geoDB Access

The geoDB is a service provided by the Euro Data Cube project (EDC) as a payed service. It comes with a Python client that provides hugh level acess to your data and a certain amount of space in a PostGreSQL database. For exploring data you will need at least a read only to the geoDB which you can purchase at the EDC market place.

You can access the service in two ways:

  • By using the Jupyter Python notebook provided by EDC Marketplace (configuartion free, geodb = GeoDBClient())

  • By using you own Jupyter notebook or Python script by providing a client id and secret to the GeoDBClient (geodb = GeoDBClient(client_id="myid", client_secret="mysecet"))

The client ID and secret is also provided by EDC in the latter case. You will find them in your EDC Marketplace account section. You can also provide the credentials via system environment varibles (GEODB_AUTH_CLIENT_ID and GEODB_AUTH_CLIENT_SECRET). These variables can be supplied via a .env file.

Exploring Data

[1]:

from xcube_geodb.core.geodb import GeoDBClient

Login from any maschine

Install xcube geoDB with command:

conda install xcube_geodb -c conda-forge

[20]:

### uncomment if not on EDC
#client_id=YourID
#client_secret=YourSecret
#geodb = GeoDBClient(client_id=client_id, client_secret=client_secret, auth_mode="client-credentials")

Login in EDC environment

[21]:

### comment if not on EDC

geodb = GeoDBClient()

Get your user name

[3]:

geodb.whoami

[3]:

'geodb_ci_test_user'

[4]:

geodb.get_my_collections()


[4]:
owner database table_name
0 geodb_9bfgsdfg-453f-445b-a459 geodb_9bfgsdfg-453f-445b-a459 land_use
1 tt tt tt300 
[5]:

import geopandas

# Have a look at fiona feature schema
collections = {
        "land_use":
        {
            "crs": 3794,
            "properties":
            {
                "RABA_PID": "float",
                "RABA_ID": "float",
                "D_OD": "date"
            }
        }
    }


geodb.create_collections(collections, clear=True)



[5]:

{'collections': {'geodb_ci_test_user_land_use': {'crs': 3794,
                                                 'properties': {'D_OD': 'date',
                                                                'RABA_ID': 'float',
                                                                'RABA_PID': 'float'}}}}

[6]:

gdf = geopandas.read_file('data/sample/land_use.shp')
geodb.insert_into_collection('land_use', gdf.iloc[:100,:]) # minimizing rows to 100, if you are in EDC, you dont need to make the subset.

Processing rows from 0 to 100

[6]:

100 rows inserted into land_use

List Datasets

Step 1: List all datasets a user has access to.

[7]:

geodb.get_my_usage() # to be updated so that all available collections are displayed includign sensible information ont heir availability, e.g. public, purchased, etc..

[7]:

{'usage': '96 kB'}

[8]:

geodb.get_my_collections()

[8]:
owner database table_name
0 geodb_9bfgsdfg-453f-445b-a459 geodb_9bfgsdfg-453f-445b-a459 land_use
1 geodb_ci_test_user geodb_ci_test_user land_use
2 tt tt tt300

Step 2: Let’s get the whole content of a particular data set.

[9]:

gdf = geodb.get_collection('land_use') # to be updated, so that namespace is not needed or something more suitable, e.g. 'public'
gdf

[9]:
id created_at modified_at geometry raba_pid raba_id d_od
0 1 2021-01-22T10:02:34.390867+00:00 None POLYGON ((453952.629 91124.177, 453952.696 911... 4770326 1410 2019-03-26
1 2 2021-01-22T10:02:34.390867+00:00 None POLYGON ((453810.376 91150.199, 453812.552 911... 4770325 1300 2019-03-26
2 3 2021-01-22T10:02:34.390867+00:00 None POLYGON ((456099.635 97696.070, 456112.810 976... 2305689 7000 2019-02-25
3 4 2021-01-22T10:02:34.390867+00:00 None POLYGON ((455929.405 97963.785, 455933.284 979... 2305596 1100 2019-02-25
4 5 2021-01-22T10:02:34.390867+00:00 None POLYGON ((461561.512 96119.256, 461632.114 960... 2310160 1100 2019-03-11
... ... ... ... ... ... ... ...
95 96 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458514.067 93026.352, 458513.306 930... 5960564 1600 2019-01-11
96 97 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458259.239 93110.981, 458259.022 931... 5960569 3000 2019-01-11
97 98 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458199.608 93099.296, 458199.825 930... 5960630 3000 2019-01-11
98 99 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458189.403 93071.618, 458179.669 930... 5960648 1100 2019-01-11
99 100 2021-01-22T10:02:34.390867+00:00 None POLYGON ((454901.777 95801.099, 454889.964 958... 2353305 1321 2019-01-05

100 rows × 7 columns

Step 3: Plot the GeoDataframe, select a reasonable column to diplay

[10]:

gdf.plot(column="raba_id", figsize=(15,15), cmap = 'jet')

[10]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f323b4fa190>
../_images/notebooks_geodb_explore_collections_18_1.png

Step 5: Subselect the data. Here: Select a specific use by defining an ID value to choose

[11]:

gdfsub = geodb.get_collection('land_use', query='raba_id=eq.1410')
gdfsub.head()

[11]:
id created_at modified_at geometry raba_pid raba_id d_od
0 1 2021-01-22T10:02:34.390867+00:00 None POLYGON ((453952.629 91124.177, 453952.696 911... 4770326 1410 2019-03-26
1 62 2021-01-22T10:02:34.390867+00:00 None POLYGON ((457261.001 96349.254, 457256.831 963... 3596498 1410 2019-01-05
2 22 2021-01-22T10:02:34.390867+00:00 None POLYGON ((455384.809 97907.054, 455380.659 979... 3616776 1410 2019-02-25
3 28 2021-01-22T10:02:34.390867+00:00 None POLYGON ((462585.734 93088.987, 462567.020 930... 3826126 1410 2019-01-23
4 32 2021-01-22T10:02:34.390867+00:00 None POLYGON ((457748.827 96167.354, 457748.394 961... 2309744 1410 2019-01-05 
[12]:

gdfsub.plot(column="raba_id", figsize=(15,15), cmap = 'jet')

[12]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f323b199190>
../_images/notebooks_geodb_explore_collections_21_1.png

Step 6: Filter by bbox, limit it to 200 entries

[13]:

gdf = geodb.get_collection_by_bbox(collection="land_use",  bbox = (452750.0, 88909.549, 464000.0, 102486.299), comparison_mode="contains", bbox_crs=3794, limit=200, offset=10)
gdf

[13]:
id created_at modified_at geometry raba_pid raba_id d_od
0 11 2021-01-22T10:02:34.390867+00:00 None POLYGON ((460137.998 95628.898, 460111.001 956... 5983161 1100 2019-03-11
1 12 2021-01-22T10:02:34.390867+00:00 None POLYGON ((453673.609 91328.224, 453678.929 913... 5983074 1600 2019-03-26
2 13 2021-01-22T10:02:34.390867+00:00 None POLYGON ((460312.295 96127.114, 460300.319 961... 5983199 1600 2019-03-11
3 14 2021-01-22T10:02:34.390867+00:00 None POLYGON ((460459.445 96117.356, 460470.516 961... 5983217 1100 2019-03-11
4 15 2021-01-22T10:02:34.390867+00:00 None POLYGON ((457798.753 99628.982, 457783.076 996... 6299143 1600 2019-03-04
... ... ... ... ... ... ... ...
85 96 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458514.067 93026.352, 458513.306 930... 5960564 1600 2019-01-11
86 97 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458259.239 93110.981, 458259.022 931... 5960569 3000 2019-01-11
87 98 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458199.608 93099.296, 458199.825 930... 5960630 3000 2019-01-11
88 99 2021-01-22T10:02:34.390867+00:00 None POLYGON ((458189.403 93071.618, 458179.669 930... 5960648 1100 2019-01-11
89 100 2021-01-22T10:02:34.390867+00:00 None POLYGON ((454901.777 95801.099, 454889.964 958... 2353305 1321 2019-01-05

90 rows × 7 columns

[14]:

gdf.plot(column="raba_pid", figsize=(15,15), cmap = 'jet')

[14]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f323b11b090>
../_images/notebooks_geodb_explore_collections_24_1.png

Step 6: Fltering using PostGres Syntax; see https://www.postgresql.org/docs/9.1/index.html for details

[15]:

gdf = geodb.get_collection_pg(collection='land_use', where='raba_id=1410')
gdf.head()

[15]:
id created_at modified_at geometry raba_pid raba_id d_od
0 1 2021-01-22T10:02:34.390867+00:00 None POLYGON ((453952.629 91124.177, 453952.696 911... 4770326 1410 2019-03-26
1 62 2021-01-22T10:02:34.390867+00:00 None POLYGON ((457261.001 96349.254, 457256.831 963... 3596498 1410 2019-01-05
2 22 2021-01-22T10:02:34.390867+00:00 None POLYGON ((455384.809 97907.054, 455380.659 979... 3616776 1410 2019-02-25
3 28 2021-01-22T10:02:34.390867+00:00 None POLYGON ((462585.734 93088.987, 462567.020 930... 3826126 1410 2019-01-23
4 32 2021-01-22T10:02:34.390867+00:00 None POLYGON ((457748.827 96167.354, 457748.394 961... 2309744 1410 2019-01-05 
[16]:

gdf.plot(column="raba_pid", figsize=(15,15), cmap = 'jet')

[16]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f323b13a9d0>
../_images/notebooks_geodb_explore_collections_27_1.png

Step 7: Fltering using PostGres Syntax Allowing Aggregation Here according to data, note that the data set has been reduced to 200 entries above

[17]:

df = geodb.get_collection_pg('land_use', where='raba_id=1410', group='d_od', select='COUNT(d_od) as ct, d_od')
df.head()

[17]:
ct d_od
0 1 2019-03-04
1 1 2019-03-20
2 1 2019-03-26
3 1 2019-04-01
4 1 2019-02-25 
[18]:

geodb.drop_collection('land_use')

[18]:

Collection ['geodb_ci_test_user_land_use'] deleted

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