Visualizing project locations with leafmap¶

This guide will walk you through the steps for visualizing the location of your D2S projects with leafmap.

To get started, you will need to have access to a D2S instance where you have created projects and the open-source library leafmap added to your Python environment.

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# Uncomment and run the following line if working out of Google Colab
# !pip install d2spy
# !pip install leafmap

# Uncomment and run the following line if working out of Google Colab # !pip install d2spy # !pip install leafmap

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import leafmap
import pandas as pd

from d2spy.workspace import Workspace

import leafmap import pandas as pd from d2spy.workspace import Workspace

You must connect to your D2S workspace before you can request any data. The Workspace module's connect method can be used to login to a D2S instance and connect to your workspace in one go.

Note: This tutorial uses a D2S instance hosted at https://ps2.d2s.org. You will need to have an account and access to data on this instance to use it. Change the URL if you are self-hosting an instance or using an instance hosted elsewhere.

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# Connect to D2S workspace
workspace = Workspace.connect("https://ps2.d2s.org", "yourD2Semail@example.com")

# Connect to D2S workspace workspace = Workspace.connect("https://ps2.d2s.org", "yourD2Semail@example.com")

The Workspace get_projects method will retrieve a list of the projects your account can currently access on the D2S instance.

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# Get list of all your projects
projects = workspace.get_projects()
print(projects[0])

# Get list of all your projects projects = workspace.get_projects() print(projects[0])

The projects variable now contains a list of Project objects. You can access attributes such as title, description, start_date, end_date, and field from a Project object. The field attribute contains a GeoJSON representation of the field boundary provided during the project's creation.

Next, leafmap will be used to plot markers on a map at the location of each of your projects. It requires the location information to be stored in either a CSV file or pandas DataFrame. The next two cells will create a pandas DataFrame containing your project titles and center coordinates.

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# Now make a dictionary that can be used to create a DataFrame
data = []
# Store the project title and the project center coordinates for each project in the workspace
for project in projects:
    data.append([project.title, project.centroid['x'], project.centroid['y']])

# Now make a dictionary that can be used to create a DataFrame data = [] # Store the project title and the project center coordinates for each project in the workspace for project in projects: data.append([project.title, project.centroid['x'], project.centroid['y']])

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# Now create a Pandas dataFrame
df = pd.DataFrame(data, columns=['title', 'x', 'y'])

# Now create a Pandas dataFrame df = pd.DataFrame(data, columns=['title', 'x', 'y'])

With the project titles and center coordinates now stored in a DataFrame, you can plot them using leafmap.

Leafmap will cluster nearby project markers. The total number of projects within a cluster will be shown at the center of the cluster symbol. When you hover over a cluster, a bounding box for the cluster's region will appear. Clicking on a cluster will zoom the map to the extent of the cluster's bounding box.

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# Now create a map
Map = leafmap.Map()
Map.add_xy_data(df, x="x", y="y", layer_name="My D2S Projects")
Map

# Now create a map Map = leafmap.Map() Map.add_xy_data(df, x="x", y="y", layer_name="My D2S Projects") Map

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# Create map displaying project boundary for first project
Map = leafmap.Map()
Map.add_geojson(projects[1].get_project_boundary(), layer_name=projects[1].title, fill_colors=['#daaa00'], zoom_to_layer=True)
Map

# Create map displaying project boundary for first project Map = leafmap.Map() Map.add_geojson(projects[1].get_project_boundary(), layer_name=projects[1].title, fill_colors=['#daaa00'], zoom_to_layer=True) Map

We can also retrieve any map layers associated with our project using the project's get_map_layers method. This method will return a list of GeoJSON FeatureCollections objects for each map layer.

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# Create map for displaying map layers
Map = leafmap.Map()
# Use filter_by_title to find project with map layers
demo_projects = projects.filter_by_title("Demo")
if len(demo_projects) > 0:
    project_with_map_layers = demo_projects[0]
    # Returns list of dictionaries in GeoJSON FeatureCollection structure for each project map layer
    map_layers = project_with_map_layers.get_map_layers()
    # Display map layers on map
    for map_layer in map_layers:
        Map.add_geojson(map_layer, fill_colors=["#daaa00"])
Map

# Create map for displaying map layers Map = leafmap.Map() # Use filter_by_title to find project with map layers demo_projects = projects.filter_by_title("Demo") if len(demo_projects) > 0: project_with_map_layers = demo_projects[0] # Returns list of dictionaries in GeoJSON FeatureCollection structure for each project map layer map_layers = project_with_map_layers.get_map_layers() # Display map layers on map for map_layer in map_layers: Map.add_geojson(map_layer, fill_colors=["#daaa00"]) Map

Once finished viewing your data, you can revoke your authorization session by logging out.

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# Removes access token from future requests
workspace.logout()

# Removes access token from future requests workspace.logout()