Mapbox density heatmap with Plotly in Python
Data visualization is one of the highlights of data analysis as it involves a graphical representation of data. We can enhance our ability to interpret data findings greatly.
Mapbox density heatmaps are interesting plots in Plotly that combine density and heatmaps. In this Answer, we will learn these and visualize the world map with density heatmaps, so don't miss out!
Plotly
Plotly is a data visualization library that allows us to create rich and dynamic visualizations in Python, R, and JavaScript. Some interesting features are:
Zooming
Tooltips
Rotating
Downloading plots
Let's break down the keywords before continuing with Mapbox density heatmaps.
Density
In data visualization, density indicates how closely data points are packed together in a specific region.
Heatmaps
Heatmaps are visual representations that use colors to display the intensity of data points.
Mapbox
Mapbox is a platform for creating different interactive maps and allows us to integrate location-based data and design customized maps accordingly.
Mapbox density heatmaps
Very simply put, we can use Mapbox's capabilities to visualize the concentration of data points over areas through color intensity.
Density heatmaps provide a great way to represent the distribution of data points across a geographic area. By using color gradients to signify density levels, we can easily identify regions with higher concentrations of data.
Code walkthrough
Importing and preprocessing the data
Our first step is to get the data we need for plotting on the map. For our answer, we'll plot the earthquake data and use pandas to preprocess the dataset for us efficiently.
import pandas as pdquakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
Creating the heatmap
To make our density heatmap, we will utilize Plotly's Densitymapbox trace type. This trace type allows us to specify latitude, longitude, and density values to easily generate an interactive density heatmap in a few lines!
import plotly.graph_objects as gofig = go.Figure(go.Densitymapbox(lat = quakes.Latitude, lon = quakes.Longitude, z = quakes.Magnitude,radius = 10))
Customizing our map visualization
We can customize the visualization by adjusting the map style, centering the map, and refining the map layout.
fig.update_layout(mapbox_style = "stamen-terrain", mapbox_center_lon = 180)fig.update_layout( margin = { "r": 0, "t": 0, "l": 0, "b": 0} )
Displaying the final plot
Finally, we can display the Mapbox density heatmap by calling the show() method on our Plotly figure.
fig.show()
Executable code
The following code is completely executable and renders the interactive Mapbox plot for us and even saves it in an HTML file called output.html. You can experiment with it and click "Run" once done.
import pandas as pd
quakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
import plotly.graph_objects as go
fig = go.Figure(go.Densitymapbox(lat = quakes.Latitude, lon = quakes.Longitude, z = quakes.Magnitude,
radius = 10))
fig.update_layout(mapbox_style = "stamen-terrain", mapbox_center_lon = 180)
fig.update_layout( margin = { "r": 0, "t": 0, "l": 0, "b": 0} )
fig.show()
fig.write_html("output.html", auto_open=True)Demonstration
This is how the plot shows the varying density of earthquakes occurring at different places on the map.
We can zoom in at any point within the map.
Let's witness the interactive aspect of the plot as well.
Use cases of Mapbox density heatmaps
Mapbox density heatmaps have quite a few real-life applications in various domains where the density of a variable, such as population.
Use cases | Explanations |
Urban planning | Analyzing population density for city planning |
Business intelligence | Optimizing store locations for brands |
Disaster response | Areas affected during different emergencies |
End notes
Congratulations, we've succeeded in creating an interactive visualization to reveal geographical data patterns in a highly clear and zoomable map plot! We have understood data distribution across space by combining Plotly's features and Mapbox's rich mapping capabilities.
Test your Mapbox density heatmap knowledge!
What Plotly function do we call in order to create a Mapbox density heatmap figure?
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