Example of DOV search methods for interpretations (informele stratigrafie)¶

Use cases explained below¶

Get 'informele stratigrafie' in a bounding box
Get 'informele stratigrafie' with specific properties
Get 'informele stratigrafie' in a bounding box based on specific properties
Select 'informele stratigrafie' in a municipality and return date
Get 'informele stratigrafie' based on fields not available in the standard output dataframe
Get 'informele stratigrafie' data, returning fields not available in the standard output dataframe

In [1]:

%matplotlib inline
import inspect, sys

In [2]:

# check pydov path
import pydov

Get information about the datatype 'Informele stratigrafie'¶

In [3]:

from pydov.search.interpretaties import InformeleStratigrafieSearch
itp = InformeleStratigrafieSearch()

A description is provided for the 'Informele stratigrafie' datatype:

In [4]:

itp.get_description()

Out[4]:

'Een informele stratigrafie van een boring is, net als een formele stratigrafie, een lithostratigrafische interpretatie van een (gecodeerde) lithologische beschrijving per diepte-interval. Het verschil met de formele stratigrafie is dat de informele stratigrafie niet moet voldoen aan een algemeen aanvaarde standaard. De informele stratigrafie is een plaats in DOV om oude interpretaties of interpretaties die niet voldoen aan de huidige stratigrafische indeling toch weer te geven. De informele stratigrafie kan ook een gedetailleerde beschrijving bevatten van een lithostratigrafische eenheid.'

The different fields that are available for objects of the 'Informele stratigrafie' datatype can be requested with the get_fields() method:

In [5]:

fields = itp.get_fields()

# print available fields
for f in fields.values():
    print(f['name'])

pkey_interpretatie
Type_proef
Proefnummer
Proeffiche
x
y
Z_mTAW
diepte_tot_m
gemeente
Auteurs
Datum
Opdrachten
betrouwbaarheid_interpretatie
Geldig_van
Geldig_tot
diepte_laag_van
diepte_laag_tot
beschrijving
pkey_boring
pkey_sondering

You can get more information of a field by requesting it from the fields dictionary:

name: name of the field
definition: definition of this field
cost: currently this is either 1 or 10, depending on the datasource of the field. It is an indication of the expected time it will take to retrieve this field in the output dataframe.
notnull: whether the field is mandatory or not
type: datatype of the values of this field

In [6]:

fields['Datum']

Out[6]:

{'name': 'Datum',
 'definition': 'De datum waarop de informele stratigrafie werd beschreven.',
 'type': 'date',
 'notnull': False,
 'query': True,
 'cost': 1}

Example use cases¶

Get 'informele stratigrafie' in a bounding box¶

Get data for all the 'informele stratigrafie' interpretations that are geographically located within the bounds of the specified box.

The coordinates are in the Belgian Lambert72 (EPSG:31370) coordinate system and are given in the order of lower left x, lower left y, upper right x, upper right y.

In [7]:

from pydov.util.location import Within, Box

df = itp.search(location=Within(Box(153145, 206930, 153150, 206935)))
df.head()

[000/001] .

Out[7]:

	pkey_interpretatie	pkey_boring	pkey_sondering	betrouwbaarheid_interpretatie	x	y	diepte_laag_van	diepte_laag_tot	beschrijving
0	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1974...	NaN	goed	153147.0	206931.0	0.0	3.00	Quartair
1	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1974...	NaN	goed	153147.0	206931.0	3.0	14.05	Tertiair - Mioceen: Antwerpiaan

The dataframe contains one 'informele stratigrafie' interpretation where three layers ('laag') were identified. The available data are flattened to represent unique attributes per row of the dataframe.

Using the pkey_interpretatie field one can request the details of this interpretation in a webbrowser:

In [8]:

for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)

https://www.dov.vlaanderen.be/data/interpretatie/1975-213484

Get 'informele stratigrafie' with specific properties¶

Next to querying interpretations based on their geographic location within a bounding box, we can also search for interpretations matching a specific set of properties. For this we can build a query using a combination of the 'InformeleStratigrafie' fields and operators provided by the WFS protocol.

A list of possible operators can be found below:

In [9]:

[i for i,j in inspect.getmembers(sys.modules['owslib.fes'], inspect.isclass) if 'Property' in i]

Out[9]:

['PropertyIsBetween',
 'PropertyIsEqualTo',
 'PropertyIsGreaterThan',
 'PropertyIsGreaterThanOrEqualTo',
 'PropertyIsLessThan',
 'PropertyIsLessThanOrEqualTo',
 'PropertyIsLike',
 'PropertyIsNotEqualTo',
 'PropertyIsNull',
 'SortProperty']

In this example we build a query using the PropertyIsEqualTo operator to find all interpretations that are within the community (gemeente) of 'Herstappe':

In [10]:

from owslib.fes import PropertyIsEqualTo

query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Herstappe')
df = itp.search(query=query)

df.head()

[000/003] ...

Out[10]:

	pkey_interpretatie	pkey_boring	pkey_sondering	betrouwbaarheid_interpretatie	x	y	diepte_laag_van	diepte_laag_tot	beschrijving
0	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/2016...	NaN	goed	224687.4	158191.0	0.00	3.75	q2
1	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/2016...	NaN	goed	224687.4	158191.0	3.75	6.00	q1c
2	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1993...	NaN	goed	224820.0	157794.0	0.00	50.00	?
3	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1993...	NaN	goed	224820.0	157794.0	5.00	50.00	Formatie van Gulpen

Once again we can use the pkey_interpretatie as a permanent link to the information of these interpretations:

In [11]:

for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)

https://www.dov.vlaanderen.be/data/interpretatie/2010-106477
https://www.dov.vlaanderen.be/data/interpretatie/2016-271517
https://www.dov.vlaanderen.be/data/interpretatie/1993-097697

Get 'informele stratigrafie' in a bounding box based on specific properties¶

We can combine a query on attributes with a query on geographic location to get the interpretations within a bounding box that have specific properties.

The following example requests the interpretations of boreholes only, within the given bounding box.

(Note that the datatype of the literal parameter should be a string, regardless of the datatype of this field in the output dataframe.)

In [12]:

from owslib.fes import PropertyIsEqualTo

query = PropertyIsEqualTo(
            propertyname='Type_proef',
            literal='Boring')

df = itp.search(
    location=Within(Box(205000, 205000, 206000, 206000)),
    query=query
    )

df.head()

[000/003] ...

Out[12]:

	pkey_interpretatie	pkey_boring	pkey_sondering	betrouwbaarheid_interpretatie	x	y	diepte_laag_van	diepte_laag_tot	beschrijving
0	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1975...	NaN	goed	205264.2	205607.5	0.0	8.0	Kwartair
1	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1896...	NaN	goed	205970.0	205270.5	0.0	2.3	q4
2	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1896...	NaN	goed	205952.9	205993.0	0.0	1.0	R
3	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1896...	NaN	goed	205952.9	205993.0	1.0	2.3	q4

We can look at one of the interpretations in a webbrowser using its pkey_interpretatie:

In [13]:

for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)

https://www.dov.vlaanderen.be/data/interpretatie/1896-044150
https://www.dov.vlaanderen.be/data/interpretatie/1975-044576
https://www.dov.vlaanderen.be/data/interpretatie/1896-044149

Select 'informele stratigrafie' in a municipality and return date¶

We can limit the columns in the output dataframe by specifying the return_fields parameter in our search.

In this example we query all the 'informele stratigrafie' interpretations in the city of Ghent and return their date:

In [14]:

query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Gent')
df = itp.search(query=query,
                return_fields=('Datum',))
df.head()

Out[14]:

	Datum
0	NaN
1	NaN
2	NaN
3	1981-02-25
4	NaN

In [15]:

df.describe()

Out[15]:

	Datum
count	642
unique	248
top	1996-11-25
freq	41

Get 'informele stratigrafie' based on fields not available in the standard output dataframe¶

To keep the output dataframe size acceptable, not all available WFS fields are included in the standard output. However, one can use this information to select interpretations as illustrated below.

For example, make a selection of the interpretations in municipality the of Antwerp, before 1/1/1900:

In [16]:

from owslib.fes import And, PropertyIsEqualTo, PropertyIsLessThan

query = And([PropertyIsEqualTo(propertyname='gemeente',
                               literal='Antwerpen'),
             PropertyIsLessThan(propertyname='Datum', 
                                 literal='1900-01-01')]
            )
df = itp.search(query=query,
                return_fields=('pkey_interpretatie', 'Datum'))
df.head()

Out[16]:

	pkey_interpretatie	Datum
0	https://www.dov.vlaanderen.be/data/interpretat...	1895-05-06
1	https://www.dov.vlaanderen.be/data/interpretat...	1895-05-10
2	https://www.dov.vlaanderen.be/data/interpretat...	1879-08-09
3	https://www.dov.vlaanderen.be/data/interpretat...	1879-08-18
4	https://www.dov.vlaanderen.be/data/interpretat...	1879-03-05

Get 'informele stratigrafie' data, returning fields not available in the standard output dataframe¶

As denoted in the previous example, not all available fields are available in the default output frame to keep its size limited. However, you can request any available field by including it in the return_fields parameter of the search:

In [17]:

query = PropertyIsEqualTo(
            propertyname='gemeente',
            literal='Herstappe')

df = itp.search(query=query,
                return_fields=('pkey_interpretatie', 'pkey_boring', 'pkey_sondering',
                               'x', 'y', 'Z_mTAW', 'gemeente', 'Auteurs', 'Proefnummer'))

df.head()

Out[17]:

	pkey_interpretatie	pkey_boring	pkey_sondering	x	y	Z_mTAW	gemeente	Auteurs	Proefnummer
0	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/2016...	NaN	224687.4	158191.0	124.0	Herstappe	Rutôt, A. - Belgische Geologische Dienst (BGD)	kb33d106e-B236
1	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1993...	NaN	224820.0	157794.0	132.0	Herstappe	boormeester - bedrijf-dienst onbekend	kb41d120e-B1027
2	https://www.dov.vlaanderen.be/data/interpretat...	https://www.dov.vlaanderen.be/data/boring/1993...	NaN	224820.0	157794.0	132.0	Herstappe	Lagrou, David - VLAKO	kb41d120e-B1027

Visualize results¶

Using Folium, we can display the results of our search on a map.

In [18]:

# import the necessary modules (not included in the requirements of pydov!)
import folium
from folium.plugins import MarkerCluster
from pyproj import Transformer

In [19]:

# convert the coordinates to lat/lon for folium
def convert_latlon(x1, y1):
    transformer = Transformer.from_crs("epsg:31370", "epsg:4326", always_xy=True)
    x2,y2 = transformer.transform(x1, y1)
    return x2, y2

df['lon'], df['lat'] = zip(*map(convert_latlon, df['x'], df['y'])) 
# convert to list
loclist = df[['lat', 'lon']].values.tolist()

In [20]:

# initialize the Folium map on the centre of the selected locations, play with the zoom until ok
fmap = folium.Map(location=[df['lat'].mean(), df['lon'].mean()], zoom_start=12)
marker_cluster = MarkerCluster().add_to(fmap)
for loc in range(0, len(loclist)):
    folium.Marker(loclist[loc], popup=df['Proefnummer'][loc]).add_to(marker_cluster)
fmap

Out[20]:

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