Visualization

HERE Inspector

You can visualize data in Jupyter notebooks using the HERE Inspector package. The inspector allows you to visualize data from various sources, including the HERE platform. It provides an interface designed to support multiple backends. Currently, supported backends are:

Note: The map widget used in this visualization is the default widget provided by the HERE map service. In order to use the widget, please ensure that you have set the environment variable "LS_API_KEY" with your HERE API key. Additionally, please note that custom styling options such as zoom, center, basemap, theme, and colors are not currently supported by the Kepler.gl library.

Capabilities include:

  • Display interactive maps
  • Set basemap
  • Add layers
  • Center and zoom
  • Display interactive charts
  • Simple programmatic interface
  • Support for displaying heatmaps, choropleths, markers, clustering, and styling
  • UI controls for interactive applications

Data formats supported:

Basic Visualization

The simplest usage of Inspector is to call its inspect function for immediate one-line visualization of georeferenced data within a Jupyter notebook.

Inspect Single Dataset

Note: To use a specific map widget, you will need to specify the appropriate inspector_class and api_key options. For example, to use the Ipyleaflet map widget, you would set options.inspector_class = IpyleafletInspector and options.api_key = None. Similarly, to use the HERE Map or Kepler.gl map widgets, you would set options.inspector_class = HEREMapInspector or options.inspector_class = KeplerInspector, respectively and set api_key as per the requirement of widgets.

Here Map Widget
import json
from here.inspector import inspect

# Dataset type = GeoJSON file
# This file contains polygon features describing neighborhood boundaries in Chicago
with open('sample_datasets/neighborhood_boundaries.geojson') as f:
    area_boundaries = json.load(f)
#By default if no inspector class is set , sdk will take HERE Map widget for map visualization.
#user can manually set it as follows:-
from here.inspector import options,HEREMapInspector
options.inspector_class = HEREMapInspector
options.api_key = 'Your actual HERE API key'

inspect(area_boundaries)
Here Map Widget
Figure 1. Here Map Widget
Ipyleaflet Widget
import json
from here.inspector import inspect

# Dataset type = GeoJSON file
# This file contains polygon features describing neighborhood boundaries in Chicago
with open('sample_datasets/neighborhood_boundaries.geojson') as f:
    area_boundaries = json.load(f)

from here.inspector import options,IpyleafletInspector
options.inspector_class = IpyleafletInspector
options.api_key = None

inspect(area_boundaries)
Ipyleaflet Widget
Figure 2. Ipyleaflet Widget
Kepler Widget
import json
from here.inspector import inspect

# Dataset type = GeoJSON file
# This file contains polygon features describing neighborhood boundaries in Chicago
with open('sample_datasets/neighborhood_boundaries.geojson') as f:
    area_boundaries = json.load(f)

from here.inspector import options,KeplerInspector
options.inspector_class = KeplerInspector
options.api_key = None

inspect(area_boundaries)
Kepler Widget
Figure 3. Kepler Widget

Inspect Multiple Datasets

import geopandas as gpd

# Dataset type = GeoDataFrame
# This dataframe contains all Chicago Transit Authority rail station entrance locations
cta_df = gpd.read_file('sample_datasets/cta_entrances.geojson')
cta_df.head()
name agency line geometry
0 18th CTA Pink Line POINT (-87.669144 41.857849)
1 35th/Archer CTA Orange Line POINT (-87.680632 41.829274)
2 95th-Dan Ryan CTA Red Line POINT (-87.62441 41.722729)
3 Adams/Wabash CTA Brown, Purple, Orange, Pink, Green Lines POINT (-87.625997 41.879715)
4 Addison CTA Blue Line POINT (-87.718406 41.946604)
Here Map Widget

from here.inspector import options,HEREMapInspector
options.inspector_class = HEREMapInspector
options.api_key = 'Your actual HERE API key'

inspect(layers={'Neighborhoods': area_boundaries, 'Stations': cta_df})
Here Map Widget
Figure 4. Here Map Widget
Ipyleaflet Widget

from here.inspector import options,IpyleafletInspector
options.inspector_class = IpyleafletInspector
options.api_key = None

inspect(layers={'Neighborhoods': area_boundaries, 'Stations': cta_df})
Ipyleaflet Map Widget
Figure 5. Ipyleaflet Map Widget
Kepler Widget

from here.inspector import options,KeplerInspector
options.inspector_class = KeplerInspector
options.api_key = None

inspect(layers={'Neighborhoods': area_boundaries, 'Stations': cta_df})
Kepler Map Widget
Figure 6. Kepler Map Widget

Custom styles can be specified by passing the additional layers_style parameter or by simply passing tuples.

Note:- Custom styling feature is available only for HERE Map and Ipyleaflet widget.

from here.inspector import Color

inspect(layers=[
    ('Neighborhoods', area_boundaries, Color.AQUA),
    ('Stations', cta_df, Color.RED)
])

Advanced Styling

The Inspect interface provides basic configuration options including:

Advanced styling capabilities are available for supported rendering backends (HERE Map Widget for Jupyter , ipyleaflet and

  • Kepler Widget ). The Inspector.backend function provides access to all advanced functionalities of the rendering backend, allowing unlimited customization.

Please see the Tutorial Notebooks named ExploreInspector_ipyleaflet.ipynb , ExploreInspector_kepler.ipynb and ExploreInspector_HereMapWidgetForJupyter.ipynb for comprehensive examples of all visualization options.

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