Visualizing Geospatial Data: Creating Heatmaps with Plotly and Folium in Python

Introduction

Heatmaps or Density Maps are popular tools for displaying the distribution of a variable over a geographic area. They help in identifying trends, detecting outliers, and presenting data in a visually appealing way. Python provides several mapping libraries for creating heatmaps, including Plotly Express and Folium.

Folium is a feature-rich library that simplifies the process of visualizing geospatial data. It is built on top of Leaflet.js, a popular JavaScript mapping library, and is designed to work on any platform. Folium provides a variousools and functionality for creating interactive and dynamic maps.

Plotly, on the other hand, is a widely-used library for generating interactive data visualizations with minimal coding. Plotly can be used to create interactive maps that incorporate MapBox functionality. Plotly Express is a simple and user-friendly wrapper library for Plotly, designed to make it easier to create a wide range of interactive visualizations, including heatmaps.

Importing libraries and reading the dataset

We will import the pandas library to load our data from a CSV file. The dataset we will be working with is https://www.kaggle.com/competitions/covid19-global-forecasting-week-4 COVID19 Global Forecasting (Week 4) competition dataset, which will be predicting the cumulative number of confirmed COVID-19 cases in various locations across the world, as well as the number of resulting fatalities, for future dates.

Heatmap with Plotly Express

To get started on generating a heatmap using Plotly Express, we will first import the library into our notebook using the following code:

Next, we will create a new figure by calling the density_mapbox function from  Plotly Express https://plotly.com/graphing-libraries/. To generate the map, we need to specify several parameters:

df: the name of the data frame containing the data

lat: the name of the column containing latitude values

lon: the name of the column containing longitude values

z: the name of the column containing the data we want to display on the heatmap

center: the coordinates that the map will be centered on, which we can obtain by calculating the mean of the latitude and longitude columns

zoom: the initial zoom level of the map

mapbox_style: the background mapping layer

height: the height of the plot

We can create an informative and visually appealing heatmap by setting these parameters.

Examining the generated map, we can observe that there are higher Importance values in the middle part of the area, and various factors may influence this trend.

Heatmap with Folium

To start working with Folium, we must first import it. However, we must import the Heatmap plugin from folium Plugins if we wish to create heatmaps.

After importing Folium, we can create a base map by calling the folium.Map() method, and pass several arguments within the parentheses. For this example, we will use the following parameters:

location: the coordinates where the map will be centered

zoom_start: the initial zoom level of the map

control_scale: whether the scale controls are displayed on the map

If you want to learn more about the parameters available for the map function, refer to the help documentation at https://python-visualization.github.io/folium/.

The next step is to create the heatmap layer. To accomplish this, we must first convert the latitude, longitude, and value data to a list, which can then be passed into the Heatmap function.

To customize the heatmap’s appearance, we can set several parameters, such as the minimum and maximum opacity, the radius of the color gradient around each data point, and others.

Conclusion

Heatmaps are an effective way to visualize and analyze geographical data, particularly petrophysical and well-log properties. Python offers several mapping libraries to create heatmaps, including Folium and Plotly Express. These libraries are user-friendly and enable the mapping of large regions, allowing for identifying trends and variations in the data. The insights gained from analyzing these maps can help in decision-making processes related to resource exploration and extraction. Therefore, heatmaps are an essential tool for the geoscience community, and Folium and Plotly Express are valuable libraries for creating them.

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