Example of DOV search methods for quartaire stratigrafie¶
Use cases:¶
Select records in a bbox
Select records in a bbox with selected properties
Select records within a distance from a point
Select records in a municipality
Get records using info from wfs fields, not available in the standard output dataframe
[1]:
%matplotlib inline
import os, sys
import inspect
[2]:
# check pydov path
import pydov
Get information about code base¶
[3]:
from pydov.search.interpretaties import QuartairStratigrafieSearch
# information about the QuartairStratigrafie type (In Dutch):
ip_quart = QuartairStratigrafieSearch()
ip_quart.get_description()
[3]:
'De quartaire afzettingen in Vlaanderen, de jongste afzettingen, zijn lateraal en verticaal heterogeen en varieren erg in dikte. De afzettingen zijn vooral onder continentale omstandigheden tot stand gekomen (rivier-, duinafzettingen). Eigenlijk zijn het pakketten die zelden meer dan 30 meter en soms zelfs minder dan 1 meter dik zijn. Omwille van deze grote eigenheid en de grote mate van detail van de beschrijvingen, werd hiervoor een eigen stratigrafische codering uitgewerkt voor Vlaanderen.'
[4]:
# information about the available fields for a QuartairStratigrafie object
fields = ip_quart.get_fields()
# print available fields
for f in fields.values():
print(f['name'])
pkey_interpretatie
Type_proef
Proefnummer
pkey_boring
x
y
start_interpretatie_mtaw
diepte_tot_m
gemeente
Auteurs
Datum
Opdrachten
betrouwbaarheid_interpretatie
Geldig_van
Geldig_tot
eerste_invoer
geom
diepte_laag_van
diepte_laag_tot
lid1
relatie_lid1_lid2
lid2
[5]:
# print information for a certain field
fields['Type_proef']
[5]:
{'name': 'Type_proef',
'definition': "Het type van de proef waarvan de quartaire stratigrafie een interpretatie is. Bij een quartaire stratigrafie zal dit altijd 'Boring' zijn.",
'type': 'string',
'notnull': False,
'query': True,
'cost': 1,
'values': {'Boring': 'De quartaire stratigrafie is een interpretatie van een boring.'}}
The cost is an arbitrary attribute to indicate if the information is retrieved from a wfs query (cost = 1), or from an xml (cost = 10)
Try-out of use cases¶
Select interpretations in a bbox¶
[6]:
from pydov.util.location import Within, Box
# Get all interpretations in a bounding box (llx, lly, ulx, uly)
# the pkey_boring link is not available below, but is in the df
df = ip_quart.search(location=Within(Box(132815, 191700, 133815, 192700)))
df.head()
[000/001] .
[000/003] ...
[6]:
| pkey_interpretatie | pkey_boring | betrouwbaarheid_interpretatie | x | y | start_interpretatie_mtaw | diepte_laag_van | diepte_laag_tot | lid1 | relatie_lid1_lid2 | lid2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 0.0 | 4.5 | K | T | K |
| 1 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 4.5 | 7.5 | F | T | F |
| 2 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 7.5 | 8.0 | Rf | T | Rf |
| 3 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132949.0 | 192068.0 | 2.57 | 0.0 | 4.5 | K | T | K |
| 4 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132949.0 | 192068.0 | 2.57 | 4.5 | 9.5 | F | T | F |
Select interpretations in a bbox with selected properties¶
[7]:
# list available query methods
methods = [i for i,j in inspect.getmembers(sys.modules['owslib.fes2'],
inspect.isclass)
if 'Property' in i]
methods
[7]:
['PropertyIsBetween',
'PropertyIsEqualTo',
'PropertyIsGreaterThan',
'PropertyIsGreaterThanOrEqualTo',
'PropertyIsLessThan',
'PropertyIsLessThanOrEqualTo',
'PropertyIsLike',
'PropertyIsNotEqualTo',
'PropertyIsNull',
'SortProperty']
[8]:
from owslib.fes2 import PropertyIsGreaterThanOrEqualTo
The property feature methodes listed above are available from the owslib module. These were not adapted for use in pydov.
[9]:
# Get deep boreholes in a bounding box
from owslib.fes2 import PropertyIsLike
# the propertyname can be any of the fields of the interpretations object that belong to the wfs source
# the literal is always a string, no matter what its definition is in the boring object (string, float...)
query = PropertyIsLike(
propertyname='betrouwbaarheid_interpretatie', literal='onbekend')
df = ip_quart.search(location=Within(Box(132815, 191700, 133815, 192700)),
query=query
)
df.head()
[000/001] .
[000/003] ccc
[9]:
| pkey_interpretatie | pkey_boring | betrouwbaarheid_interpretatie | x | y | start_interpretatie_mtaw | diepte_laag_van | diepte_laag_tot | lid1 | relatie_lid1_lid2 | lid2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 0.0 | 4.5 | K | T | K |
| 1 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 4.5 | 7.5 | F | T | F |
| 2 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 7.5 | 8.0 | Rf | T | Rf |
| 3 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132949.0 | 192068.0 | 2.57 | 0.0 | 4.5 | K | T | K |
| 4 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132949.0 | 192068.0 | 2.57 | 4.5 | 9.5 | F | T | F |
Select records within a distance from a point¶
[10]:
from pydov.util.location import Point, WithinDistance
# Get all interpretations within a defined distance from a point
location = WithinDistance(location=Point(133000, 192000),
distance=1000, distance_unit='meter')
df = ip_quart.search(location=location)
df.head()
[000/001] .
[000/015] cc.........c...
[10]:
| pkey_interpretatie | pkey_boring | betrouwbaarheid_interpretatie | x | y | start_interpretatie_mtaw | diepte_laag_van | diepte_laag_tot | lid1 | relatie_lid1_lid2 | lid2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 0.0 | 4.50 | K | T | K |
| 1 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 4.5 | 7.50 | F | T | F |
| 2 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1974... | onbekend | 132975.0 | 191969.0 | 2.84 | 7.5 | 8.00 | Rf | T | Rf |
| 3 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/2016... | onbekend | 133144.8 | 191408.3 | 5.53 | 0.0 | 4.20 | D | T | D |
| 4 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/2016... | onbekend | 133144.8 | 191408.3 | 5.53 | 4.2 | 10.41 | F | T | F |
Select interpretations in a municipality¶
[11]:
from owslib.fes2 import PropertyIsEqualTo
query = PropertyIsEqualTo(propertyname='gemeente',
literal='Hamme')
df = ip_quart.search(query=query)
df.head()
[000/001] .
[000/028] ............................
[11]:
| pkey_interpretatie | pkey_boring | betrouwbaarheid_interpretatie | x | y | start_interpretatie_mtaw | diepte_laag_van | diepte_laag_tot | lid1 | relatie_lid1_lid2 | lid2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1894... | goed | 128930.0 | 198740.0 | 4.0 | 0.0 | 12.2 | F | T | F |
| 1 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1894... | goed | 128930.0 | 198740.0 | 4.0 | 12.2 | 20.4 | $ | T | $ |
| 2 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1909... | onbekend | 134722.6 | 194263.5 | 2.0 | 0.0 | 2.9 | ! | T | ! |
| 3 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1909... | onbekend | 134722.6 | 194263.5 | 2.0 | 2.9 | 13.9 | F | T | F |
| 4 | https://www.dov.vlaanderen.be/data/interpretat... | https://www.dov.vlaanderen.be/data/boring/1909... | onbekend | 134722.6 | 194263.5 | 2.0 | 13.9 | 35.0 | $ | T | $ |
Visualize results¶
Using Folium, we can display the results of our search on a map.
[12]:
# import the necessary modules (not included in the requirements of pydov!)
import folium
from folium.plugins import MarkerCluster
from pyproj import Transformer
# for windows 10 users, we could not get it working with Microsoft Edge
# change the browser to Chrome in the Notebook config file instead
[13]:
# 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()
[14]:
# 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['lid1'][loc]).add_to(marker_cluster)
fmap
[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook