(POLYGON ((104516.6317766312 431769.4257101389...
46	2954BE	(POLYGON ((104516.6317766312 431769.4257101389...
47	2954BL	(POLYGON ((104661.6371766392 431750.6767101502...
48	2954BP	(POLYGON ((104877.8170766566 431930.610210091,...
49	2954ND	(POLYGON ((105399.8403766898 431692.1669101812...
50	2954PB	(POLYGON ((104924.5819766576 431750.2877101554...
51	2954PC	(POLYGON ((104872.5781766572 431821.1258101297...
52	2969BT	(POLYGON ((108344.7648768795 430083.2758107731...
53	3355AA	(POLYGON ((107624.7321768187 427766.2941115281...

	formula	station_number	timestamp_measured	value
0	NO	NL01908	2018-10-08T00:00:00+00:00	15.3
1	NO2	NL01908	2018-10-08T00:00:00+00:00	44.7
2	PM10	NL01908	2018-10-08T00:00:00+00:00	19.9
3	NO	NL01908	2018-10-07T23:00:00+00:00	26.7
4	NO2	NL01908	2018-10-07T23:00:00+00:00	52.1
5	PM10	NL01908	2018-10-07T23:00:00+00:00	15.4
6	NO	NL01908	2018-10-07T22:00:00+00:00	19.1
7	NO2	NL01908	2018-10-07T22:00:00+00:00	48.9
8	PM10	NL01908	2018-10-07T22:00:00+00:00	11.5
9	NO	NL01908	2018-10-07T21:00:00+00:00	6.6
10	NO2	NL01908	2018-10-07T21:00:00+00:00	25.6
11	PM10	NL01908	2018-10-07T21:00:00+00:00	17.6
12	NO	NL01908	2018-10-07T20:00:00+00:00	4.3
13	NO2	NL01908	2018-10-07T20:00:00+00:00	18.3
14	PM10	NL01908	2018-10-07T20:00:00+00:00	14.6
15	NO2	NL01908	2018-10-07T19:00:00+00:00	22.0
16	PM10	NL01908	2018-10-07T19:00:00+00:00	12.6
17	NO	NL01908	2018-10-07T19:00:00+00:00	5.1
18	NO	NL01908	2018-10-07T18:00:00+00:00	3.5
19	NO2	NL01908	2018-10-07T18:00:00+00:00	15.7
20	PM10	NL01908	2018-10-07T18:00:00+00:00	11.8
21	NO	NL01908	2018-10-07T17:00:00+00:00	2.5
22	NO2	NL01908	2018-10-07T17:00:00+00:00	10.7
23	PM10	NL01908	2018-10-07T17:00:00+00:00	13.2
24	NO	NL01908	2018-10-07T16:00:00+00:00	4.9
25	NO2	NL01908	2018-10-07T16:00:00+00:00	11.8
26	PM10	NL01908	2018-10-07T16:00:00+00:00	13.7
27	NO	NL01908	2018-10-07T15:00:00+00:00	2.8
28	NO2	NL01908	2018-10-07T15:00:00+00:00	7.4
29	PM10	NL01908	2018-10-07T15:00:00+00:00	18.5
...	...	...	...	...
472	NO2	NL01908	2019-02-11T10:00:00+00:00	36.5
473	PM10	NL01908	2019-02-11T10:00:00+00:00	24.2
474	NO	NL01908	2019-02-11T09:00:00+00:00	20.3
475	NO2	NL01908	2019-02-11T09:00:00+00:00	45.5
476	PM10	NL01908	2019-02-11T09:00:00+00:00	19.8
477	NO	NL01908	2019-02-11T08:00:00+00:00	13.7
478	NO2	NL01908	2019-02-11T08:00:00+00:00	39.5
479	PM10	NL01908	2019-02-11T08:00:00+00:00	16.4
480	NO	NL01908	2019-02-11T07:00:00+00:00	14.1
481	NO2	NL01908	2019-02-11T07:00:00+00:00	36.7
482	PM10	NL01908	2019-02-11T07:00:00+00:00	12.8
483	NO	NL01908	2019-02-11T06:00:00+00:00	12.1
484	NO2	NL01908	2019-02-11T06:00:00+00:00	37.8
485	PM10	NL01908	2019-02-11T06:00:00+00:00	20.3
486	NO	NL01908	2019-02-11T05:00:00+00:00	4.4
487	NO2	NL01908	2019-02-11T05:00:00+00:00	25.0
488	PM10	NL01908	2019-02-11T05:00:00+00:00	20.6
489	NO	NL01908	2019-02-11T04:00:00+00:00	0.0
490	NO2	NL01908	2019-02-11T04:00:00+00:00	10.7
491	PM10	NL01908	2019-02-11T04:00:00+00:00	19.7
492	NO	NL01908	2019-02-11T03:00:00+00:00	0.4
493	NO2	NL01908	2019-02-11T03:00:00+00:00	13.8
494	PM10	NL01908	2019-02-11T03:00:00+00:00	17.0
495	NO	NL01908	2019-02-11T02:00:00+00:00	0.2
496	NO2	NL01908	2019-02-11T02:00:00+00:00	11.8
497	PM10	NL01908	2019-02-11T02:00:00+00:00	16.6
498	NO2	NL01908	2019-02-11T01:00:00+00:00	7.3
499	PM10	NL01908	2019-02-11T01:00:00+00:00	8.7
500	NO2	NL01908	2019-02-11T00:00:00+00:00	7.4
501	PM10	NL01908	2019-02-11T00:00:00+00:00	8.5

	station_number_	timestamp_measured_	value_NO	value_NO2	value_PM10
0	NL01908	2018-10-01T00:00:00+00:00	0.6	8.8	9.1
1	NL01908	2018-10-01T01:00:00+00:00	0.8	10.9	7.2
2	NL01908	2018-10-01T02:00:00+00:00	0.9	11.8	2.5
3	NL01908	2018-10-01T03:00:00+00:00	1.0	7.6	2.3
4	NL01908	2018-10-01T04:00:00+00:00	8.1	30.2	5.7
5	NL01908	2018-10-01T05:00:00+00:00	36.1	53.7	5.2
6	NL01908	2018-10-01T06:00:00+00:00	31.9	47.6	6.0
7	NL01908	2018-10-01T07:00:00+00:00	34.7	49.1	7.0
8	NL01908	2018-10-01T08:00:00+00:00	19.4	33.0	8.7
9	NL01908	2018-10-01T09:00:00+00:00	10.0	20.2	9.2
10	NL01908	2018-10-01T10:00:00+00:00	13.3	26.0	16.8
11	NL01908	2018-10-01T11:00:00+00:00	6.8	9.9	12.9
12	NL01908	2018-10-01T12:00:00+00:00	3.4	10.5	17.6
13	NL01908	2018-10-01T13:00:00+00:00	6.2	13.9	16.6
14	NL01908	2018-10-01T14:00:00+00:00	6.4	14.7	19.3
15	NL01908	2018-10-01T15:00:00+00:00	14.3	26.7	19.5
16	NL01908	2018-10-01T16:00:00+00:00	8.0	16.6	22.3
17	NL01908	2018-10-01T17:00:00+00:00	10.4	23.1	15.8
18	NL01908	2018-10-01T18:00:00+00:00	4.2	16.2	12.7
19	NL01908	2018-10-01T19:00:00+00:00	3.4	16.0	12.1
20	NL01908	2018-10-01T20:00:00+00:00	6.3	34.2	10.5
21	NL01908	2018-10-01T21:00:00+00:00	0.8	8.6	11.4
22	NL01908	2018-10-01T22:00:00+00:00	7.6	40.7	16.1
23	NL01908	2018-10-01T23:00:00+00:00	9.1	43.5	32.9
24	NL01908	2018-10-02T00:00:00+00:00	10.3	38.4	17.1
25	NL01908	2018-10-02T01:00:00+00:00	3.8	22.5	16.0
26	NL01908	2018-10-02T02:00:00+00:00	3.7	19.5	8.8
27	NL01908	2018-10-02T03:00:00+00:00	7.1	25.0	16.6
28	NL01908	2018-10-02T04:00:00+00:00	16.1	32.9	15.5
29	NL01908	2018-10-02T05:00:00+00:00	43.9	47.2	20.6
...	...	...	...	...	...
3329	NL01908	2019-02-16T19:00:00+00:00	73.3	76.6	68.7
3330	NL01908	2019-02-16T20:00:00+00:00	168.3	77.9	81.4
3331	NL01908	2019-02-16T21:00:00+00:00	188.1	79.8	70.7
3332	NL01908	2019-02-16T22:00:00+00:00	138.8	79.5	64.4
3333	NL01908	2019-02-16T23:00:00+00:00	53.1	72.0	53.5
3334	NL01908	2019-02-17T00:00:00+00:00	40.3	70.5	51.3
3335	NL01908	2019-02-17T01:00:00+00:00	17.7	69.3	54.0
3336	NL01908	2019-02-17T02:00:00+00:00	15.8	68.2	50.6
3337	NL01908	2019-02-17T03:00:00+00:00	12.8	62.1	54.4
3338	NL01908	2019-02-17T04:00:00+00:00	3.5	50.2	46.3
3339	NL01908	2019-02-17T05:00:00+00:00	5.4	47.2	48.1
3340	NL01908	2019-02-17T06:00:00+00:00	5.0	41.6	41.6
3341	NL01908	2019-02-17T07:00:00+00:00	7.0	40.3	37.2
3342	NL01908	2019-02-17T08:00:00+00:00	6.8	35.9	40.5
3343	NL01908	2019-02-17T09:00:00+00:00	14.2	35.7	43.1
3344	NL01908	2019-02-17T10:00:00+00:00	15.1	33.0	86.0
3345	NL01908	2019-02-17T11:00:00+00:00	21.9	39.0	43.4
3346	NL01908	2019-02-17T12:00:00+00:00	15.5	33.2	32.3
3347	NL01908	2019-02-17T13:00:00+00:00	12.9	31.1	39.3
3348	NL01908	2019-02-17T14:00:00+00:00	12.0	31.7	33.8
3349	NL01908	2019-02-17T15:00:00+00:00	9.8	30.8	29.1
3350	NL01908	2019-02-17T16:00:00+00:00	6.5	30.4	26.0
3351	NL01908	2019-02-17T17:00:00+00:00	6.6	35.1	32.3
3352	NL01908	2019-02-17T18:00:00+00:00	9.9	43.4	29.1
3353	NL01908	2019-02-17T19:00:00+00:00	8.0	39.0	25.8
3354	NL01908	2019-02-17T20:00:00+00:00	10.4	42.6	28.1
3355	NL01908	2019-02-17T21:00:00+00:00	6.9	39.3	24.2
3356	NL01908	2019-02-17T22:00:00+00:00	7.3	41.8	28.2
3357	NL01908	2019-02-17T23:00:00+00:00	7.9	42.6	31.5
3358	NL01908	2019-02-18T00:00:00+00:00	5.3	44.7	32.9

	code	gemeentenaam	wkb_geometry	airquality
0	1621	Lansingerland	(POLYGON ((94441.60400000001 449656.069, 92566...	{'Errors': [], 'actueel': {'pm10': {'min': 12....
1	0356	Nieuwegein	(POLYGON ((132952.11 447885.813, 133552.582 44...	{'Errors': [], 'actueel': {'pm10': {'min': 15....
2	0059	Achtkarspelen	(POLYGON ((201481.106 579247.514, 201656.815 5...	{'Errors': [], 'actueel': {'pm10': {'min': 10....
3	0654	Borsele	(POLYGON ((39150.056 391591.526, 39144.171 391...	{'Errors': [], 'actueel': {'pm10': {'min': 10....
4	0629	Wassenaar	(POLYGON ((87262.114 465480.798, 87136.628 465...	{'Errors': [], 'actueel': {'pm10': {'min': 8.9...

	deviceName	macAddress	serialNumber	Air Pressure	CO	CO2	DateTime	NO2	Ozone	PM2.5	...	Temperature	Timestamp	datetime	pc6	pc4	x	y	geometry	luchst_number	luchst_distance
0	2951 BE / Project Ablasserdam	606405acf1e5	49ff76067580485655301867	1013.7	0.0	485.0	2019-03-08 09:49	18.2	10.9	5.7	...	10.2	1552034949	2019-03-08 09:49:00	2951BE	2951.0	104986.141167	431261.367223	POINT (4.66103620904929 51.86765719595473)	NL01908	1087.391620
1	2951 BE / Project Ablasserdam	a81b6a9ae855	48ff72067580505235331667	1012.2	0.0	528.0	2019-03-08 09:49	4.9	10.2	38.8	...	16.3	1552034942	2019-03-08 09:49:00	2951BE	2951.0	104986.141167	431261.367223	POINT (4.66103620904929 51.86765719595473)	NL01908	1087.391620
2	2951 PM / Project Alblasserdam	606405acd6e2	48ff72067580505252431467	1011.7	0.0	601.0	2019-03-08 09:48	42.6	14.6	6.2	...	20.7	1552034935	2019-03-08 09:48:00	2951PM	2951.0	105656.167100	431517.265449	POINT (4.6707271772421 51.8700168821404)	NL01908	1533.355217
3	2953 AM / Project Alblasserdam	a81b6a9ad4ff	48ff6c067580505240571367	1011.5	0.0	786.0	2019-03-08 09:48	0.1	7.8	6.8	...	20.0	1552034925	2019-03-08 09:48:00	2953AM	2953.0	104442.317284	431414.477687	POINT (4.653118439552964 51.86898422920255)	NL01908	1324.152958
4	2951 HB / Project Alblasserdam	606405acf51f	49ff6d067580485648301367	1024.4	0.0	1510.0	2018-12-04 15:34	19.4	8.7	10.8	...	20.3	1543934068	2018-12-04 15:34:00	2951HB	2951.0	105268.590241	431539.562700	POINT (4.665096748820099 51.87018282349947)	NL01908	1408.893940
5	2953 WB / Project Alblasserdam	606405acf6d4	30ff70064d48303544172543	1012.0	0.0	486.0	2019-03-08 11:50	48.5	10.0	35.3	...	19.6	1552035022	2019-03-08 11:50:00	2953WB	2953.0	104120.203803	431567.387576	POINT (4.648419250645758 51.87032921600763)	NL01908	1600.569085
6	2953 WB / Project Alblasserdam	606405acb241	48ff6e067580505240551367	1011.8	0.0	451.0	2019-03-08 09:50	53.6	10.2	4.7	...	19.2	1552035004	2019-03-08 09:50:00	2953WB	2953.0	104120.203803	431567.387576	POINT (4.648419250645758 51.87032921600763)	NL01908	1600.569085
7	2953 BB / Project Alblasserdam	606405acf1d7	49ff6c067580485635281367	1012.0	0.0	553.0	2019-03-08 09:48	20.0	8.5	3.1	...	20.5	1552034920	2019-03-08 09:48:00	2953BB	2953.0	104393.792409	431587.771284	POINT (4.652388499674358 51.8705373364708)	NL01908	1503.474796
8	2952 AH / Project Alblasserdam	606405ace2b3	49ff6c067580485629261367	1012.1	0.0	630.0	2019-03-08 09:49	5.8	7.6	11.1	...	19.5	1552034992	2019-03-08 09:49:00	2952AH	2952.0	105056.215168	430312.525493	POINT (4.662190910466406 51.85913554548875)	NL01908	198.114291
9	2953 HC / Project Alblasserdam	606405acf440	49ff68067580485646301367	1011.7	0.0	691.0	2019-03-08 09:49	0.3	5.9	38.0	...	21.4	1552034954	2019-03-08 09:49:00	2953HC	2953.0	104138.338669	430882.454735	POINT (4.648783544037864 51.86417489635055)	NL01908	1047.482782
10	2953 HC / Project Alblasserdam	a81b6a9ae0df	48ff70067580505251401867	1011.8	0.0	624.0	2019-03-08 09:49	37.9	10.3	22.1	...	20.5	1552034968	2019-03-08 09:49:00	2953HC	2953.0	104138.338669	430882.454735	POINT (4.648783544037864 51.86417489635055)	NL01908	1047.482782
11	2953 HC / Project Alblasserdam	606405acf33d	49ff6c067580485642222067	1035.2	0.0	975.0	2019-02-26 10:43	19.4	8.0	8.5	...	19.0	1551174195	2019-02-26 10:43:00	2953HC	2953.0	104138.338669	430882.454735	POINT (4.648783544037864 51.86417489635055)	NL01908	1047.482782
12	2951 HH / Project Alblasserdam	606405ace741	49ff71067580485651331367	1011.8	0.0	819.0	2019-03-08 09:49	0.5	5.4	3.2	...	20.5	1552034990	2019-03-08 09:49:00	2951HH	2951.0	105167.122994	431517.009344	POINT (4.663626803568363 51.86997105381214)	NL01908	1364.542411
13	2951 JG / Project Alblasserdam	606405acf78e	49ff72067580485655441767	1009.5	0.0	553.0	2019-03-08 09:47	7.5	8.4	18.7	...	21.0	1552034870	2019-03-08 09:47:00	2951JG	2951.0	104354.160978	430560.253868	POINT (4.651963940785627 51.86129870322683)	NL01908	677.246907
14	2951 JG / Project Alblasserdam	606405acb237	48ff6e067580505237541367	1010.3	0.0	639.0	2019-03-08 09:50	34.0	9.4	57.0	...	19.8	1552035028	2019-03-08 09:50:00	2951JG	2951.0	104354.160978	430560.253868	POINT (4.651963940785627 51.86129870322683)	NL01908	677.246907
15	2951 JG / Project Alblasserdam	a81b6a9aeb50	49ff70067580485649461767	1009.5	0.0	646.0	2019-03-08 09:49	6.2	7.8	8.2	...	21.8	1552034969	2019-03-08 09:49:00	2951JG	2951.0	104354.160978	430560.253868	POINT (4.651963940785627 51.86129870322683)	NL01908	677.246907
16	2953 XT / Project Alblasserdam	606405acae24	49ff72067580485623351367	1011.3	0.0	610.0	2019-03-08 09:48	5.7	8.3	65.3	...	17.4	1552034910	2019-03-08 09:48:00	2953XT	2953.0	103061.711526	432547.135891	POINT (4.632903444532086 51.87903720284938)	NL01908	3007.357533
17	2953 XT / Project Alblasserdam	a81b6a9ae58f	48ff70067580505226481767	1010.4	0.0	649.0	2019-03-08 09:50	0.6	5.7	4.6	...	15.9	1552035016	2019-03-08 09:50:00	2953XT	2953.0	103061.711526	432547.135891	POINT (4.632903444532086 51.87903720284938)	NL01908	3007.357533
18	2953 XD / Project Alblasserdam	606405acf1d1	48ff76067580505240431367	1004.1	0.0	805.0	2019-02-09 14:02	0.3	8.6	43.8	...	21.6	1549717374	2019-02-09 14:02:00	2953XD	2953.0	103415.057880	431813.550818	POINT (4.638144421738962 51.87247681158955)	NL01908	2218.411096
19	2951 VH / Project Alblasserdam	a81b6a9ae594	49ff76067580485649291867	1010.5	0.0	512.0	2019-03-08 09:49	16.4	8.2	13.8	...	21.6	1552034974	2019-03-08 09:49:00	2951VH	2951.0	105145.317207	431001.833917	POINT (4.663384666308675 51.86533884541635)	NL01908	857.634706
20	2951 VH / Project Alblasserdam	606405acb0f2	48ff6a067580505243151767	1012.1	0.0	480.0	2019-03-08 09:49	45.2	10.3	35.3	...	21.8	1552034958	2019-03-08 09:49:00	2951VH	2951.0	105145.317207	431001.833917	POINT (4.663384666308675 51.86533884541635)	NL01908	857.634706
21	2951 VH / Project Alblasserdam	606405a4462c	53ff73067788545138430567	1012.7	0.0	742.0	2019-03-08 10:50	16.6	8.3	4.5	...	19.4	1552035032	2019-03-08 10:50:00	2951VH	2951.0	105145.317207	431001.833917	POINT (4.663384666308675 51.86533884541635)	NL01908	857.634706
22	2953 BE / Project Alblasserdam	606405acf459	49ff74067580485629291367	1011.5	0.0	532.0	2019-03-08 09:50	0.2	8.2	3.7	...	21.8	1552035011	2019-03-08 09:50:00	2953BE	2953.0	104339.016072	431597.973439	POINT (4.651591698369757 51.87062405231882)	NL01908	1532.686689
23	2952 AK / Project Alblasserdam	606405acf1a7	49ff72067580485612381667	1011.3	0.0	804.0	2019-03-08 09:50	30.5	9.7	5.1	...	20.8	1552035025	2019-03-08 09:50:00	2952AK	2952.0	105082.945668	430370.276138	POINT (4.662570561758304 51.85965698792576)	NL01908	258.284295
24	2952 AK / Project Alblasserdam	a81b6a9ad4ed	48ff76067580505238401467	1012.5	0.0	662.0	2019-03-08 09:48	6.6	8.3	9.6	...	18.1	1552034917	2019-03-08 09:48:00	2952AK	2952.0	105082.945668	430370.276138	POINT (4.662570561758304 51.85965698792576)	NL01908	258.284295
25	2951 AG / Project Alblasserdam	606405ace2c2	49ff72067580485617291367	1012.0	0.0	965.0	2019-03-08 09:49	33.0	9.4	9.8	...	21.2	1552034997	2019-03-08 09:49:00	2951AG	2951.0	105303.645488	430842.076344	POINT (4.665706202640886 51.86391712936577)	NL01908	772.166780
26	2951 XV / Project Alblasserdam	606405acf32b	48ff6e067580505232341967	1011.1	0.0	759.0	2019-03-08 09:48	32.8	11.4	8.8	...	18.6	1552034884	2019-03-08 09:48:00	2951XV	2951.0	105463.636847	430866.048430	POINT (4.668025354979314 51.86414682958139)	NL01908	882.940936
27	2954 BP / Project Alblasserdam	606405acf502	49ff72067580485658331367	1012.5	0.0	528.0	2019-03-08 09:48	41.5	10.0	9.0	...	18.3	1552034938	2019-03-08 09:48:00	2954BP	2954.0	104668.695522	431959.790913	POINT (4.656325550531225 51.87390584443126)	NL01908	1799.843652
28	2951 XW / Project Alblasserdam	a81b6a9ae0a9	48ff74067580505240471367	1011.3	0.0	1550.0	2019-03-08 09:50	54.4	9.9	34.8	...	20.8	1552035006	2019-03-08 09:50:00	2951XW	2951.0	105426.429543	431078.193482	POINT (4.667454728915423 51.86605022525549)	NL01908	1038.031751
29	2951 PK / Project Alblasserdam	606405acb262	49ff72067580485618491667	1011.6	0.0	749.0	2019-03-08 09:50	23.6	9.1	5.3	...	18.0	1552035012	2019-03-08 09:50:00	2951PK	2951.0	105608.557033	431431.727551	POINT (4.670048177355471 51.86924387130158)	NL01908	1435.460207
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37	2951 JD / Project Alblasserdam	606405acf41f	49ff71067580485631311367	1011.6	0.0	790.0	2019-03-08 09:49	7.5	7.8	6.1	...	17.4	1552034956	2019-03-08 09:49:00	2951JD	2951.0	104368.171834	430706.957885	POINT (4.652145796139145 51.86261851072978)	NL01908	759.831122
38	2951 JD / Project Alblasserdam	606405acafe0	49ff70067580485654281867	1011.9	0.0	915.0	2019-03-08 09:50	10.8	9.0	23.1	...	19.6	1552035022	2019-03-08 09:50:00	2951JD	2951.0	104368.171834	430706.957885	POINT (4.652145796139145 51.86261851072978)	NL01908	759.831122
39	2954 BA / Project Alblasserdam	606405acf6d8	49ff69067580485641461767	1012.4	0.0	400.0	2019-03-08 09:07	10.3	8.0	30.2	...	17.2	1552032476	2019-03-08 09:07:00	2954BA	2954.0	104467.031671	431790.614281	POINT (4.653422145419168 51.87236707806075)	NL01908	1674.379865
40	2951 GJ / Project Alblasserdam	606405acf6b3	48ff70067580505252281367	1009.9	0.0	561.0	2019-03-08 09:49	28.6	9.0	7.8	...	21.0	1552034982	2019-03-08 09:49:00	2951GJ	2951.0	105459.427030	430602.079060	POINT (4.668002143402793 51.86177396654328)	NL01908	693.250637
41	2954 ND / Project Alblasserdam	a81b6a9aeb21	49ff72067580485637301367	1012.5	0.0	489.0	2019-03-08 09:49	13.5	8.3	4.9	...	21.0	1552034970	2019-03-08 09:49:00	2954ND	2954.0	105336.338497	431677.844383	POINT (4.666060473931189 51.87143170515778)	NL01908	1560.115511
42	2954 AA / Project Ablasserdam	606405acb245	49ff70067580485632431667	1012.3	0.0	648.0	2019-03-08 09:48	37.5	10.3	23.8	...	19.0	1552034933	2019-03-08 09:48:00	2954AA	2954.0	105156.242095	431629.260895	POINT (4.66345260164969 51.87097896678377)	NL01908	1473.124867
43	2951 AT / Project Alblasserdam	606405acf423	49ff70067580485653451767	1016.8	0.0	542.0	2019-03-01 11:40	42.1	9.9	56.2	...	18.8	1551436830	2019-03-01 11:40:00	2951AT	2951.0	105164.122268	430722.949170	POINT (4.663697953142499 51.86283398548988)	NL01908	601.735825
44	3355 AA / Project Alblasserdam	606405acb0cd	49ff73067580485650381667	1006.4	0.6	2078.0	2019-01-13 21:39	15.6	10.0	18.7	...	20.5	1547411973	2019-01-13 21:39:00	3355AA	3355.0	107633.752610	427693.820153	POINT (4.699964446977091 51.83582414088034)	NL01908	3683.828785
45	2953 EZ / Project Alblasserdam	606405acae3e	49ff72067580485644381667	1011.7	0.0	886.0	2019-03-08 09:49	44.5	10.6	55.5	...	19.7	1552034977	2019-03-08 09:49:00	2953EZ	2953.0	103861.068779	431888.126684	POINT (4.644609305650088 51.87318819607706)	NL01908	2008.615090