Canis lupus meets Felis silvestris

++ Currently, the examples are being reworked after the latest update because GBIF behaves differently now. Find out more. ++

This workflow uses the VAT to compare the occurrence of Canis lupus and Felis silvestris as a function of land use classification from the Ökosystematlas.

The purpose of this notebook is also to demonstrate the capabilities of Geo Engine. Therefore some useful techniques will be shown:

  • Using the GBIF data catalogue
  • Point in polygon selection
  • Raster vector join of occurrence data with land use classification
  • Plotting a class histogram
  • Nested workflows

When building your own nested workflow, it is recommended to build it in several steps as shown in this notebook.

Documentation about the operators and how to use them in Python can be found here: https://docs.geoengine.io/operators/intro.html

Preparation

#Import packages
import geoengine as ge
import geoengine_openapi_client
from datetime import datetime
from geoengine.types import RasterBandDescriptor
import altair as alt

alt.renderers.enable('default')

RendererRegistry.enable('default')

#Initialize Geo Engine in VAT
ge.initialize("https://vat.gfbio.org/api")

#Get the GBIF DataProvider id (Useful for translating the DataProvider name to its id)
root_collection = ge.layer_collection()
gbif_prov_id = ''
for elem in root_collection.items:
    if elem.name == 'GBIF':
        gbif_prov_id = str(elem.provider_id)
        
gbif_prov_id

'1c01dbb9-e3ab-f9a2-06f5-228ba4b6bf7a'

Load boundaries of Germany for later GBIF occurrence extraction (optional)

This chapter is not required and only shows that country borders are available.

#Create workflow to request German border
workflow_germany = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "OgrSource",
        "params": {
            "data": "germany",
        }
    }
})

workflow_germany

2429a993-385f-546f-b4f7-97b3ba4a5adb

#Set time
start_time = datetime.strptime(
    '2000-04-01T12:00:00.000Z', "%Y-%m-%dT%H:%M:%S.%f%z")
end_time = datetime.strptime(
    '2030-04-01T12:00:00.000Z', "%Y-%m-%dT%H:%M:%S.%f%z")

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_germany.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot()

<Axes: >

output_10_1.png

Load Ökosystematlas for later raster vector join with occurrence data (optional)

This chapter is not needed and only shows that raster data is also available.

#Create a workflow to request the oekosystematlas raster data
workflow_oekosystematlas = ge.register_workflow({ 
    "type": "Raster",
    "operator": {
        "type": "GdalSource",
        "params": {
            "data": "oekosystematlas"
        }
    }
})

workflow_oekosystematlas

8a859eeb-0778-5190-a9d1-b1f787e4176d

#Request the data from Geo Engine into a xarray dataarray
data = workflow_oekosystematlas.get_xarray(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot(vmax=75)

/home/duempelmann/geoengine_env/lib/python3.10/site-packages/owslib/coverage/wcs110.py:85: FutureWarning: The behavior of this method will change in future versions. Use specific 'len(elem)' or 'elem is not None' test instead.
  elem = self._capabilities.find(self.ns.OWS('ServiceProvider')) or self._capabilities.find(self.ns.OWS('ServiceProvider'))  # noqa





<matplotlib.collections.QuadMesh at 0x7f67d1c4ada0>

output_14_2.png

Processing Canis lupus

None of the following steps are theoretically necessary, as the entire workflow will be projected in the nested request in the end. However, the steps are intended to show the capabilities of Geo Engine.

Load Canis lupus (Optional)

#Create workflow to request Canis lupus incidents
workflow_canis_lupus = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "OgrSource",
        "params": {
            "data": f"_:{gbif_prov_id}:`species/Canis lupus`",
        }
    }
})

workflow_canis_lupus.get_result_descriptor()

Data type:         MultiPoint
Spatial Reference: EPSG:4326
Columns:
  gbifid:
    Column Type: int
    Measurement: unitless
  scientificname:
    Column Type: text
    Measurement: unitless
  basisofrecord:
    Column Type: text
    Measurement: unitless

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_canis_lupus.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot()

<Axes: >

output_19_1.png

Point in Polygon Canis lupus

#Create workflow to request Canis lupus incidents filtered by German border
workflow_canis_lupus_cut = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "PointInPolygonFilter",
        "params": {},
        "sources": {
            "points": { #Canis lupus ###############################
                "type": "OgrSource",
                "params": {
                    "data": f"_:{gbif_prov_id}:`species/Canis lupus`",
                    "attributeProjection": []
                } 
            }, #####################################################
            "polygons": { #Germany #################################
                "type": "OgrSource",
                "params": {
                    "data": "germany"
                }
            } ######################################################
        } 
    }
})

workflow_canis_lupus_cut

f30ac841-81b0-5301-bac6-840dd914c1ba

#Request the data from Geo Engine into a geopandas dataframe
data_canis_lupus = workflow_canis_lupus_cut.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data_canis_lupus.plot()

<Axes: >

output_22_1.png

Nested Point in Polygon and Raster Vector Join Canis lupus

#Create a workflow to request Canis lupus occurrences filtered by the German border and linked to the Ökosystematlas data.
workflow_canis_lupus_cut_join = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "RasterVectorJoin",
        "params": {
            "names": {
                "type": "names",
                "values": ["Ökosystematlas"]
            },  
            "temporalAggregation": "none",
            "featureAggregation": "mean",
        },
        "sources": {
            "vector": { #Canis lupus cut ######################################
                "type": "PointInPolygonFilter", 
                "params": {},
                "sources": {
                    "points": {
                        "type": "OgrSource",
                        "params": {
                            "data": f"_:{gbif_prov_id}:`species/Canis lupus`",
                            "attributeProjection": []
                        }
                    },
                    "polygons": {
                        "type": "OgrSource",
                        "params": {
                            "data": "germany"
                        }
                    }
                }
            }, ##############################################################
            "rasters": [{ #Ökosystematlas ###################################
                "type": "GdalSource",
                "params": {
                    "data": "oekosystematlas"
                }
            }] ##############################################################
        },
    }
})

workflow_canis_lupus_cut_join

2c8ebbbc-b848-58e6-8f5c-f51976db3c8f

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_canis_lupus_cut_join.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    ),
    resolve_classifications=True
)

#Show the geopandas dataframe
data
geometry basisofrecord gbifid scientificname Ökosystematlas start end
0 POINT (9.49776 52.08503) HUMAN_OBSERVATION 3447336010 Canis familiaris Linnaeus, 1758 Laubwälder 2022-01-06 00:00:00+00:00 2022-01-06 00:00:00+00:00
1 POINT (8.63148 50.01629) HUMAN_OBSERVATION 1579887520 Canis familiaris Linnaeus, 1758 Verkehrsinfrastruktur 2017-03-11 00:00:00+00:00 2017-03-11 00:00:00+00:00
2 POINT (9.55500 48.97333) HUMAN_OBSERVATION 1579896270 Canis familiaris Linnaeus, 1758 Mischwälder 2017-01-01 00:00:00+00:00 2017-01-01 00:00:00+00:00
3 POINT (6.14376 50.81583) HUMAN_OBSERVATION 1883797122 Canis familiaris Linnaeus, 1758 Grünland 2018-05-14 00:00:00+00:00 2018-05-14 00:00:00+00:00
4 POINT (10.29174 48.88160) HUMAN_OBSERVATION 1891284730 Canis familiaris Linnaeus, 1758 Laubwälder 2018-08-16 00:00:00+00:00 2018-08-16 00:00:00+00:00
... ... ... ... ... ... ... ...
1336 POINT (14.90000 51.35000) HUMAN_OBSERVATION 3725545490 Canis lupus Linnaeus, 1758 Nadelwälder 2019-01-13 00:00:00+00:00 2019-01-13 00:00:00+00:00
1337 POINT (12.42115 51.19143) HUMAN_OBSERVATION 3712440633 Canis lupus Linnaeus, 1758 Siedlungsfläche mit niedriger Baudichte 2022-03-05 17:27:07+00:00 2022-03-05 17:27:07+00:00
1338 POINT (14.20000 51.45000) HUMAN_OBSERVATION 2837851869 Canis lupus Linnaeus, 1758 Siedlungsfläche mit niedriger Baudichte 2019-04-26 00:00:00+00:00 2019-04-26 00:00:00+00:00
1339 POINT (14.85000 51.35000) HUMAN_OBSERVATION 2836478160 Canis lupus Linnaeus, 1758 Ackerland 2019-01-13 00:00:00+00:00 2019-01-13 00:00:00+00:00
1340 POINT (6.51747 49.46328) HUMAN_OBSERVATION 2511463696 Canis lupus Linnaeus, 1758 Laubwälder 2014-01-01 00:00:00+00:00 2014-01-01 00:00:00+00:00

1341 rows × 7 columns

It can be seen that the Ökosystematlas variable is numerical, while the classes are human-readable encoded in the metadata of the files. This can be adjusted using a class histogram

Nested Full Workflow Canis lupus

#Create a workflow to plot Canis lupus occurrences filtered by the German border and merged with Ökosystematlas data as a class histogram.
workflow_canis_lupus_full = ge.register_workflow({
    "type": "Plot",
    "operator": {
       "type": "ClassHistogram",
       "params": {
          "columnName": "Ökosystematlas"
       },
        "sources": {
            "source": { #Canis lupus cut join #####################################
                "type": "RasterVectorJoin",
                "params": {
                        "names": {
                            "type": "names",
                            "values": ["Ökosystematlas"]
                        }, 
                        "temporalAggregation": "none",
                        "featureAggregation": "mean",
                },
                "sources": {
                    "vector": {
                        "type": "PointInPolygonFilter",
                        "params": {},
                        "sources": {
                            "points": {
                                "type": "OgrSource",
                                "params": {
                                    "data": f"_:{gbif_prov_id}:`species/Canis lupus`",
                                    "attributeProjection": []
                                }
                            },
                            "polygons": {
                                "type": "OgrSource",
                                "params": {
                                    "data": "germany"
                                }
                            }
                        }
                    },
                    "rasters": [{
                        "type": "GdalSource",
                        "params": {
                            "data": "oekosystematlas"
                        }
                    }]
                }
            } ######################################################################
       }
    }
})
    
workflow_canis_lupus_full

b182c10b-59ce-5d5b-946f-fccc3ae04c88

#Request the plot from Geo Engine
plot_canis_lupus = workflow_canis_lupus_full.plot_chart(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Show the plot
alt.Chart.from_dict(plot_canis_lupus.spec)

Processing Felis silvestris

None of the following steps are theoretically necessary, as the entire workflow will be projected in the nested request in the end. However, the steps are intended to show the capabilities of Geo Engine.

Load Felis silvestris (Optional)

#Create workflow to request Felis silvestris occurrences
workflow_felis_silvestris = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "OgrSource",
        "params": {
            "data": f"_:{gbif_prov_id}:`species/Felis silvestris`",
        }
    }
})

workflow_felis_silvestris

f8d5abd5-7d5f-567e-97a2-7830052d6cbf

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_felis_silvestris.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot()

<Axes: >

output_34_1.png

Point in Polygon Felis silvestris

#Create workflow to request Felis silvestris occurrences filtered by German border
workflow_felis_silvestris_cut = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "PointInPolygonFilter",
        "params": {},
        "sources": {
            "points": { #Felis silvestris ################################
                "type": "OgrSource",
                "params": {
                    "data": f"_:{gbif_prov_id}:`species/Felis silvestris`",
                    "attributeProjection": []
                }
            }, ###########################################################
            "polygons": { #Germany #######################################
                "type": "OgrSource",
                "params": {
                    "data": "germany"
                }
            } ############################################################
        } 
    }
})

workflow_felis_silvestris_cut

518c27b3-0ce7-56ac-b826-5a72be463a73

#Request the data from Geo Engine into a geopandas dataframe
data_felis_silvestris = workflow_felis_silvestris_cut.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data_felis_silvestris.plot()

<Axes: >

output_37_1.png

Nested Point in Polygon and Raster Vector Join Felis silvestris

#Create a workflow to request Felis silvestris occurrences filtered by the German border and linked to the Ökosystematlas data.
workflow_felis_silvestris_cut_join = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "RasterVectorJoin",
        "params": {
                "names": {
                    "type": "names",
                    "values": ["Ökosystematlas"]
                }, 
                "temporalAggregation": "none",
                "featureAggregation": "mean",
        },
        "sources": {
            "vector": { #Felis silvestris cut #####################################
                "type": "PointInPolygonFilter", 
                "params": {},
                "sources": {
                    "points": {
                        "type": "OgrSource",
                        "params": {
                            "data": f"_:{gbif_prov_id}:`species/Felis silvestris`",
                            "attributeProjection": []
                        }
                    },
                    "polygons": {
                        "type": "OgrSource",
                        "params": {
                            "data": "germany"
                        }
                    }
                }
            }, ###################################################################
            "rasters": [{ #Ökosystematlas ########################################
                "type": "GdalSource",
                "params": {
                    "data": "oekosystematlas"
                }
            }] ###################################################################
        },
    }
})

workflow_felis_silvestris_cut_join

355b4e59-65cc-5cfe-a0b4-636f4d41beab

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_felis_silvestris_cut_join.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    ),
    resolve_classifications=True
)

#Show the geopandas dataframe
data
geometry basisofrecord gbifid scientificname Ökosystematlas start end
0 POINT (8.08720 50.78140) MATERIAL_SAMPLE 3774757042 Felis silvestris Schreber, 1777 Laubwälder 2015-09-13 00:00:00+00:00 2015-09-13 00:00:00+00:00
1 POINT (6.74050 50.43160) PRESERVED_SPECIMEN 3774755207 Felis silvestris Schreber, 1777 Grünland 2017-10-11 00:00:00+00:00 2017-10-11 00:00:00+00:00
2 POINT (6.36984 50.50914) HUMAN_OBSERVATION 1828993691 Felis silvestris Schreber, 1777 Natürliche und extensiv genutzte Grünflächen 2018-02-24 00:00:00+00:00 2018-02-24 00:00:00+00:00
3 POINT (6.92310 50.62580) PRESERVED_SPECIMEN 3774754593 Felis silvestris Schreber, 1777 Ackerland 2017-11-08 00:00:00+00:00 2017-11-08 00:00:00+00:00
4 POINT (6.87770 50.42950) PRESERVED_SPECIMEN 3774753913 Felis silvestris Schreber, 1777 Nadelwälder 2003-10-14 00:00:00+00:00 2003-10-14 00:00:00+00:00
... ... ... ... ... ... ... ...
1116 POINT (6.13130 50.10320) HUMAN_OBSERVATION 3695923471 Felis silvestris Schreber, 1777 Laubwälder 2016-07-19 00:00:00+00:00 2016-07-19 00:00:00+00:00
1117 POINT (6.13130 50.10320) HUMAN_OBSERVATION 3695924066 Felis silvestris Schreber, 1777 Laubwälder 2019-01-09 00:00:00+00:00 2019-01-09 00:00:00+00:00
1118 POINT (6.13130 50.10320) HUMAN_OBSERVATION 3695924069 Felis silvestris Schreber, 1777 Laubwälder 2019-01-04 00:00:00+00:00 2019-01-04 00:00:00+00:00
1119 POINT (8.29065 50.12195) HUMAN_OBSERVATION 841588052 Felis silvestris Schreber, 1777 Nadelwälder 2013-08-06 17:51:20+00:00 2013-08-06 17:51:20+00:00
1120 POINT (6.13130 50.10320) HUMAN_OBSERVATION 3695923382 Felis silvestris Schreber, 1777 Laubwälder 2019-11-01 00:00:00+00:00 2019-11-01 00:00:00+00:00

1121 rows × 7 columns

Nested Full Workflow Felis silvestris

#Create a workflow to plot Felis silvestris occurrences filtered by the German border and merged with the Ökosystematlas data as a class histogram.
workflow_felis_silvestris_full = ge.register_workflow({
    "type": "Plot",
    "operator": {
       "type": "ClassHistogram",
       "params": {
          "columnName": "Ökosystematlas"
       },
        "sources": {
            "source": {
                "type": "RasterVectorJoin",
                "params": {
                        "names": {
                            "type": "names",
                            "values": ["Ökosystematlas"]
                        }, 
                        "temporalAggregation": "none",
                        "featureAggregation": "mean",
                },
                "sources": {
                    "vector": {
                        "type": "PointInPolygonFilter",
                        "params": {},
                        "sources": {
                            "points": {
                                "type": "OgrSource",
                                "params": {
                                    "data": f"_:{gbif_prov_id}:`species/Felis silvestris`",
                                    "attributeProjection": []
                                }
                            },
                            "polygons": {
                                "type": "OgrSource",
                                "params": {
                                    "data": "germany"
                                }
                            }
                        }
                    },
                    "rasters": [{
                        "type": "GdalSource",
                        "params": {
                            "data": "oekosystematlas"
                        }
                    }]
                }
            }
       }
    }
})
    
workflow_felis_silvestris_full

db03640c-cf0e-5fe0-978c-f45a55eb5da3

#Request the plot from Geo Engine
plot_felis_silvestris = workflow_felis_silvestris_full.plot_chart(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Show the plot
alt.Chart.from_dict(plot_felis_silvestris.spec)

Comparison Canis lupus and Felis silvestris

#Show the plot from Canis lupus
alt.Chart.from_dict(plot_canis_lupus.spec)

#Show the plot from Felis silvestris
alt.Chart.from_dict(plot_felis_silvestris.spec)

Further experiments

In this chapter, some other useful links between Geo Engine and Python are shown.

#Comparison plots
import pandas as pd

# Convert the JSON data to pandas DataFrames
df1 = pd.DataFrame(plot_canis_lupus.spec['data']['values'])
df2 = pd.DataFrame(plot_felis_silvestris.spec['data']['values'])

df1['dataset'] = 'Canis lupus'
df2['dataset'] = 'Felis silvestris'

combined_df = pd.concat([df1, df2])

chart = alt.Chart(combined_df).mark_bar().encode(
    x=alt.X('Land Cover:N', title='Land Cover'),
    y=alt.Y('Frequency:Q', title='Frequency'),
    color=alt.Color('dataset:N', title='Dataset'),
    xOffset=alt.Color('dataset:N', title='Dataset')
).properties(width=600)

# Display the grouped barplot
chart

#Plotting of multiple species
import geopandas as gpd

gdf1 = data_canis_lupus
gdf2 = data_felis_silvestris

gdf1['dataset'] = 'Canis lupus'
gdf2['dataset'] = 'Felis silvestris'

combined_gdf = pd.concat([gdf1, gdf2])

combined_gdf.plot(column='dataset', cmap='rainbow', markersize=5, legend=True)

<Axes: >

output_50_1.png