Creating a Map Using GeoJSON Data in Altair

Creating a map using GeoJSON data in Altair can be a powerful way to visualize geographical data. Altair, a declarative statistical visualization library for Python, allows you to create interactive and visually appealing maps with relatively simple code. In this article, we will explore how to make a map using GeoJSON data in Altair, covering the necessary steps and providing examples to guide you through the process.

Understanding GeoJSON and Altair

GeoJSON is a format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON). It is widely used for representing geographical features with their non-spatial attributes. Altair, on the other hand, is a Python library based on Vega and Vega-Lite, which provides a high-level grammar for creating statistical graphics.

Steps to Create a Map Using GeoJSON in Altair

Before we start creating maps, ensure that you have Altair and GeoPandas installed in your Python environment. You can install these packages using pip:

pip install altair geopandas

GeoPandas is a library that makes working with geospatial data in Python easier. It extends the datatypes used by pandas to allow spatial operations on geometric types.

1. Loading GeoJSON Data

To create a map, you first need to load your GeoJSON data. You can do this using the geopandas library, which can read various spatial data formats, including GeoJSON. We will create random GeoJSON data with 10 features, each representing a point with random coordinates. This data can be used to test and visualize maps in Altair.

import json
import random

# Function to generate random coordinates
def generate_random_coordinates(num_points):
    return [[random.uniform(-180, 180), random.uniform(-90, 90)] for _ in range(num_points)]

# Generating random GeoJSON data
random_geojson = {
    "type": "FeatureCollection",
    "features": [
        {
            "type": "Feature",
            "properties": {
                "name": f"Random Feature {i}"
            },
            "geometry": {
                "type": "Point",
                "coordinates": generate_random_coordinates(1)[0]  # Single random point
            }
        } for i in range(10)  # 10 random points
    ]
}

# Save to file
with open('random_geojson_data.geojson', 'w') as f:
    json.dump(random_geojson, f)

# Output the random GeoJSON data
print(json.dumps(random_geojson, indent=2))

Output:

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "properties": {
        "name": "Random Feature 0"
      },
      "geometry": {
        "type": "Point",
        "coordinates": [
          -154.58003764916992,
          68.48986612802739
        ]
      }
    },
    {
      "type": "Feature",
      "properties": {
        "name": "Random Feature 1"
      },
      "geometry": {
        "type": "Point",
        "coordinates": [
          116.731836161805,
          57.05678638341732
        ]
      }
    },
    {
      "type": "Feature",
      "properties": {
        "name": "Random Feature 2"
      },
      "geometry": {
        "type": "Point",
        "coordinates": [
          64.25601917607622,
          -58.37415416692299
        ]
      }
    },
    {
      "type": "Feature",
      "properties": {
        "name": "Random Feature 3"
      },
      "geometry": {
        "type": "Point",
        "coordinates": [
          -133.51800779832104,
          -9.073051097633808
        ]
      }
    },

Explanation:

• Function generate_random_coordinates: This function generates random latitude and longitude values for the specified number of points.
• GeoJSON Structure: The GeoJSON data is structured as a FeatureCollection, with each Feature having a Point geometry and a name property.
• Saving to File: The generated GeoJSON data is saved to a file named random_geojson_data.geojson.

2. Using the GeoJSON Data in Altair

You can use this generated GeoJSON data in Altair to create a map. Here's how you can load and visualize it:

import altair as alt
import geopandas as gpd

# Load the random GeoJSON data
geo_df = gpd.read_file('random_geojson_data.geojson')

# Create a map using Altair
chart = alt.Chart(geo_df).mark_geoshape().encode(
    tooltip='name:N'
).project(
    type='mercator'
).properties(
    width=800,
    height=400
)

chart

Output:

This setup allows to visualize random geographic data points on a map using Altair, providing a practical example of how to work with GeoJSON data.

Customizing the Altair Map

You can customize the appearance of your map by modifying the properties of the mark_geoshape method. For example, you can change the fill color, stroke color, and stroke width.

chart = alt.Chart(geo_df).mark_geoshape(
    fill='lightgray',
    stroke='black',
    strokeWidth=0.5
).encode(
    tooltip='properties.name:N'
).project(
    type='mercator'
).properties(
    width=800,
    height=400
)
chart

Output:

Adding Data Layers

Altair allows you to layer multiple datasets on a single map. For instance, you can overlay point data on top of a geographic boundary map. This is useful for visualizing additional data such as population density or weather patterns. To demonstrate how to layer multiple datasets on a single map using Altair, we will overlay random point data on top of a geographic boundary map. 

import altair as alt
import geopandas as gpd
import pandas as pd
import json
import random

# Load the random GeoJSON data
with open('random_geojson_data.geojson', 'r') as f:
    random_geojson = json.load(f)

# Create a GeoDataFrame from the GeoJSON data
geo_df = gpd.GeoDataFrame.from_features(random_geojson['features'])

# Generate random population density data
random_points = {
    'name': [f'Feature {i}' for i in range(10)],
    'population_density': [random.randint(1, 100) for _ in range(10)],  # Random values between 1 and 100
    'longitude': [feature['geometry']['coordinates'][0] for feature in random_geojson['features']],
    'latitude': [feature['geometry']['coordinates'][1] for feature in random_geojson['features']]
}

population_df = pd.DataFrame(random_points)

# Create the base map using GeoJSON data
base_map = alt.Chart(geo_df).mark_geoshape(
    fill='lightgray',
    stroke='black',
    strokeWidth=0.5
).encode(
    tooltip='name:N'
).project(
    type='mercator'
).properties(
    width=800,
    height=400
)

# Create a layer of points with population density data
points_layer = alt.Chart(population_df).mark_circle(size=100).encode(
    longitude='longitude:Q',
    latitude='latitude:Q',
    color='population_density:Q',
    tooltip=['name:N', 'population_density:Q']
)

# Combine the base map and points layer
layered_map = base_map + points_layer

layered_map

Output:

Explanation:

• Base Map: The base map is created using the mark_geoshape method, which renders the geographic boundaries.
• Points Layer: The points are added using the mark_circle method, with the color encoding representing the population density.
• Layering: The base map and points layer are combined using the + operator, allowing for a layered visualization.

Conclusion

Creating maps with GeoJSON data in Altair is a straightforward process that allows for a high degree of customization and interactivity. By leveraging Altair's declarative syntax, you can easily visualize complex geographic data and gain insights from it. Whether you're working with simple geographic boundaries or more complex spatial datasets, Altair provides the tools needed to create compelling visualizations.